system

Abstract

The system according to this embodiment aims to appropriately manage the driver's health condition and prevent accidents. [Solution] The system according to the embodiment comprises a monitoring unit, a management unit, and a warning unit. The monitoring unit monitors the driver's condition. The management unit manages the driver's health condition based on the condition monitored by the monitoring unit. The warning unit issues a warning based on the health condition managed by the management unit.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the health state of a driver who drives for a long time has not been sufficiently managed appropriately to prevent accidents.

[0005] The system according to the embodiment aims to appropriately manage the health state of a driver and prevent accidents.

Means for Solving the Problems

[0006] The system according to the embodiment includes a monitoring unit, a management unit, and a warning unit. The monitoring unit monitors the situation of the driver. The management unit manages the health state of the driver based on the situation monitored by the monitoring unit. The warning unit issues a warning based on the health state managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately manage the driver's health condition and prevent accidents. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The driver safety management system according to an embodiment of the present invention is a system for ensuring the safety of drivers involved in long-haul trucking and transportation. This driver safety management system utilizes an AI camera and an AI communication tool managed by a generation AI system to manage the driver's condition and health status and provide a service to deter actions that could lead to accidents. First, the AI ​​camera monitors the driver's condition in real time. For example, the AI ​​camera analyzes the driver's facial expressions and eye movements to detect signs of drowsy driving or inattentive driving. By analyzing the driver's facial expressions and eye movements, the AI ​​camera can determine whether the driver is tired. For example, if the driver's eyes close frequently or their eye movements are sluggish, the AI ​​camera will determine that the driver is tired. Next, the AI ​​communication tool manages the driver's health status. For example, the AI ​​communication tool monitors the driver's heart rate and body temperature and issues a warning if an abnormality is detected. The AI ​​communication tool monitors the driver's health status in real time and, if an abnormality is detected, sends a message to the driver urging them to take a break. For example, if a driver's heart rate or body temperature is abnormally high, the AI ​​communication tool sends a message to the driver urging them to take a break. Furthermore, the AI ​​camera and AI communication tool work together to comprehensively manage the driver's situation and health. For instance, if the AI ​​camera detects signs of drowsy driving, the AI ​​communication tool sends a message to the driver urging them to take a break. Also, if the AI ​​communication tool detects an abnormality in the driver's health, the AI ​​camera monitors the driver's condition in detail and issues a warning as needed. In this way, by utilizing the AI ​​camera and AI communication tool managed by the generated AI system, the driver's situation and health can be managed in real time, and actions that could lead to accidents can be deterred. This ensures the safety of drivers in long-haul trucks and the transportation industry, and prevents accidents. Thus, the driver safety management system can manage the driver's situation and health in real time and deter actions that could lead to accidents.

[0029] The driver safety management system according to this embodiment comprises a monitoring unit, a management unit, and a warning unit. The monitoring unit monitors the driver's condition. For example, the monitoring unit can analyze the driver's facial expressions and eye movements. For example, the monitoring unit can use an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The monitoring unit can also analyze the driver's eye movements and detect how often the eyes close or how sluggish the eye movements are. For example, the monitoring unit determines that the driver is tired if the eyes close frequently or if the eye movements are sluggish. The management unit manages the driver's health condition based on the conditions monitored by the monitoring unit. For example, the management unit can monitor the driver's heart rate and body temperature. For example, the management unit can use an AI communication tool to monitor the driver's heart rate and body temperature in real time and issue a warning if an abnormality is detected. The management unit also comprehensively manages the driver's health condition and can send a message to the driver encouraging them to take a break if an abnormality is detected. For example, the management unit sends a message to the driver urging them to take a break if their heart rate or body temperature is abnormally high. The warning unit issues warnings based on the health status managed by the management unit. The warning unit can, for example, send a message to the driver urging them to take a break. The warning unit can, for example, use an AI communication tool to send a message to the driver urging them to take a break. The warning unit can also issue a warning to the driver if an abnormality is detected in the driver's health status. For example, the warning unit will issue a warning to the driver if their heart rate or body temperature is abnormally high. As a result, the driver safety management system according to this embodiment can manage the driver's situation and health status in real time and deter actions that could lead to accidents.

[0030] The monitoring unit monitors the driver's condition. For example, the monitoring unit can analyze the driver's facial expressions and eye movements. Specifically, the monitoring unit uses an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The AI ​​camera acquires high-resolution video and analyzes the driver's expressions using facial recognition technology. For example, it analyzes the frequency of the driver's eyes closing, blinking patterns, and mouth movements to detect signs of drowsy driving. The monitoring unit can also analyze the driver's eye movements to detect how often the eyes close or if the eye movements are sluggish. Specifically, it uses an AI algorithm to analyze the opening and closing of the eyes and the movement of the gaze, and if the eyes close frequently or the eye movements are sluggish, it determines that the driver is tired. Furthermore, the monitoring unit can also monitor the driver's head tilt and changes in posture to detect abnormal movements. For example, if the driver makes a movement as if leaning forward, it is determined that there is a high possibility of drowsy driving. In this way, the monitoring unit can monitor the driver's condition from multiple angles and detect abnormalities in real time.

[0031] The management department manages the driver's health status based on the situation monitored by the monitoring department. For example, the management department can monitor the driver's heart rate and body temperature. Specifically, the management department uses AI communication tools to monitor the driver's heart rate and body temperature in real time and issues a warning if an abnormality is detected. Heart rate and body temperature data are obtained from wearable devices worn by the driver. For example, smartwatches or belts with heart rate sensors are used. These devices continuously measure the driver's heart rate and body temperature and transmit the data to the management department. The management department analyzes this data and, if an abnormality is detected, can send a message to the driver prompting them to take a break. For example, if the heart rate or body temperature is abnormally high, a message prompting the driver to take a break will be sent. The management department also comprehensively manages the driver's health status and can instruct the driver to take appropriate action if an abnormality is detected. For example, if the driver has been driving for a long time, a message prompting them to take regular breaks will be sent. This allows the management department to manage the driver's health status in real time and reduce the risk of accidents.

[0032] The warning unit issues warnings based on the driver's health status, which is managed by the control unit. For example, the warning unit can send messages to the driver urging them to take a break. Specifically, the warning unit uses an AI communication tool to send messages to the driver urging them to take a break. For example, if the driver's heart rate or body temperature is abnormally high, the warning unit will warn the driver to take a break. The warning is sent as a message displayed on the driver's smartphone or in-car display. The warning unit can also use voice and vibration alerts to draw the driver's attention. For example, if the driver shows signs of drowsy driving, the warning unit will issue a voice alert to draw the driver's attention. Furthermore, if the warning unit detects an abnormality in the driver's health status, it can send a notification to emergency contacts. For example, if the driver loses consciousness, the warning unit will send a notification to emergency contacts to encourage a quick response. This allows the warning unit to issue appropriate warnings based on the driver's health status, thereby reducing the risk of accidents.

[0033] The monitoring unit includes an analysis unit that analyzes the driver's facial expressions and eye movements. The analysis unit can, for example, use an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The analysis unit can also, for example, analyze the driver's eye movements and detect how often the eyes close or how sluggish the eye movements are. For example, if the eyes close frequently or the eye movements are sluggish, the analysis unit will determine that the driver is tired. The analysis unit can, for example, use an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The analysis unit can also, for example, analyze the driver's eye movements and detect how often the eyes close or how sluggish the eye movements are. For example, if the eyes close frequently or the eye movements are sluggish, the analysis unit will determine that the driver is tired. In this way, by analyzing the driver's facial expressions and eye movements, signs of drowsy driving or inattentive driving can be detected. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data on the driver's facial expressions and eye movements into a generating AI and have the generating AI perform the detection of signs of drowsy driving or inattentive driving.

[0034] The management unit includes a monitoring unit that monitors the driver's heart rate and body temperature. The monitoring unit can, for example, use an AI communication tool to monitor the driver's heart rate and body temperature in real time and issue a warning if an abnormality is detected. The monitoring unit can, for example, monitor the driver's heart rate and body temperature and issue a warning if an abnormality is detected. The monitoring unit can, for example, use an AI communication tool to monitor the driver's heart rate and body temperature in real time and issue a warning if an abnormality is detected. The monitoring unit can, for example, monitor the driver's heart rate and body temperature and issue a warning if an abnormality is detected. This allows for real-time management of the driver's health status by monitoring their heart rate and body temperature. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input data on the driver's heart rate and body temperature into a generative AI and have the generative AI perform abnormality detection.

[0035] The warning unit includes a message unit that sends a message to the driver encouraging them to take a break. The message unit can send a message to the driver encouraging them to take a break, for example, using an AI communication tool. The message unit can send a message to the driver encouraging them to take a break, for example. The message unit can send a message to the driver encouraging them to take a break, for example, using an AI communication tool. The message unit can send a message to the driver encouraging them to take a break, for example. This reduces the risk of accidents by sending a message to the driver encouraging them to take a break. Some or all of the above processing in the message unit may be performed, for example, using a generative AI, or without using a generative AI. For example, the message unit can input data on the driver's health status into a generative AI and have the generative AI generate a message encouraging them to take a break.

[0036] The analysis unit can analyze the frequency of eye closures and the sluggishness of eye movements to determine whether the driver is tired. The analysis unit can, for example, analyze the frequency of eye closures to determine whether the driver is tired. The analysis unit can, for example, analyze the sluggishness of eye movements to determine whether the driver is tired. The analysis unit can, for example, analyze the frequency of eye closures to determine whether the driver is tired. The analysis unit can, for example, analyze the sluggishness of eye movements to determine whether the driver is tired. By determining whether the driver is tired, it is possible to prompt them to take a break at an appropriate time. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data on the driver's eye movements into a generating AI and have the generating AI perform a fatigue determination.

[0037] The monitoring unit can detect abnormalities in heart rate and body temperature, and issue a warning if an abnormality is detected. For example, the monitoring unit can detect an abnormal heart rate and issue a warning if an abnormality is detected. For example, the monitoring unit can detect an abnormal body temperature and issue a warning if an abnormality is detected. For example, the monitoring unit can detect an abnormal heart rate and issue a warning if an abnormality is detected. For example, the monitoring unit can detect an abnormal body temperature and issue a warning if an abnormality is detected. This allows for appropriate management of the driver's health status by detecting abnormalities in heart rate and body temperature. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the monitoring unit can input the driver's heart rate and body temperature data into a generation AI and have the generation AI perform abnormality detection.

[0038] The monitoring unit can analyze the driver's past driving history and select the optimal monitoring method. For example, if the driver has a history of accidents, the monitoring unit can increase the monitoring frequency. For example, if the driver has a history of driving for long periods of time, the monitoring unit can maintain a moderate monitoring frequency. For example, if the driver has a history of safe driving, the monitoring unit can decrease the monitoring frequency. In this way, by analyzing the driver's past driving history, the monitoring unit can provide an individually optimized monitoring method. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's past driving history data into a generative AI and have the generative AI select the optimal monitoring method.

[0039] The monitoring unit can change its monitoring focus based on the driver's current driving environment during monitoring. For example, in rainy weather, the monitoring unit can increase its monitoring focus to account for poor visibility. For example, in traffic congestion, the monitoring unit can increase its monitoring focus to account for stressful situations. For example, during nighttime driving, the monitoring unit can increase its monitoring focus to account for accumulated fatigue. By changing the monitoring focus according to the driving environment, more effective monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's current driving environment data into a generative AI and have the generative AI adjust the monitoring focus.

[0040] The monitoring unit can prioritize monitoring highly relevant monitoring items by considering the driver's geographical location information during monitoring. For example, on a highway, the monitoring unit can prioritize monitoring fatigue due to prolonged driving. For example, in an urban area, the monitoring unit can prioritize monitoring stress due to traffic congestion. For example, in a mountainous area, the monitoring unit can prioritize monitoring decreased attention due to changes in road conditions. This allows for more effective monitoring by prioritizing monitoring items based on geographical location information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's geographical location information into a generative AI and have the generative AI select highly relevant monitoring items.

[0041] The monitoring unit can analyze the driver's social media activity during monitoring and reflect the relevant information in the monitoring. For example, if the driver posts on social media indicating stress, the monitoring unit can increase the monitoring frequency. For example, if the driver posts on social media indicating relaxation, the monitoring unit can decrease the monitoring frequency. For example, if the driver posts on social media indicating fatigue, the monitoring unit can maintain a moderate monitoring frequency. This allows for monitoring tailored to the driver's situation by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's social media activity data into a generative AI and have the generative AI adjust the monitoring frequency.

[0042] The management unit can select the optimal management method by referring to the driver's past health data when managing their health status. For example, if the driver has a history of hypertension, the management unit can enhance blood pressure monitoring. For example, if the driver has a history of abnormal heart rate, the management unit can enhance heart rate monitoring. For example, if the driver has a history of abnormal body temperature, the management unit can enhance body temperature monitoring. This allows the management unit to provide individually optimized health management methods by referring to past health data. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input the driver's past health data into a generative AI and have the generative AI select the optimal management method.

[0043] The management department can customize the means of health management based on the driver's current lifestyle when monitoring their health. For example, if the driver is driving for long periods, the management department can encourage them to take frequent breaks. For example, if the driver is driving at night, the management department can monitor the quality of their sleep. For example, if the driver is driving in a high-stress environment, the management department can suggest ways to relax. By customizing the means of management based on the driver's current lifestyle, more effective health management becomes possible. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input data on the driver's current lifestyle into a generative AI and have the generative AI perform the customization of the means of management.

[0044] The management department can select the optimal management method when managing the driver's health status, taking into account the driver's geographical location information. For example, on highways, the management department can encourage breaks considering fatigue from long hours of driving. For example, in urban areas, the management department can suggest relaxation methods considering stress caused by traffic congestion. For example, in mountainous areas, the management department can monitor the driver's health status considering decreased attention due to changes in road conditions. This enables more effective health management by selecting the optimal management method based on geographical location information. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or without a generative AI. For example, the management department can input the driver's geographical location information into a generative AI and have the generative AI select the optimal management method.

[0045] The management department can analyze drivers' social media activity when managing their health status and incorporate relevant information into management. For example, if a driver posts on social media indicating they are stressed, the management department can suggest ways to relax. For example, if a driver posts on social media indicating they are relaxed, the management department can continue with the usual health management methods. For example, if a driver posts on social media indicating they are tired, the management department can encourage them to take a break and monitor their health status. This allows for health management tailored to the driver's situation by analyzing social media activity. Some or all of the above processing in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input driver social media activity data into generative AI and have the generative AI adjust health management methods.

[0046] The warning unit can select the optimal warning method by referring to the driver's past response history when issuing a warning. For example, the warning unit can prioritize warning methods to which the driver has responded quickly in the past. For example, the warning unit can avoid warning methods that the driver has ignored in the past. For example, the warning unit can continue using warning methods that have been effective for the driver in the past. In this way, by referring to past response history, the optimal warning method can be provided on an individual basis. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the driver's past response history data into a generating AI and have the generating AI select the optimal warning method.

[0047] The warning unit can adjust the timing of warnings based on the driver's current driving conditions. For example, the warning unit can issue warnings earlier on highways. For example, the warning unit can issue warnings in accordance with traffic signals in urban areas. For example, the warning unit can issue warnings according to road conditions in mountainous areas. By adjusting the timing of warnings according to driving conditions, more effective warnings can be provided. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the driver's current driving condition data into a generating AI and have the generating AI adjust the timing of warnings.

[0048] The warning unit can select the optimal warning method when issuing a warning, taking into account the driver's geographical location information. For example, the warning unit can issue a warning earlier on a highway. For example, the warning unit can issue a warning in accordance with traffic signals in urban areas. For example, the warning unit can issue a warning according to road conditions in mountainous areas. By selecting the optimal warning method based on geographical location information, more effective warnings become possible. Some or all of the above processing in the warning unit may be performed using, for example, a generation AI, or without a generation AI. For example, the warning unit can input the driver's geographical location information into a generation AI and have the generation AI select the optimal warning method.

[0049] The warning unit can analyze the driver's social media activity when issuing a warning and reflect the relevant information in the warning. For example, if the driver is posting stressful content on social media, the warning unit can issue a warning in a calm voice. For example, if the driver is posting relaxing content on social media, the warning unit can issue a warning in a cheerful voice. For example, if the driver is posting tired content on social media, the warning unit can issue a quick and concise warning. This allows for warnings tailored to the driver's situation by analyzing social media activity. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the warning unit can input the driver's social media activity data into a generative AI and have the generative AI adjust the way the warning is expressed.

[0050] The analysis unit can select the optimal analysis method by referring to the driver's past facial expression data during the analysis. For example, the analysis unit can perform a fatigue analysis based on facial expression data showing that the driver was tired in the past. For example, the analysis unit can perform a stress analysis based on facial expression data showing that the driver was stressed in the past. For example, the analysis unit can perform a relaxation level analysis based on facial expression data showing that the driver was relaxed in the past. In this way, by referring to past facial expression data, the optimal analysis method can be provided on an individual basis. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the driver's past facial expression data into a generating AI and have the generating AI select the optimal analysis method.

[0051] The analysis unit can change the focus of its analysis during the analysis, taking into account the driver's current driving conditions. For example, on a highway, the analysis unit can prioritize the analysis of fatigue due to long-distance driving. For example, in an urban area, the analysis unit can prioritize the analysis of stress due to traffic congestion. For example, in a mountainous area, the analysis unit can prioritize the analysis of decreased attention due to changes in road conditions. By changing the focus of the analysis according to the driving conditions, a more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the driver's current driving condition data into the generating AI and have the generating AI adjust the focus of the analysis.

[0052] The analysis unit can select the optimal analysis method during analysis, taking into account the driver's geographical location information. For example, on a highway, the analysis unit can prioritize the analysis of fatigue due to long-distance driving. For example, in an urban area, the analysis unit can prioritize the analysis of stress due to traffic congestion. For example, in a mountainous area, the analysis unit can prioritize the analysis of decreased attention due to changes in road conditions. By selecting the optimal analysis method based on geographical location information, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the driver's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0053] The monitoring unit can select the optimal monitoring method by referring to the driver's past health data during monitoring. For example, if the driver has a history of hypertension, the monitoring unit can enhance blood pressure monitoring. For example, if the driver has a history of abnormal heart rate, the monitoring unit can enhance heart rate monitoring. For example, if the driver has a history of abnormal body temperature, the monitoring unit can enhance body temperature monitoring. In this way, by referring to past health data, the monitoring unit can provide an individually optimized monitoring method. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input the driver's past health data into a generative AI and have the generative AI select the optimal monitoring method.

[0054] The monitoring unit can customize the monitoring methods based on the driver's current living situation during monitoring. For example, if the driver is driving for a long time, the monitoring unit can prompt the driver to take frequent breaks. For example, if the driver is driving at night, the monitoring unit can monitor the quality of sleep. For example, if the driver is driving in a high-stress environment, the monitoring unit can suggest ways to relax. By customizing the monitoring methods based on the current living situation, more effective monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's current living situation data into a generative AI and have the generative AI perform the customization of the monitoring methods.

[0055] The monitoring unit can select the optimal monitoring method by considering the driver's geographical location information during monitoring. For example, on a highway, the monitoring unit can encourage rest due to fatigue from long hours of driving. For example, in an urban area, the monitoring unit can suggest relaxation methods considering stress caused by traffic congestion. For example, in a mountainous area, the monitoring unit can monitor the driver's health condition by considering decreased attention due to changes in road conditions. By selecting the optimal monitoring method based on geographical location information, more effective monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input the driver's geographical location information into a generative AI and have the generative AI select the optimal monitoring method.

[0056] The monitoring unit can analyze the driver's social media activity during monitoring and reflect the relevant information in the monitoring. For example, if the monitoring unit is posting stressful content on social media, it can suggest ways to relax. For example, if the monitoring unit is posting relaxing content on social media, it can continue with the normal monitoring method. For example, if the monitoring unit is posting tired content on social media, it can encourage the driver to take a break and monitor their health. This allows for monitoring tailored to the driver's situation by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's social media activity data into a generative AI and have the generative AI customize the monitoring methods.

[0057] The messaging unit can select the optimal message by referring to the driver's past response history when sending a message. For example, the messaging unit can prioritize messaging methods to which the driver has responded quickly in the past. For example, the messaging unit can avoid messaging methods that the driver has ignored in the past. For example, the messaging unit can continue using messaging methods that have been effective for the driver in the past. This allows for the provision of individually optimized messages by referring to past response history. Some or all of the above processing in the messaging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the messaging unit can input the driver's past response history data into a generation AI and have the generation AI select the optimal messaging method.

[0058] The messaging unit can adjust the timing of messages based on the driver's current driving conditions when sending a message. For example, the messaging unit can send messages earlier on highways. For example, the messaging unit can send messages in accordance with traffic signals in urban areas. For example, the messaging unit can send messages according to road conditions in mountainous areas. By adjusting the timing of messages according to driving conditions, more effective messaging becomes possible. Some or all of the above processing in the messaging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the messaging unit can input the driver's current driving condition data into the generation AI and have the generation AI adjust the timing of messages.

[0059] The messaging unit can select the optimal message when sending a message, taking into account the driver's geographical location information. For example, the messaging unit can send messages earlier on highways. For example, the messaging unit can send messages in accordance with traffic signals in urban areas. For example, the messaging unit can send messages according to road conditions in mountainous areas. This makes it possible to send more effective messages by selecting the optimal message based on geographical location information. Some or all of the above processing in the messaging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the messaging unit can input the driver's geographical location information into a generation AI and have the generation AI select the optimal message.

[0060] The messaging unit can analyze the driver's social media activity when sending a message and reflect relevant information in the message. For example, if the driver has posted stressful content on social media, the messaging unit can send a message in a calm voice. For example, if the driver has posted relaxed content on social media, the messaging unit can send a message in a cheerful voice. For example, if the driver has posted tired content on social media, the messaging unit can send a quick and concise message. This allows for messages tailored to the driver's situation by analyzing social media activity. Some or all of the processing described above in the messaging unit may be performed using, for example, a generative AI, or not. For example, the messaging unit can input the driver's social media activity data into a generative AI and have the generative AI adjust the way the message is expressed.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The driver safety management system can also include a style analysis unit that analyzes the driver's driving style. For example, the style analysis unit can analyze the frequency of acceleration and braking, and steering patterns, and evaluate the driving style. For instance, if there are frequent sudden accelerations or sudden brakes, the style analysis unit can determine that the driver is driving aggressively and issue a warning. Similarly, if the steering is unstable, the style analysis unit can determine that the driver may be fatigued and send a message encouraging them to take a break. This allows for more personalized safety management by analyzing the driver's driving style.

[0063] The driver safety management system may also include a history recording unit that records the driver's driving history. This unit can record, for example, the driver's driving time, distance traveled, and rest periods, allowing for analysis of long-term driving patterns. For instance, if the driver has been driving for extended periods, the unit can send a message prompting them to take a break. It can also provide warnings for specific times of day or routes based on past driving history. This enables more effective safety management by utilizing the driver's driving history.

[0064] The driver safety management system can also include a predictive unit that forecasts the driver's health condition. This unit can predict future health risks based, for example, on the driver's past health data and driving history. For instance, if the driver has a history of high blood pressure, the unit can prompt the driver to undergo regular health checkups. Furthermore, if the driver has been driving for extended periods, the unit can predict fatigue accumulation and send a message encouraging them to take a break. This allows for the prediction of the driver's health condition and proactive measures to be taken.

[0065] The driver safety management system may also include a posture monitoring unit that monitors the driver's posture while driving. For example, the posture monitoring unit can monitor the driver's sitting position and spinal curvature, and provide guidance to maintain correct posture. If the driver is driving in the same position for a long period, the posture monitoring unit can send a message prompting the driver to stretch. Furthermore, if the driver's posture is poor, the posture monitoring unit can instruct the driver to correct their posture. This allows for monitoring the driver's posture and supporting healthy driving.

[0066] The driver safety management system may also include an eye-tracking unit that tracks the driver's gaze while driving. The eye-tracking unit can, for example, issue a warning if the driver's gaze deviates from the road. For instance, if the driver is operating a smartphone or distracted by the surrounding scenery, the eye-tracking unit can send a message to alert the driver. Furthermore, if the driver's gaze remains within a certain range, the eye-tracking unit can determine that the driver is concentrating on driving. This allows the system to monitor the driver's gaze and support safe driving.

[0067] The driver safety management system can also include a food and beverage management unit to manage the driver's eating and drinking habits while driving. For example, the food and beverage management unit can send messages encouraging hydration if the driver is driving for extended periods. It can also send messages encouraging energy replenishment if the driver is fatigued. Furthermore, it can suggest appropriate rest stops if the driver is approaching mealtime. This allows for the management of the driver's eating and drinking habits, supporting healthy driving.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The monitoring unit monitors the driver's condition. The monitoring unit can, for example, analyze the driver's facial expressions and eye movements. The monitoring unit uses an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The monitoring unit can also analyze the driver's eye movements and detect how often the eyes close or how sluggish the eye movements are. For example, if the eyes close frequently or the eye movements are sluggish, it will be determined that the driver is tired. Step 2: The management unit manages the driver's health status based on the conditions monitored by the monitoring unit. The management unit can, for example, monitor the driver's heart rate and body temperature. The management unit uses an AI communication tool to monitor the driver's heart rate and body temperature in real time and issues a warning if an abnormality is detected. The management unit also comprehensively manages the driver's health status and can send a message to the driver encouraging them to take a break if an abnormality is detected. For example, if the heart rate or body temperature is abnormally high, a message encouraging the driver to take a break will be sent. Step 3: The warning unit issues warnings based on the health status managed by the control unit. For example, the warning unit can send a message to the driver encouraging them to take a break. The warning unit uses an AI communication tool to send messages to the driver encouraging them to take a break. The warning unit can also issue a warning to the driver if an abnormality is detected in the driver's health status. For example, it will issue a warning to the driver if their heart rate or body temperature is abnormally high.

[0070] (Example of form 2) The driver safety management system according to an embodiment of the present invention is a system for ensuring the safety of drivers involved in long-haul trucking and transportation. This driver safety management system utilizes an AI camera and an AI communication tool managed by a generation AI system to manage the driver's condition and health status and provide a service to deter actions that could lead to accidents. First, the AI ​​camera monitors the driver's condition in real time. For example, the AI ​​camera analyzes the driver's facial expressions and eye movements to detect signs of drowsy driving or inattentive driving. By analyzing the driver's facial expressions and eye movements, the AI ​​camera can determine whether the driver is tired. For example, if the driver's eyes close frequently or their eye movements are sluggish, the AI ​​camera will determine that the driver is tired. Next, the AI ​​communication tool manages the driver's health status. For example, the AI ​​communication tool monitors the driver's heart rate and body temperature and issues a warning if an abnormality is detected. The AI ​​communication tool monitors the driver's health status in real time and, if an abnormality is detected, sends a message to the driver urging them to take a break. For example, if a driver's heart rate or body temperature is abnormally high, the AI ​​communication tool sends a message to the driver urging them to take a break. Furthermore, the AI ​​camera and AI communication tool work together to comprehensively manage the driver's situation and health. For instance, if the AI ​​camera detects signs of drowsy driving, the AI ​​communication tool sends a message to the driver urging them to take a break. Also, if the AI ​​communication tool detects an abnormality in the driver's health, the AI ​​camera monitors the driver's condition in detail and issues a warning as needed. In this way, by utilizing the AI ​​camera and AI communication tool managed by the generated AI system, the driver's situation and health can be managed in real time, and actions that could lead to accidents can be deterred. This ensures the safety of drivers in long-haul trucks and the transportation industry, and prevents accidents. Thus, the driver safety management system can manage the driver's situation and health in real time and deter actions that could lead to accidents.

[0071] The driver safety management system according to this embodiment comprises a monitoring unit, a management unit, and a warning unit. The monitoring unit monitors the driver's condition. For example, the monitoring unit can analyze the driver's facial expressions and eye movements. For example, the monitoring unit can use an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The monitoring unit can also analyze the driver's eye movements and detect how often the eyes close or how sluggish the eye movements are. For example, the monitoring unit determines that the driver is tired if the eyes close frequently or if the eye movements are sluggish. The management unit manages the driver's health condition based on the conditions monitored by the monitoring unit. For example, the management unit can monitor the driver's heart rate and body temperature. For example, the management unit can use an AI communication tool to monitor the driver's heart rate and body temperature in real time and issue a warning if an abnormality is detected. The management unit also comprehensively manages the driver's health condition and can send a message to the driver encouraging them to take a break if an abnormality is detected. For example, the management unit sends a message to the driver urging them to take a break if their heart rate or body temperature is abnormally high. The warning unit issues warnings based on the health status managed by the management unit. The warning unit can, for example, send a message to the driver urging them to take a break. The warning unit can, for example, use an AI communication tool to send a message to the driver urging them to take a break. The warning unit can also issue a warning to the driver if an abnormality is detected in the driver's health status. For example, the warning unit will issue a warning to the driver if their heart rate or body temperature is abnormally high. As a result, the driver safety management system according to this embodiment can manage the driver's situation and health status in real time and deter actions that could lead to accidents.

[0072] The monitoring unit monitors the driver's condition. For example, the monitoring unit can analyze the driver's facial expressions and eye movements. Specifically, the monitoring unit uses an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The AI ​​camera acquires high-resolution video and analyzes the driver's expressions using facial recognition technology. For example, it analyzes the frequency of the driver's eyes closing, blinking patterns, and mouth movements to detect signs of drowsy driving. The monitoring unit can also analyze the driver's eye movements to detect how often the eyes close or if the eye movements are sluggish. Specifically, it uses an AI algorithm to analyze the opening and closing of the eyes and the movement of the gaze, and if the eyes close frequently or the eye movements are sluggish, it determines that the driver is tired. Furthermore, the monitoring unit can also monitor the driver's head tilt and changes in posture to detect abnormal movements. For example, if the driver makes a movement as if leaning forward, it is determined that there is a high possibility of drowsy driving. In this way, the monitoring unit can monitor the driver's condition from multiple angles and detect abnormalities in real time.

[0073] The management department manages the driver's health status based on the situation monitored by the monitoring department. For example, the management department can monitor the driver's heart rate and body temperature. Specifically, the management department uses AI communication tools to monitor the driver's heart rate and body temperature in real time and issues a warning if an abnormality is detected. Heart rate and body temperature data are obtained from wearable devices worn by the driver. For example, smartwatches or belts with heart rate sensors are used. These devices continuously measure the driver's heart rate and body temperature and transmit the data to the management department. The management department analyzes this data and, if an abnormality is detected, can send a message to the driver prompting them to take a break. For example, if the heart rate or body temperature is abnormally high, a message prompting the driver to take a break will be sent. The management department also comprehensively manages the driver's health status and can instruct the driver to take appropriate action if an abnormality is detected. For example, if the driver has been driving for a long time, a message prompting them to take regular breaks will be sent. This allows the management department to manage the driver's health status in real time and reduce the risk of accidents.

[0074] The warning unit issues warnings based on the driver's health status, which is managed by the control unit. For example, the warning unit can send messages to the driver urging them to take a break. Specifically, the warning unit uses an AI communication tool to send messages to the driver urging them to take a break. For example, if the driver's heart rate or body temperature is abnormally high, the warning unit will warn the driver to take a break. The warning is sent as a message displayed on the driver's smartphone or in-car display. The warning unit can also use voice and vibration alerts to draw the driver's attention. For example, if the driver shows signs of drowsy driving, the warning unit will issue a voice alert to draw the driver's attention. Furthermore, if the warning unit detects an abnormality in the driver's health status, it can send a notification to emergency contacts. For example, if the driver loses consciousness, the warning unit will send a notification to emergency contacts to encourage a quick response. This allows the warning unit to issue appropriate warnings based on the driver's health status, thereby reducing the risk of accidents.

[0075] The monitoring unit includes an analysis unit that analyzes the driver's facial expressions and eye movements. The analysis unit can, for example, use an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The analysis unit can also, for example, analyze the driver's eye movements and detect how often the eyes close or how sluggish the eye movements are. For example, if the eyes close frequently or the eye movements are sluggish, the analysis unit will determine that the driver is tired. The analysis unit can, for example, use an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The analysis unit can also, for example, analyze the driver's eye movements and detect how often the eyes close or how sluggish the eye movements are. For example, if the eyes close frequently or the eye movements are sluggish, the analysis unit will determine that the driver is tired. In this way, by analyzing the driver's facial expressions and eye movements, signs of drowsy driving or inattentive driving can be detected. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data on the driver's facial expressions and eye movements into a generating AI and have the generating AI perform the detection of signs of drowsy driving or inattentive driving.

[0076] The management unit includes a monitoring unit that monitors the driver's heart rate and body temperature. The monitoring unit can, for example, use an AI communication tool to monitor the driver's heart rate and body temperature in real time and issue a warning if an abnormality is detected. The monitoring unit can, for example, monitor the driver's heart rate and body temperature and issue a warning if an abnormality is detected. The monitoring unit can, for example, use an AI communication tool to monitor the driver's heart rate and body temperature in real time and issue a warning if an abnormality is detected. The monitoring unit can, for example, monitor the driver's heart rate and body temperature and issue a warning if an abnormality is detected. This allows for real-time management of the driver's health status by monitoring their heart rate and body temperature. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input data on the driver's heart rate and body temperature into a generative AI and have the generative AI perform abnormality detection.

[0077] The warning unit includes a message unit that sends a message to the driver encouraging them to take a break. The message unit can send a message to the driver encouraging them to take a break, for example, using an AI communication tool. The message unit can send a message to the driver encouraging them to take a break, for example. The message unit can send a message to the driver encouraging them to take a break, for example, using an AI communication tool. The message unit can send a message to the driver encouraging them to take a break, for example. This reduces the risk of accidents by sending a message to the driver encouraging them to take a break. Some or all of the above processing in the message unit may be performed, for example, using a generative AI, or without using a generative AI. For example, the message unit can input data on the driver's health status into a generative AI and have the generative AI generate a message encouraging them to take a break.

[0078] The analysis unit can analyze the frequency of eye closures and the sluggishness of eye movements to determine whether the driver is tired. The analysis unit can, for example, analyze the frequency of eye closures to determine whether the driver is tired. The analysis unit can, for example, analyze the sluggishness of eye movements to determine whether the driver is tired. The analysis unit can, for example, analyze the frequency of eye closures to determine whether the driver is tired. The analysis unit can, for example, analyze the sluggishness of eye movements to determine whether the driver is tired. By determining whether the driver is tired, it is possible to prompt them to take a break at an appropriate time. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data on the driver's eye movements into a generating AI and have the generating AI perform a fatigue determination.

[0079] The monitoring unit can detect abnormalities in heart rate and body temperature, and issue a warning if an abnormality is detected. For example, the monitoring unit can detect an abnormal heart rate and issue a warning if an abnormality is detected. For example, the monitoring unit can detect an abnormal body temperature and issue a warning if an abnormality is detected. For example, the monitoring unit can detect an abnormal heart rate and issue a warning if an abnormality is detected. For example, the monitoring unit can detect an abnormal body temperature and issue a warning if an abnormality is detected. This allows for appropriate management of the driver's health status by detecting abnormalities in heart rate and body temperature. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the monitoring unit can input the driver's heart rate and body temperature data into a generation AI and have the generation AI perform abnormality detection.

[0080] The monitoring unit can estimate the driver's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the driver is stressed, the monitoring unit can increase the monitoring frequency. For example, if the driver is relaxed, the monitoring unit can decrease the monitoring frequency. For example, if the driver is tired, the monitoring unit can maintain a moderate monitoring frequency. This allows for more effective monitoring by adjusting the monitoring frequency according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the monitoring unit can input driver emotion data into a generative AI and have the generative AI adjust the monitoring frequency.

[0081] The monitoring unit can analyze the driver's past driving history and select the optimal monitoring method. For example, if the driver has a history of accidents, the monitoring unit can increase the monitoring frequency. For example, if the driver has a history of driving for long periods of time, the monitoring unit can maintain a moderate monitoring frequency. For example, if the driver has a history of safe driving, the monitoring unit can decrease the monitoring frequency. In this way, by analyzing the driver's past driving history, the monitoring unit can provide an individually optimized monitoring method. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's past driving history data into a generative AI and have the generative AI select the optimal monitoring method.

[0082] The monitoring unit can change its monitoring focus based on the driver's current driving environment during monitoring. For example, in rainy weather, the monitoring unit can increase its monitoring focus to account for poor visibility. For example, in traffic congestion, the monitoring unit can increase its monitoring focus to account for stressful situations. For example, during nighttime driving, the monitoring unit can increase its monitoring focus to account for accumulated fatigue. By changing the monitoring focus according to the driving environment, more effective monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's current driving environment data into a generative AI and have the generative AI adjust the monitoring focus.

[0083] The monitoring unit can estimate the driver's emotions and determine the priority of items to monitor based on the estimated emotions. For example, if the driver is stressed, the monitoring unit can prioritize monitoring facial expressions and eye movements. For example, if the driver is relaxed, the monitoring unit can prioritize monitoring heart rate and body temperature. For example, if the driver is tired, the monitoring unit can prioritize monitoring eye movements and the frequency of eye closing. This allows for more effective monitoring by determining the priority of items to monitor according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not. For example, the monitoring unit can input the driver's emotion data into a generative AI and have the generative AI determine the priority of items to monitor.

[0084] The monitoring unit can prioritize monitoring highly relevant monitoring items by considering the driver's geographical location information during monitoring. For example, on a highway, the monitoring unit can prioritize monitoring fatigue due to prolonged driving. For example, in an urban area, the monitoring unit can prioritize monitoring stress due to traffic congestion. For example, in a mountainous area, the monitoring unit can prioritize monitoring decreased attention due to changes in road conditions. This allows for more effective monitoring by prioritizing monitoring items based on geographical location information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's geographical location information into a generative AI and have the generative AI select highly relevant monitoring items.

[0085] The monitoring unit can analyze the driver's social media activity during monitoring and reflect the relevant information in the monitoring. For example, if the driver posts on social media indicating stress, the monitoring unit can increase the monitoring frequency. For example, if the driver posts on social media indicating relaxation, the monitoring unit can decrease the monitoring frequency. For example, if the driver posts on social media indicating fatigue, the monitoring unit can maintain a moderate monitoring frequency. This allows for monitoring tailored to the driver's situation by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's social media activity data into a generative AI and have the generative AI adjust the monitoring frequency.

[0086] The management unit can estimate the driver's emotions and adjust the health management method based on the estimated emotions. For example, if the driver is stressed, the management unit can encourage a break to relax. For example, if the driver is relaxed, the management unit can continue with the normal health management method. For example, if the driver is tired, the management unit can encourage a break and monitor their health. This allows for more effective health management by adjusting the health management method according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using, for example, generative AI, or not using generative AI. For example, the management unit can input driver emotion data into a generative AI and have the generative AI adjust the health management method.

[0087] The management unit can select the optimal management method by referring to the driver's past health data when managing their health status. For example, if the driver has a history of hypertension, the management unit can enhance blood pressure monitoring. For example, if the driver has a history of abnormal heart rate, the management unit can enhance heart rate monitoring. For example, if the driver has a history of abnormal body temperature, the management unit can enhance body temperature monitoring. This allows the management unit to provide individually optimized health management methods by referring to past health data. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input the driver's past health data into a generative AI and have the generative AI select the optimal management method.

[0088] The management department can customize the means of health management based on the driver's current lifestyle when monitoring their health. For example, if the driver is driving for long periods, the management department can encourage them to take frequent breaks. For example, if the driver is driving at night, the management department can monitor the quality of their sleep. For example, if the driver is driving in a high-stress environment, the management department can suggest ways to relax. By customizing the means of management based on the driver's current lifestyle, more effective health management becomes possible. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input data on the driver's current lifestyle into a generative AI and have the generative AI perform the customization of the means of management.

[0089] The management unit can estimate the driver's emotions and determine the priority of health items to manage based on the estimated emotions. For example, if the driver is stressed, the management unit can prioritize heart rate monitoring. For example, if the driver is relaxed, the management unit can prioritize body temperature monitoring. For example, if the driver is tired, the management unit can prioritize sleep quality monitoring. This allows for more effective health management by prioritizing health items according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using, for example, generative AI, or not using generative AI. For example, the management unit can input driver emotion data into a generative AI and have the generative AI determine the priority of health items.

[0090] The management department can select the optimal management method when managing the driver's health status, taking into account the driver's geographical location information. For example, on highways, the management department can encourage breaks considering fatigue from long hours of driving. For example, in urban areas, the management department can suggest relaxation methods considering stress caused by traffic congestion. For example, in mountainous areas, the management department can monitor the driver's health status considering decreased attention due to changes in road conditions. This enables more effective health management by selecting the optimal management method based on geographical location information. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or without a generative AI. For example, the management department can input the driver's geographical location information into a generative AI and have the generative AI select the optimal management method.

[0091] The management department can analyze drivers' social media activity when managing their health status and incorporate relevant information into management. For example, if a driver posts on social media indicating they are stressed, the management department can suggest ways to relax. For example, if a driver posts on social media indicating they are relaxed, the management department can continue with the usual health management methods. For example, if a driver posts on social media indicating they are tired, the management department can encourage them to take a break and monitor their health status. This allows for health management tailored to the driver's situation by analyzing social media activity. Some or all of the above processing in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input driver social media activity data into generative AI and have the generative AI adjust health management methods.

[0092] The warning unit can estimate the driver's emotions and adjust the way the warning is delivered based on the estimated emotions. For example, if the driver is tense, the warning unit can issue a warning in a calm voice. For example, if the driver is relaxed, the warning unit can issue a warning in a cheerful voice. For example, if the driver is in a hurry, the warning unit can issue a quick and concise warning. By adjusting the way the warning is delivered according to the driver's emotions, more effective warnings can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using a generative AI, or not using a generative AI. For example, the warning unit can input driver emotion data into a generative AI and have the generative AI adjust the way the warning is delivered.

[0093] The warning unit can select the optimal warning method by referring to the driver's past response history when issuing a warning. For example, the warning unit can prioritize warning methods to which the driver has responded quickly in the past. For example, the warning unit can avoid warning methods that the driver has ignored in the past. For example, the warning unit can continue using warning methods that have been effective for the driver in the past. In this way, by referring to past response history, the optimal warning method can be provided on an individual basis. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the driver's past response history data into a generating AI and have the generating AI select the optimal warning method.

[0094] The warning unit can adjust the timing of warnings based on the driver's current driving conditions. For example, the warning unit can issue warnings earlier on highways. For example, the warning unit can issue warnings in accordance with traffic signals in urban areas. For example, the warning unit can issue warnings according to road conditions in mountainous areas. By adjusting the timing of warnings according to driving conditions, more effective warnings can be provided. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the driver's current driving condition data into a generating AI and have the generating AI adjust the timing of warnings.

[0095] The warning unit can estimate the driver's emotions and determine the priority of warnings based on the estimated emotions. For example, if the driver is tense, the warning unit can prioritize important warnings. For example, if the driver is relaxed, the warning unit can prioritize normal warnings. For example, if the driver is in a hurry, the warning unit can prioritize urgent warnings. This allows for more effective warnings by determining the priority of warnings according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using a generative AI, or not using a generative AI. For example, the warning unit can input driver emotion data into a generative AI and have the generative AI determine the priority of warnings.

[0096] The warning unit can select the optimal warning method when issuing a warning, taking into account the driver's geographical location information. For example, the warning unit can issue a warning earlier on a highway. For example, the warning unit can issue a warning in accordance with traffic signals in urban areas. For example, the warning unit can issue a warning according to road conditions in mountainous areas. By selecting the optimal warning method based on geographical location information, more effective warnings become possible. Some or all of the above processing in the warning unit may be performed using, for example, a generation AI, or without a generation AI. For example, the warning unit can input the driver's geographical location information into a generation AI and have the generation AI select the optimal warning method.

[0097] The warning unit can analyze the driver's social media activity when issuing a warning and reflect the relevant information in the warning. For example, if the driver is posting stressful content on social media, the warning unit can issue a warning in a calm voice. For example, if the driver is posting relaxing content on social media, the warning unit can issue a warning in a cheerful voice. For example, if the driver is posting tired content on social media, the warning unit can issue a quick and concise warning. This allows for warnings tailored to the driver's situation by analyzing social media activity. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the warning unit can input the driver's social media activity data into a generative AI and have the generative AI adjust the way the warning is expressed.

[0098] The analysis unit can estimate the driver's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the driver is tense, the analysis unit can perform a detailed analysis. For example, if the driver is relaxed, the analysis unit can perform a normal analysis. For example, if the driver is tired, the analysis unit can perform a simplified analysis. By adjusting the accuracy of the analysis according to the driver's emotions, a more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the driver's emotion data into a generative AI and have the generative AI adjust the accuracy of the analysis.

[0099] The analysis unit can select the optimal analysis method by referring to the driver's past facial expression data during the analysis. For example, the analysis unit can perform a fatigue analysis based on facial expression data showing that the driver was tired in the past. For example, the analysis unit can perform a stress analysis based on facial expression data showing that the driver was stressed in the past. For example, the analysis unit can perform a relaxation level analysis based on facial expression data showing that the driver was relaxed in the past. In this way, by referring to past facial expression data, the optimal analysis method can be provided on an individual basis. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the driver's past facial expression data into a generating AI and have the generating AI select the optimal analysis method.

[0100] The analysis unit can change the focus of its analysis during the analysis, taking into account the driver's current driving conditions. For example, on a highway, the analysis unit can prioritize the analysis of fatigue due to long-distance driving. For example, in an urban area, the analysis unit can prioritize the analysis of stress due to traffic congestion. For example, in a mountainous area, the analysis unit can prioritize the analysis of decreased attention due to changes in road conditions. By changing the focus of the analysis according to the driving conditions, a more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the driver's current driving condition data into the generating AI and have the generating AI adjust the focus of the analysis.

[0101] The analysis unit can estimate the driver's emotions and determine the priority of items to analyze based on the estimated emotions. For example, if the driver is tense, the analysis unit can prioritize stress analysis. For example, if the driver is relaxed, the analysis unit can prioritize fatigue analysis. For example, if the driver is tired, the analysis unit can prioritize sleep quality analysis. This allows for more effective analysis by determining the priority of items to analyze according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the driver's emotion data into a generative AI and have the generative AI determine the priority of items to analyze.

[0102] The analysis unit can select the optimal analysis method during analysis, taking into account the driver's geographical location information. For example, on a highway, the analysis unit can prioritize the analysis of fatigue due to long-distance driving. For example, in an urban area, the analysis unit can prioritize the analysis of stress due to traffic congestion. For example, in a mountainous area, the analysis unit can prioritize the analysis of decreased attention due to changes in road conditions. By selecting the optimal analysis method based on geographical location information, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the driver's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0103] The monitoring unit can estimate the driver's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the driver is stressed, the monitoring unit can increase the monitoring frequency. For example, if the driver is relaxed, the monitoring unit can decrease the monitoring frequency. For example, if the driver is tired, the monitoring unit can maintain a moderate monitoring frequency. This allows for more effective monitoring by adjusting the monitoring frequency according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using a generative AI, or not. For example, the monitoring unit can input driver emotion data into a generative AI and have the generative AI adjust the monitoring frequency.

[0104] The monitoring unit can select the optimal monitoring method by referring to the driver's past health data during monitoring. For example, if the driver has a history of hypertension, the monitoring unit can enhance blood pressure monitoring. For example, if the driver has a history of abnormal heart rate, the monitoring unit can enhance heart rate monitoring. For example, if the driver has a history of abnormal body temperature, the monitoring unit can enhance body temperature monitoring. In this way, by referring to past health data, the monitoring unit can provide an individually optimized monitoring method. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input the driver's past health data into a generative AI and have the generative AI select the optimal monitoring method.

[0105] The monitoring unit can customize the monitoring methods based on the driver's current living situation during monitoring. For example, if the driver is driving for a long time, the monitoring unit can prompt the driver to take frequent breaks. For example, if the driver is driving at night, the monitoring unit can monitor the quality of sleep. For example, if the driver is driving in a high-stress environment, the monitoring unit can suggest ways to relax. By customizing the monitoring methods based on the current living situation, more effective monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's current living situation data into a generative AI and have the generative AI perform the customization of the monitoring methods.

[0106] The monitoring unit can estimate the driver's emotions and determine the priority of items to monitor based on the estimated emotions. For example, if the driver is stressed, the monitoring unit can prioritize monitoring heart rate. For example, if the driver is relaxed, the monitoring unit can prioritize monitoring body temperature. For example, if the driver is tired, the monitoring unit can prioritize monitoring sleep quality. This allows for more effective monitoring by determining the priority of items to monitor according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not. For example, the monitoring unit can input the driver's emotion data into a generative AI and have the generative AI determine the priority of items to monitor.

[0107] The monitoring unit can select the optimal monitoring method by considering the driver's geographical location information during monitoring. For example, on a highway, the monitoring unit can encourage rest due to fatigue from long hours of driving. For example, in an urban area, the monitoring unit can suggest relaxation methods considering stress caused by traffic congestion. For example, in a mountainous area, the monitoring unit can monitor the driver's health condition by considering decreased attention due to changes in road conditions. By selecting the optimal monitoring method based on geographical location information, more effective monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input the driver's geographical location information into a generative AI and have the generative AI select the optimal monitoring method.

[0108] The monitoring unit can analyze the driver's social media activity during monitoring and reflect the relevant information in the monitoring. For example, if the monitoring unit is posting stressful content on social media, it can suggest ways to relax. For example, if the monitoring unit is posting relaxing content on social media, it can continue with the normal monitoring method. For example, if the monitoring unit is posting tired content on social media, it can encourage the driver to take a break and monitor their health. This allows for monitoring tailored to the driver's situation by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the driver's social media activity data into a generative AI and have the generative AI customize the monitoring methods.

[0109] The messaging unit can estimate the driver's emotions and adjust the message's presentation based on the estimated emotions. For example, if the driver is tense, the messaging unit can send a message in a calm voice. If the driver is relaxed, the messaging unit can send a message in a cheerful voice. If the driver is in a hurry, the messaging unit can send a quick and concise message. By adjusting the message's presentation according to the driver's emotions, more effective messaging becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the messaging unit may be performed using a generative AI, or not. For example, the messaging unit can input driver emotion data into a generative AI and have the generative AI adjust the message's presentation.

[0110] The messaging unit can select the optimal message by referring to the driver's past response history when sending a message. For example, the messaging unit can prioritize messaging methods to which the driver has responded quickly in the past. For example, the messaging unit can avoid messaging methods that the driver has ignored in the past. For example, the messaging unit can continue using messaging methods that have been effective for the driver in the past. This allows for the provision of individually optimized messages by referring to past response history. Some or all of the above processing in the messaging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the messaging unit can input the driver's past response history data into a generation AI and have the generation AI select the optimal messaging method.

[0111] The messaging unit can adjust the timing of messages based on the driver's current driving conditions when sending a message. For example, the messaging unit can send messages earlier on highways. For example, the messaging unit can send messages in accordance with traffic signals in urban areas. For example, the messaging unit can send messages according to road conditions in mountainous areas. By adjusting the timing of messages according to driving conditions, more effective messaging becomes possible. Some or all of the above processing in the messaging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the messaging unit can input the driver's current driving condition data into the generation AI and have the generation AI adjust the timing of messages.

[0112] The messaging unit can estimate the driver's emotions and prioritize messages based on the estimated emotions. For example, if the driver is stressed, the messaging unit can prioritize important messages. For example, if the driver is relaxed, the messaging unit can prioritize normal messages. For example, if the driver is in a hurry, the messaging unit can prioritize urgent messages. This allows for more effective messaging by prioritizing messages according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the messaging unit may be performed using a generative AI, or not. For example, the messaging unit can input driver emotion data into a generative AI and have the generative AI determine the priority of messages.

[0113] The messaging unit can select the optimal message when sending a message, taking into account the driver's geographical location information. For example, the messaging unit can send messages earlier on highways. For example, the messaging unit can send messages in accordance with traffic signals in urban areas. For example, the messaging unit can send messages according to road conditions in mountainous areas. This makes it possible to send more effective messages by selecting the optimal message based on geographical location information. Some or all of the above processing in the messaging unit may be performed using, for example, a generation AI, or without a generation AI. For example, the messaging unit can input the driver's geographical location information into a generation AI and have the generation AI select the optimal message.

[0114] The messaging unit can analyze the driver's social media activity when sending a message and reflect relevant information in the message. For example, if the driver has posted stressful content on social media, the messaging unit can send a message in a calm voice. For example, if the driver has posted relaxed content on social media, the messaging unit can send a message in a cheerful voice. For example, if the driver has posted tired content on social media, the messaging unit can send a quick and concise message. This allows for messages tailored to the driver's situation by analyzing social media activity. Some or all of the processing described above in the messaging unit may be performed using, for example, a generative AI, or not. For example, the messaging unit can input the driver's social media activity data into a generative AI and have the generative AI adjust the way the message is expressed.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The driver safety management system can also include a style analysis unit that analyzes the driver's driving style. For example, the style analysis unit can analyze the frequency of acceleration and braking, and steering patterns, and evaluate the driving style. For instance, if there are frequent sudden accelerations or sudden brakes, the style analysis unit can determine that the driver is driving aggressively and issue a warning. Similarly, if the steering is unstable, the style analysis unit can determine that the driver may be fatigued and send a message encouraging them to take a break. This allows for more personalized safety management by analyzing the driver's driving style.

[0117] The driver safety management system may also include a history recording unit that records the driver's driving history. This unit can record, for example, the driver's driving time, distance traveled, and rest periods, allowing for analysis of long-term driving patterns. For instance, if the driver has been driving for extended periods, the unit can send a message prompting them to take a break. It can also provide warnings for specific times of day or routes based on past driving history. This enables more effective safety management by utilizing the driver's driving history.

[0118] The driver safety management system may also include an acoustic adjustment unit that estimates the driver's emotions and adjusts the music and ambient sounds during driving based on those estimated emotions. For example, if the driver is feeling stressed, the acoustic adjustment unit can play relaxing music. If the driver is tired, it can play music with an alerting effect. If the driver is relaxed, it can play normal music. In this way, by adjusting the acoustic environment according to the driver's emotions, a more comfortable driving environment can be provided.

[0119] The driver safety management system can also include a predictive unit that forecasts the driver's health condition. This unit can predict future health risks based, for example, on the driver's past health data and driving history. For instance, if the driver has a history of high blood pressure, the unit can prompt the driver to undergo regular health checkups. Furthermore, if the driver has been driving for extended periods, the unit can predict fatigue accumulation and send a message encouraging them to take a break. This allows for the prediction of the driver's health condition and proactive measures to be taken.

[0120] The driver safety management system may also include a lighting adjustment unit that estimates the driver's emotions and adjusts the lighting during driving based on those emotions. For example, if the driver is stressed, the lighting adjustment unit can provide relaxing, warm-colored lighting. If the driver is tired, it can provide bright, alerting lighting. If the driver is relaxed, it can provide normal lighting. This allows for a more comfortable driving environment by adjusting the lighting environment according to the driver's emotions.

[0121] The driver safety management system may also include a posture monitoring unit that monitors the driver's posture while driving. For example, the posture monitoring unit can monitor the driver's sitting position and spinal curvature, and provide guidance to maintain correct posture. If the driver is driving in the same position for a long period, the posture monitoring unit can send a message prompting the driver to stretch. Furthermore, if the driver's posture is poor, the posture monitoring unit can instruct the driver to correct their posture. This allows for monitoring the driver's posture and supporting healthy driving.

[0122] The driver safety management system may also include an air conditioning control unit that estimates the driver's emotions and adjusts the air conditioning settings during driving based on those estimated emotions. For example, if the driver is feeling stressed, the air conditioning control unit can provide a relaxing temperature setting. If the driver is tired, it can provide a cool temperature setting that has an invigorating effect. If the driver is relaxed, it can provide a normal temperature setting. In this way, a more comfortable driving environment can be provided by adjusting the air conditioning environment according to the driver's emotions.

[0123] The driver safety management system may also include an eye-tracking unit that tracks the driver's gaze while driving. The eye-tracking unit can, for example, issue a warning if the driver's gaze deviates from the road. For instance, if the driver is operating a smartphone or distracted by the surrounding scenery, the eye-tracking unit can send a message to alert the driver. Furthermore, if the driver's gaze remains within a certain range, the eye-tracking unit can determine that the driver is concentrating on driving. This allows the system to monitor the driver's gaze and support safe driving.

[0124] The driver safety management system may also include a navigation adjustment unit that estimates the driver's emotions and adjusts the navigation guidance during driving based on those estimated emotions. For example, if the driver is feeling stressed, the navigation adjustment unit can suggest a relaxing route. If the driver is tired, it can suggest a route with rest areas. If the driver is relaxed, it can suggest a normal route. In this way, by adjusting the navigation guidance according to the driver's emotions, a more comfortable driving environment can be provided.

[0125] The driver safety management system can also include a food and beverage management unit to manage the driver's eating and drinking habits while driving. For example, the food and beverage management unit can send messages encouraging hydration if the driver is driving for extended periods. It can also send messages encouraging energy replenishment if the driver is fatigued. Furthermore, it can suggest appropriate rest stops if the driver is approaching mealtime. This allows for the management of the driver's eating and drinking habits, supporting healthy driving.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The monitoring unit monitors the driver's condition. The monitoring unit can, for example, analyze the driver's facial expressions and eye movements. The monitoring unit uses an AI camera to monitor the driver's facial expressions in real time and detect signs of drowsy driving or inattentive driving. The monitoring unit can also analyze the driver's eye movements and detect how often the eyes close or how sluggish the eye movements are. For example, if the eyes close frequently or the eye movements are sluggish, it will be determined that the driver is tired. Step 2: The management unit manages the driver's health status based on the conditions monitored by the monitoring unit. The management unit can, for example, monitor the driver's heart rate and body temperature. The management unit uses an AI communication tool to monitor the driver's heart rate and body temperature in real time and issues a warning if an abnormality is detected. The management unit also comprehensively manages the driver's health status and can send a message to the driver encouraging them to take a break if an abnormality is detected. For example, if the heart rate or body temperature is abnormally high, a message encouraging the driver to take a break will be sent. Step 3: The warning unit issues warnings based on the health status managed by the control unit. For example, the warning unit can send a message to the driver encouraging them to take a break. The warning unit uses an AI communication tool to send messages to the driver encouraging them to take a break. The warning unit can also issue a warning to the driver if an abnormality is detected in the driver's health status. For example, it will issue a warning to the driver if their heart rate or body temperature is abnormally high.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the monitoring unit, management unit, and warning unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit uses the AI ​​camera of the smart device 14 to monitor the driver's facial expressions and eye movements in real time and detect signs of drowsy driving or inattentive driving. The management unit, for example, uses the identification processing unit 290 of the data processing unit 12 to monitor the driver's heart rate and body temperature and issues a warning if an abnormality is detected. The warning unit, for example, uses the AI ​​communication tool of the smart device 14 to send a message to the driver urging them to take a break. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the monitoring unit, management unit, and warning unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit uses the AI ​​camera of the smart glasses 214 to monitor the driver's facial expressions and eye movements in real time and detect signs of drowsy driving or inattentive driving. The management unit, for example, uses the identification processing unit 290 of the data processing unit 12 to monitor the driver's heart rate and body temperature and issues a warning if an abnormality is detected. The warning unit, for example, uses the AI ​​communication tool of the smart glasses 214 to send a message to the driver urging them to take a break. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the monitoring unit, management unit, and warning unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit uses the AI ​​camera of the headset terminal 314 to monitor the driver's facial expressions and eye movements in real time and detect signs of drowsy driving or inattentive driving. The management unit, for example, uses the identification processing unit 290 of the data processing unit 12 to monitor the driver's heart rate and body temperature and issues a warning if an abnormality is detected. The warning unit, for example, uses the AI ​​communication tool of the headset terminal 314 to send a message to the driver urging them to take a break. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the monitoring unit, management unit, and warning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit uses the robot 414's AI camera to monitor the driver's facial expressions and eye movements in real time and detect signs of drowsy driving or inattentive driving. The management unit, for example, uses the data processing unit 12's identification processing unit 290 to monitor the driver's heart rate and body temperature and issues a warning if an abnormality is detected. The warning unit, for example, uses the robot 414's AI communication tool to send a message to the driver urging them to take a break. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A monitoring unit that monitors the driver's status, A management unit manages the driver's health status based on the conditions monitored by the aforementioned monitoring unit, The system includes a warning unit that issues a warning based on the health status managed by the aforementioned control unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, It is equipped with an analysis unit that analyzes the driver's facial expressions and eye movements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, It features a monitoring unit that monitors the driver's heart rate and body temperature. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned warning unit is It includes a message unit that sends messages to the driver encouraging them to take a break. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The system analyzes the frequency of eye closures and the sluggishness of eye movements to determine if the driver is fatigued. The system described in Appendix 2, characterized by the features described herein. (Note 6) The monitoring unit, It detects abnormalities in heart rate and body temperature, and issues a warning if an abnormality is detected. The system described in Appendix 3, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, The system estimates the driver's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, Analyze the driver's past driving history and select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, During monitoring, the focus of monitoring changes based on the driver's current driving environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, The system estimates the driver's emotions and prioritizes monitoring items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, During monitoring, the system prioritizes monitoring of highly relevant items, taking into account the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, During monitoring, analyze the driver's social media activity and incorporate relevant information into the monitoring process. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, The system estimates the driver's emotions and adjusts health management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, When managing a driver's health status, the optimal management method is selected by referring to the driver's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, When managing health status, customize the management methods based on the driver's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, It estimates the driver's emotions and prioritizes health items to manage based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, When managing the driver's health status, the optimal management method is selected by considering the driver's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, When managing drivers' health status, analyze their social media activity and incorporate relevant information into the management process. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is The system estimates the driver's emotions and adjusts the way warnings are expressed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When a warning is issued, the system selects the most appropriate warning method by referring to the driver's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is When a warning is issued, the timing of the warning is adjusted based on the driver's current driving situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is The system estimates the driver's emotions and prioritizes warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When issuing a warning, the system selects the most appropriate warning method, taking into account the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is When issuing a warning, the system analyzes the driver's social media activity and incorporates relevant information into the warning. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts the accuracy of the analysis based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During the analysis, the optimal analysis method is selected by referring to the driver's past facial expression data. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During the analysis, the focus of the analysis is changed to take into account the driver's current driving situation. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned analysis unit, The system estimates the driver's emotions and prioritizes the items to analyze based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned analysis unit, During the analysis, the optimal analysis method is selected by considering the driver's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 30) The monitoring unit, The system estimates the driver's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The monitoring unit, During monitoring, the optimal monitoring method is selected by referring to the driver's past health data. The system described in Appendix 3, characterized by the features described herein. (Note 32) The monitoring unit, During monitoring, the monitoring methods are customized based on the driver's current living situation. The system described in Appendix 3, characterized by the features described herein. (Note 33) The monitoring unit, The system estimates the driver's emotions and prioritizes the items to monitor based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the driver's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 35) The monitoring unit, During monitoring, analyze the driver's social media activity and incorporate relevant information into the monitoring process. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned message section is, The system estimates the driver's emotions and adjusts the way messages are presented based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned message section is, When sending a message, the system selects the most appropriate message by referring to the driver's past response history. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned message section is, When sending a message, the timing of the message will be adjusted based on the driver's current driving status. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned message section is, It estimates the driver's emotions and prioritizes messages based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned message section is, When sending a message, the system selects the most appropriate message by considering the driver's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned message section is, When sending a message, the system analyzes the driver's social media activity and incorporates relevant information into the message. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors the driver's status, A management unit manages the driver's health status based on the conditions monitored by the aforementioned monitoring unit, The system includes a warning unit that issues a warning based on the health status managed by the aforementioned control unit. A system characterized by the following features.

2. The aforementioned monitoring unit, It is equipped with an analysis unit that analyzes the driver's facial expressions and eye movements. The system according to feature 1.

3. The aforementioned management department, It features a monitoring unit that monitors the driver's heart rate and body temperature. The system according to feature 1.

4. The aforementioned warning unit is It includes a message unit that sends messages to the driver encouraging them to take a break. The system according to feature 1.

5. The aforementioned analysis unit, The system analyzes the frequency of eye closures and the sluggishness of eye movements to determine if the driver is fatigued. The system according to feature 2.

6. The monitoring unit, It detects abnormalities in heart rate and body temperature, and issues a warning if an abnormality is detected. The system according to claim 3.

7. The aforementioned monitoring unit, The system estimates the driver's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

8. The aforementioned monitoring unit, Analyze the driver's past driving history and select the optimal monitoring method. The system according to feature 1.

9. The aforementioned monitoring unit, During monitoring, the focus of monitoring changes based on the driver's current driving environment. The system according to feature 1.

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