system

Abstract

The system according to this embodiment aims to analyze video data from a dashcam in real time and propose and notify the optimal countermeasures against dangerous driving behavior. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a proposal unit, a notification unit, and a storage unit. The acquisition unit acquires video data from a drive recorder in real time. The analysis unit analyzes the video data acquired by the acquisition unit and detects dangerous driving behaviors. The proposal unit proposes the most appropriate countermeasures based on the dangerous driving behaviors detected by the analysis unit. The notification unit notifies the police of the countermeasures proposed by the proposal unit. The storage unit stores data on dangerous driving behaviors detected by the analysis unit and uses it to improve future driving behaviors.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 prior art, video data of a drive recorder has not been sufficiently analyzed in real time to immediately detect and take countermeasures against dangerous driving behaviors, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze video data of a drive recorder in real time and propose and report optimal countermeasures against dangerous driving behaviors.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a proposal unit, a notification unit, and a storage unit. The acquisition unit acquires video data from a drive recorder in real time. The analysis unit analyzes the video data acquired by the acquisition unit and detects dangerous driving behaviors. The proposal unit proposes the most appropriate countermeasures based on the dangerous driving behaviors detected by the analysis unit. The notification unit notifies the police of the countermeasures proposed by the proposal unit. The storage unit stores data on dangerous driving behaviors detected by the analysis unit and uses it to improve future driving behaviors. [Effects of the Invention]

[0007] The system according to this embodiment can analyze video data from a dashcam in real time and propose and notify the driver of the optimal countermeasures against dangerous driving behavior. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 drive recorder system according to an embodiment of the present invention is a system that analyzes drive recorder video data in real time, detects and evaluates dangerous driving behavior (especially aggressive driving), and then automatically proposes the optimal countermeasures using a generating AI and notifies the police. This drive recorder system acquires drive recorder video data in real time, and the generating AI analyzes it to detect dangerous driving behavior. For example, sudden lane changes and abnormally close following distances may be detected. At this time, the generating AI evaluates the dangerous driving behavior based on past driving data and traffic rules. Next, the generating AI proposes the optimal countermeasures based on the detected dangerous driving behavior. For example, countermeasures such as issuing a warning to the driver or automatically controlling the vehicle speed may be considered. The generating AI also reports the detected dangerous driving behavior to the police. At this time, the report includes details of the dangerous driving behavior and video data. Furthermore, the generating AI accumulates data on the detected dangerous driving behavior and uses it to improve future driving behavior. For example, it can analyze the driver's driving habits and propose areas for improvement. This system allows for the detection of dangerous driving behavior in real time and the implementation of appropriate countermeasures, which is expected to prevent traffic accidents and improve driver safety. Furthermore, prompt reporting to the police enables a swift response to dangerous driving behavior. In short, the dashcam system can detect dangerous driving behavior in real time and implement appropriate countermeasures.

[0029] The drive recorder system according to this embodiment comprises an acquisition unit, an analysis unit, a proposal unit, a notification unit, and a storage unit. The acquisition unit acquires video data from the drive recorder in real time. The acquisition unit acquires video data from, for example, a camera mounted on the vehicle. The acquisition unit can also stream the video data in real time. Furthermore, the acquisition unit can transmit the video data to a cloud server, making it accessible from a remote location. The analysis unit analyzes the video data acquired by the acquisition unit and detects dangerous driving behaviors. The analysis unit detects dangerous driving behaviors such as sudden lane changes or abnormally close following distances, for example, using a generation AI. The analysis unit can also evaluate dangerous driving behaviors based on past driving data and traffic rules. The proposal unit proposes the optimal countermeasures based on the dangerous driving behaviors detected by the analysis unit. The proposal unit proposes countermeasures such as issuing a warning to the driver. The proposal unit can also propose countermeasures such as automatically controlling the vehicle's speed. The notification unit notifies the police of the countermeasures proposed by the proposal unit. The reporting unit reports details and video data of dangerous driving behavior to the police, for example. The storage unit stores data on dangerous driving behavior detected by the analysis unit and uses it to improve future driving behavior. The storage unit can, for example, analyze the driver's driving habits and suggest areas for improvement. As a result, the drive recorder system according to this embodiment can detect dangerous driving behavior in real time and take appropriate countermeasures. Some or all of the above-described processes in the acquisition unit, analysis unit, suggestion unit, reporting unit, and storage unit may be performed using AI, for example, or without AI. For example, the acquisition unit can acquire video data from the drive recorder in real time, and the analysis unit can input the acquired video data into a generating AI and have the generating AI perform the detection of dangerous driving behavior. The suggestion unit can propose the optimal countermeasures based on the dangerous driving behavior detected by the generating AI, and the reporting unit can report the proposed countermeasures to the police. The storage unit can store data on dangerous driving behavior detected by the generating AI and use it to improve future driving behavior.

[0030] The acquisition unit acquires video data from a dashcam in real time. For example, it acquires video data from cameras mounted on the vehicle. Specifically, it collects high-resolution video data from multiple cameras installed in front, behind, and on the sides of the vehicle and processes it in real time. This allows for a detailed understanding of the vehicle's surroundings. The acquisition unit can also stream video data in real time. For example, video data acquired while the vehicle is in motion can be transmitted to a cloud server via the internet, making it accessible remotely. This allows the driver to check the situation in real time and take appropriate action in the event of an accident or trouble. Furthermore, the acquisition unit can transmit video data to a cloud server, making it accessible remotely. Since video data stored on the cloud server can be played back and reviewed later, it can be used to investigate the cause of accidents and analyze driving behavior. Through these functions, the acquisition unit can gain a detailed understanding of the vehicle's surroundings and collect and transmit data in real time.

[0031] The analysis unit analyzes video data acquired by the acquisition unit to detect dangerous driving behaviors. For example, the analysis unit uses a generation AI to detect dangerous driving behaviors such as sudden lane changes and abnormally close following distances. Specifically, the generation AI receives video data as input and uses image recognition technology to analyze the vehicle's movement and surrounding conditions. For example, it measures the use of turn signals during lane changes and the distance to other vehicles to detect sudden lane changes and abnormally close following distances. The generation AI can also evaluate dangerous driving behaviors based on past driving data and traffic rules. For example, a generation AI that has learned from past driving data can determine whether a particular driving pattern is dangerous and issue a warning in real time. This allows the analysis unit to quickly and accurately analyze the collected video data and detect dangerous driving behaviors in real time. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual driving patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The suggestion unit proposes optimal countermeasures based on dangerous driving behaviors detected by the analysis unit. For example, the suggestion unit proposes countermeasures that issue warnings to the driver. Specifically, it can display warning messages in the driver's field of vision or provide voice alerts to immediately make the driver aware of dangerous driving behaviors. The suggestion unit can also propose countermeasures that automatically control the vehicle's speed. For example, if a sudden lane change or dangerously close approach is detected, the vehicle's speed can be automatically reduced to reduce the risk of an accident. Furthermore, the suggestion unit can analyze the driver's driving habits and propose long-term improvements. For example, if a driver tends to brake suddenly frequently, the suggestion unit can analyze the cause and provide advice to improve driving skills. This allows the suggestion unit to quickly propose appropriate countermeasures to the driver and prevent dangerous driving behaviors. In addition, the suggestion unit can collect driver feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion unit to provide drivers with optimal countermeasures and improve the safety of driving behaviors.

[0033] The reporting unit reports the proposed measures to the police. For example, the reporting unit reports details and video data of dangerous driving behavior to the police. Specifically, the reporting unit automatically transmits data on dangerous driving behavior detected by the analysis unit to the police and reports the situation in real time. This allows the police to respond quickly and deter dangerous driving behavior. The reporting unit can also generate and provide detailed reports, including video data, to the police. This allows the police to take appropriate action based on concrete evidence. Furthermore, the reporting unit has a function to automatically notify the police in emergencies, allowing drivers to receive prompt assistance when they find themselves in a dangerous situation. This enables the reporting unit to quickly report dangerous driving behavior to the police and prompt appropriate action.

[0034] The data storage unit accumulates data on dangerous driving behaviors detected by the analysis unit and uses it to improve future driving behavior. For example, the data storage unit can analyze drivers' driving habits and suggest areas for improvement. Specifically, the data storage unit stores past driving data over a long period and analyzes drivers' driving patterns and tendencies in detail. This allows it to identify areas for improvement in drivers' driving skills and habits and provide specific advice. The data storage unit can also compare and analyze data from multiple drivers to extract common problems and areas for improvement. This helps to improve the driving skills of drivers as a whole. Furthermore, the data storage unit can generate statistical information and reports based on driving data and provide them to drivers and managers. This allows drivers to objectively evaluate their own driving behavior and set specific goals for improvement. Through these functions, the data storage unit can support the improvement of drivers' driving behavior and contribute to the overall improvement of traffic safety.

[0035] The suggestion unit can propose measures to issue warnings to the driver. For example, the suggestion unit can issue an audible warning. For example, the suggestion unit can issue a visual warning. For example, the suggestion unit can issue a vibration warning. By issuing warnings to the driver, dangerous driving behaviors can be prevented. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can propose measures to issue warnings to the driver based on dangerous driving behaviors detected by a generative AI.

[0036] The proposal unit can propose measures to automatically control the vehicle's speed. For example, the proposal unit can propose measures to reduce the vehicle's speed. For example, the proposal unit can propose measures to keep the vehicle's speed constant. For example, the proposal unit can propose measures to accelerate the vehicle's speed. By automatically controlling the vehicle's speed, dangerous driving behavior can be suppressed. Some or all of the above-described processes in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can propose measures to automatically control the vehicle's speed based on dangerous driving behavior detected by a generative AI.

[0037] The reporting unit can report details and video data of dangerous driving behavior to the police. For example, the reporting unit can report details such as the type of dangerous driving behavior, the time it occurred, and location information to the police. For example, the reporting unit can report video data of dangerous driving behavior to the police. For example, the reporting unit can report audio data of dangerous driving behavior to the police. This allows for a swift response by reporting to the police. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can report details and video data of dangerous driving behavior detected by a generating AI to the police.

[0038] The data storage unit can analyze the driver's driving habits and suggest areas for improvement. For example, the data storage unit can analyze the driver's driving frequency, driving time, driving style, etc. For example, based on the driver's driving habits, the data storage unit can suggest areas for improvement in driving skills and safe driving. For example, based on the driver's driving history, the data storage unit can analyze patterns of driving behavior and suggest areas for improvement. In this way, by analyzing the driver's driving habits and suggesting areas for improvement, it can be used to improve future driving behavior. Some or all of the above processing in the data storage unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data storage unit can suggest areas for improvement based on the driver's driving habits analyzed by a generative AI.

[0039] The acquisition unit can analyze past driving data and select the optimal method for acquiring video data. For example, if the acquisition unit can determine from past driving data that dangerous driving behaviors frequently occur during a specific time period, it can focus on acquiring video data during that time period. For example, the acquisition unit can adjust the frequency of video data acquisition on specific roads or in specific areas based on past driving data. For example, the acquisition unit can analyze past driving data and optimize the video data acquisition method according to the driver's driving pattern. This enables efficient data acquisition by selecting the optimal video data acquisition method based on past driving data. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past driving data into a generating AI and have the generating AI select the optimal video data acquisition method.

[0040] The acquisition unit can filter video data based on the driver's current driving status and traffic conditions. For example, if the driver is driving on a highway, the acquisition unit can prioritize video data acquisition based on following distance and speed. For example, if the driver is driving in an urban area, the acquisition unit can prioritize video data acquisition based on traffic signals and pedestrian movements. For example, if the driver is stuck in traffic, the acquisition unit can prioritize video data acquisition based on the movements of surrounding vehicles. This allows for the priority acquisition of important data by filtering according to driving and traffic conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the driver's current driving status and traffic conditions into a generating AI and have the generating AI perform the filtering.

[0041] The acquisition unit can prioritize the acquisition of highly relevant data by considering the driver's geographical location information when acquiring video data. For example, if the driver is in a specific hazardous area, the acquisition unit can prioritize the acquisition of video data from that area. For example, if the driver is at a specific intersection, the acquisition unit can prioritize the acquisition of video data from that intersection. For example, if the driver is on a specific road, the acquisition unit can prioritize the acquisition of video data from that road. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the driver's geographical location information into a generating AI and cause the generating AI to prioritize the acquisition of highly relevant data.

[0042] The acquisition unit can analyze the driver's social media activity when acquiring video data and acquire relevant data. For example, if the driver mentions a specific location on social media, the acquisition unit can prioritize acquiring video data of that location. For example, if the driver mentions a specific event on social media, the acquisition unit can prioritize acquiring video data of that event. For example, if the driver mentions a specific road on social media, the acquisition unit can prioritize acquiring video data of that road. In this way, relevant data can be prioritized by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the driver's social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0043] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of video data during the analysis. For example, the analysis unit can simultaneously analyze front and rear video data to detect dangerous driving behaviors. For example, the analysis unit can simultaneously analyze left and right video data to detect dangerous driving behaviors during lane changes. For example, the analysis unit can integrate and analyze video data from multiple cameras to detect dangerous driving behaviors in all directions. This improves the accuracy of the analysis by considering the interrelationships of the video data. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input video data from multiple cameras into a generation AI and have the generation AI perform an analysis that considers the interrelationships.

[0044] The analysis unit can perform analysis while considering the driver's attribute information. For example, the analysis unit can adjust the detection criteria for dangerous driving behavior by considering the driver's age and gender. For example, the analysis unit can improve the accuracy of the analysis by considering the driver's driving experience. For example, the analysis unit can improve the accuracy of detecting dangerous driving behavior by considering the driver's past driving history. In this way, the accuracy of the analysis is improved by considering the driver's attribute information. 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 attribute information into a generating AI and have the generating AI perform the analysis.

[0045] The analysis unit can perform analysis while considering the geographical distribution of video data. For example, the analysis unit can focus its analysis on dangerous driving behavior in a specific area. For example, the analysis unit can focus its analysis on dangerous driving behavior on a specific road. For example, the analysis unit can focus its analysis on dangerous driving behavior at a specific intersection. By considering the geographical distribution, it is possible to focus the analysis on dangerous driving behavior in specific areas or on specific roads. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the geographical distribution of video data into a generative AI and have the generative AI perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest traffic safety research. For example, the analysis unit can improve the accuracy of its analysis by referring to past traffic accident data. For example, the analysis unit can improve the accuracy of its analysis by referring to the driving data of other drivers. Thus, the accuracy of the analysis is improved by referring to relevant literature. 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 relevant literature into a generating AI and have the generating AI perform the analysis.

[0047] The proposal unit can adjust the level of detail of countermeasures based on the importance of the dangerous driving behavior when making a proposal. For example, the proposal unit can propose detailed countermeasures for serious dangerous behaviors. For example, the proposal unit can propose concise countermeasures for minor dangerous behaviors. For example, the proposal unit can propose countermeasures with a moderate level of detail for moderate dangerous behaviors. In this way, by adjusting the level of detail of countermeasures according to the importance of the dangerous driving behavior, appropriate countermeasures can be proposed. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can adjust the level of detail of countermeasures based on the importance of the dangerous driving behavior detected by the generative AI.

[0048] The proposal unit can apply different countermeasure algorithms depending on the category of driving behavior when making a proposal. For example, the proposal unit can apply a countermeasure algorithm specifically for lane changes to dangerous behaviors related to lane changes. For example, the proposal unit can apply a countermeasure algorithm specifically for following distance to dangerous behaviors related to following distance. For example, the proposal unit can apply a countermeasure algorithm specifically for speeding to dangerous behaviors related to speeding. By applying a countermeasure algorithm according to the category of driving behavior, more effective countermeasures can be proposed. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can apply a countermeasure algorithm based on the category of driving behavior detected by a generative AI.

[0049] The proposal unit can determine the priority of countermeasures based on when the dangerous driving behavior occurred. For example, the proposal unit can propose countermeasures with the highest priority for dangerous behavior that occurred recently. For example, the proposal unit can propose countermeasures with the normal priority for dangerous behavior that occurred in the past. For example, the proposal unit can propose preventative measures for dangerous behavior that may occur in the future. In this way, appropriate countermeasures can be proposed by determining the priority of countermeasures according to when the dangerous driving behavior occurred. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can determine the priority of countermeasures based on when the dangerous driving behavior detected by the generative AI occurred.

[0050] The suggestion unit can adjust the order of countermeasures based on the relationships between driving behaviors when making suggestions. For example, if lane changes and dangerous behaviors such as maintaining a safe following distance are related, the suggestion unit can suggest both countermeasures consecutively. For example, if speeding and sudden braking are related, the suggestion unit can suggest both countermeasures consecutively. For example, if running a red light and dangerous behavior at an intersection are related, the suggestion unit can suggest both countermeasures consecutively. By adjusting the order of countermeasures based on the relationships between driving behaviors, more effective countermeasures can be proposed. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can adjust the order of countermeasures based on the relationships between driving behaviors detected by a generative AI.

[0051] The reporting unit can adjust the content of the report based on the level of detail of the dangerous driving behavior at the time of reporting. For example, the reporting unit can generate detailed reports for serious dangerous behaviors. For example, the reporting unit can generate concise reports for minor dangerous behaviors. For example, the reporting unit can generate reports with a moderate level of detail for moderate dangerous behaviors. This allows for appropriate reporting by adjusting the content of the report according to the level of detail of the dangerous driving behavior. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can adjust the content of the report based on the level of detail of the dangerous driving behavior detected by the generating AI.

[0052] The reporting unit can adjust the content of the report based on the location where the dangerous driving behavior occurred. For example, for dangerous behavior occurring in a specific area, the reporting unit can generate a report that includes detailed information about that area. For example, for dangerous behavior occurring on a specific road, the reporting unit can generate a report that includes detailed information about that road. For example, for dangerous behavior occurring at a specific intersection, the reporting unit can generate a report that includes detailed information about that intersection. By adjusting the report content based on the location of occurrence, it becomes possible to provide reports with more detailed information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can adjust the content of the report based on the location of the dangerous driving behavior detected by the generating AI.

[0053] The data storage unit can optimize its data storage algorithm by referring to past driving data during storage. For example, the data storage unit can concentrate data storage during specific time periods based on past driving data. For example, the data storage unit can adjust the data storage frequency for specific roads or areas based on past driving data. For example, the data storage unit can analyze past driving data and optimize the data storage method according to the driver's driving pattern. This allows the data storage algorithm to be optimized by referring to past driving data. Some or all of the above processing in the data storage unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data storage unit can input past driving data into a generation AI and have the generation AI perform the optimization of the data storage algorithm.

[0054] The data storage unit can store data while considering the driver's attribute information. The data storage unit can adjust the data storage method, for example, by considering the driver's age and gender. The data storage unit can optimize the data storage method, for example, by considering the driver's driving experience. The data storage unit can adjust the data storage method, for example, by considering the driver's past driving history. This improves the accuracy of data storage by considering the driver's attribute information. Some or all of the above processing in the data storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data storage unit can input the driver's attribute information into a generating AI and have the generating AI perform the adjustment of the data storage method.

[0055] The data storage unit can store data while considering the geographical distribution of driving behavior. For example, the data storage unit can focus on storing driving behavior data in a specific area. For example, the data storage unit can focus on storing driving behavior data on a specific road. For example, the data storage unit can focus on storing driving behavior data at a specific intersection. In this way, by considering geographical distribution, driving behavior data in a specific area or on a specific road can be focused on storing. Some or all of the above processing in the data storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data storage unit can input geographical distribution data of driving behavior into a generating AI and have the generating AI perform data storage.

[0056] The storage unit can improve the accuracy of data storage by referring to relevant literature during storage. For example, the storage unit can improve the accuracy of data storage by referring to the latest traffic safety research. For example, the storage unit can improve the accuracy of data storage by referring to past traffic accident data. For example, the storage unit can improve the accuracy of data storage by referring to driving data of other drivers. In this way, the accuracy of data storage is improved by referring to relevant literature. Some or all of the above processing in the storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the storage unit can input relevant literature into a generating AI and have the generating AI perform the improvement of data storage accuracy.

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

[0058] The acquisition unit can analyze past driving data and select the optimal method for acquiring video data. For example, if past driving data shows that dangerous driving behaviors frequently occur during a specific time period, the unit can focus on acquiring video data during that time period. Based on past driving data, the unit can adjust the frequency of video data acquisition on specific roads or in specific areas. By analyzing past driving data, the unit can optimize the video data acquisition method according to the driver's driving pattern. This enables efficient data acquisition by selecting the optimal video data acquisition method based on past driving data. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past driving data into a generating AI and have the generating AI select the optimal video data acquisition method.

[0059] The acquisition unit can filter video data based on the driver's current driving status and traffic conditions. For example, if the driver is driving on a highway, the acquisition unit can prioritize video data acquisition based on following distance and speed. If the driver is driving in an urban area, the acquisition unit can prioritize video data acquisition based on traffic signals and pedestrian movements. If the driver is stuck in traffic, the acquisition unit can prioritize video data acquisition based on the movements of surrounding vehicles. This allows for the priority acquisition of important data by filtering according to driving and traffic conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the driver's current driving status and traffic conditions into a generating AI and have the generating AI perform the filtering.

[0060] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of video data during the analysis process. For example, it can simultaneously analyze front and rear video data to detect dangerous driving behaviors. It can simultaneously analyze left and right video data to detect dangerous driving behaviors during lane changes. It can integrate and analyze video data from multiple cameras to detect dangerous driving behaviors in all directions. This improves the accuracy of the analysis by considering the interrelationships of the video data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input video data from multiple cameras into a generation AI and have the generation AI perform an analysis that considers the interrelationships.

[0061] The proposal unit can adjust the level of detail of countermeasures based on the importance of the dangerous driving behavior when making a proposal. For example, it can propose detailed countermeasures for serious dangerous behaviors, concise countermeasures for minor dangerous behaviors, and countermeasures with a moderate level of detail for moderately dangerous behaviors. In this way, by adjusting the level of detail of countermeasures according to the importance of the dangerous driving behavior, appropriate countermeasures can be proposed. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can adjust the level of detail of countermeasures based on the importance of the dangerous driving behavior detected by the generative AI.

[0062] The data storage unit can optimize its data storage algorithm by referring to past driving data during storage. For example, it can focus on storing data during specific time periods based on past driving data. It can adjust the data storage frequency for specific roads or regions based on past driving data. It can analyze past driving data and optimize the data storage method according to the driver's driving pattern. This allows the data storage algorithm to be optimized by referring to past driving data. Some or all of the above-described processes in the data storage unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data storage unit can input past driving data into a generation AI and have the generation AI perform the optimization of the data storage algorithm.

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

[0064] Step 1: The acquisition unit acquires video data from the dashcam in real time. For example, it can acquire video data from a camera mounted on the vehicle and stream it in real time. It can also send the video data to a cloud server and make it accessible from a remote location. Step 2: The analysis unit analyzes the video data acquired by the acquisition unit to detect dangerous driving behaviors. For example, it uses a generation AI to detect dangerous driving behaviors such as sudden lane changes or dangerously close following distances. It can also evaluate dangerous driving behaviors based on past driving data and traffic rules. Step 3: The proposal unit proposes the optimal countermeasures based on the dangerous driving behavior detected by the analysis unit. For example, it can propose measures such as issuing a warning to the driver or automatically controlling the vehicle's speed. Step 4: The reporting department reports the proposed measures to the police. For example, they report details of dangerous driving behavior and video data to the police. Step 5: The storage unit stores data on dangerous driving behaviors detected by the analysis unit and uses it to improve future driving behavior. For example, it can analyze the driver's driving habits and suggest areas for improvement.

[0065] (Example of form 2) The drive recorder system according to an embodiment of the present invention is a system that analyzes drive recorder video data in real time, detects and evaluates dangerous driving behavior (especially aggressive driving), and then automatically proposes the optimal countermeasures using a generating AI and notifies the police. This drive recorder system acquires drive recorder video data in real time, and the generating AI analyzes it to detect dangerous driving behavior. For example, sudden lane changes and abnormally close following distances may be detected. At this time, the generating AI evaluates the dangerous driving behavior based on past driving data and traffic rules. Next, the generating AI proposes the optimal countermeasures based on the detected dangerous driving behavior. For example, countermeasures such as issuing a warning to the driver or automatically controlling the vehicle speed may be considered. The generating AI also reports the detected dangerous driving behavior to the police. At this time, the report includes details of the dangerous driving behavior and video data. Furthermore, the generating AI accumulates data on the detected dangerous driving behavior and uses it to improve future driving behavior. For example, it can analyze the driver's driving habits and propose areas for improvement. This system allows for the detection of dangerous driving behavior in real time and the implementation of appropriate countermeasures, which is expected to prevent traffic accidents and improve driver safety. Furthermore, prompt reporting to the police enables a swift response to dangerous driving behavior. In short, the dashcam system can detect dangerous driving behavior in real time and implement appropriate countermeasures.

[0066] The drive recorder system according to this embodiment comprises an acquisition unit, an analysis unit, a proposal unit, a notification unit, and a storage unit. The acquisition unit acquires video data from the drive recorder in real time. The acquisition unit acquires video data from, for example, a camera mounted on the vehicle. The acquisition unit can also stream the video data in real time. Furthermore, the acquisition unit can transmit the video data to a cloud server, making it accessible from a remote location. The analysis unit analyzes the video data acquired by the acquisition unit and detects dangerous driving behaviors. The analysis unit detects dangerous driving behaviors such as sudden lane changes or abnormally close following distances, for example, using a generation AI. The analysis unit can also evaluate dangerous driving behaviors based on past driving data and traffic rules. The proposal unit proposes the optimal countermeasures based on the dangerous driving behaviors detected by the analysis unit. The proposal unit proposes countermeasures such as issuing a warning to the driver. The proposal unit can also propose countermeasures such as automatically controlling the vehicle's speed. The notification unit notifies the police of the countermeasures proposed by the proposal unit. The reporting unit reports details and video data of dangerous driving behavior to the police, for example. The storage unit stores data on dangerous driving behavior detected by the analysis unit and uses it to improve future driving behavior. The storage unit can, for example, analyze the driver's driving habits and suggest areas for improvement. As a result, the drive recorder system according to this embodiment can detect dangerous driving behavior in real time and take appropriate countermeasures. Some or all of the above-described processes in the acquisition unit, analysis unit, suggestion unit, reporting unit, and storage unit may be performed using AI, for example, or without AI. For example, the acquisition unit can acquire video data from the drive recorder in real time, and the analysis unit can input the acquired video data into a generating AI and have the generating AI perform the detection of dangerous driving behavior. The suggestion unit can propose the optimal countermeasures based on the dangerous driving behavior detected by the generating AI, and the reporting unit can report the proposed countermeasures to the police. The storage unit can store data on dangerous driving behavior detected by the generating AI and use it to improve future driving behavior.

[0067] The acquisition unit acquires video data from a dashcam in real time. For example, it acquires video data from cameras mounted on the vehicle. Specifically, it collects high-resolution video data from multiple cameras installed in front, behind, and on the sides of the vehicle and processes it in real time. This allows for a detailed understanding of the vehicle's surroundings. The acquisition unit can also stream video data in real time. For example, video data acquired while the vehicle is in motion can be transmitted to a cloud server via the internet, making it accessible remotely. This allows the driver to check the situation in real time and take appropriate action in the event of an accident or trouble. Furthermore, the acquisition unit can transmit video data to a cloud server, making it accessible remotely. Since video data stored on the cloud server can be played back and reviewed later, it can be used to investigate the cause of accidents and analyze driving behavior. Through these functions, the acquisition unit can gain a detailed understanding of the vehicle's surroundings and collect and transmit data in real time.

[0068] The analysis unit analyzes video data acquired by the acquisition unit to detect dangerous driving behaviors. For example, the analysis unit uses a generation AI to detect dangerous driving behaviors such as sudden lane changes and abnormally close following distances. Specifically, the generation AI receives video data as input and uses image recognition technology to analyze the vehicle's movement and surrounding conditions. For example, it measures the use of turn signals during lane changes and the distance to other vehicles to detect sudden lane changes and abnormally close following distances. The generation AI can also evaluate dangerous driving behaviors based on past driving data and traffic rules. For example, a generation AI that has learned from past driving data can determine whether a particular driving pattern is dangerous and issue a warning in real time. This allows the analysis unit to quickly and accurately analyze the collected video data and detect dangerous driving behaviors in real time. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual driving patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The suggestion unit proposes optimal countermeasures based on dangerous driving behaviors detected by the analysis unit. For example, the suggestion unit proposes countermeasures that issue warnings to the driver. Specifically, it can display warning messages in the driver's field of vision or provide voice alerts to immediately make the driver aware of dangerous driving behaviors. The suggestion unit can also propose countermeasures that automatically control the vehicle's speed. For example, if a sudden lane change or dangerously close approach is detected, the vehicle's speed can be automatically reduced to reduce the risk of an accident. Furthermore, the suggestion unit can analyze the driver's driving habits and propose long-term improvements. For example, if a driver tends to brake suddenly frequently, the suggestion unit can analyze the cause and provide advice to improve driving skills. This allows the suggestion unit to quickly propose appropriate countermeasures to the driver and prevent dangerous driving behaviors. In addition, the suggestion unit can collect driver feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion unit to provide drivers with optimal countermeasures and improve the safety of driving behaviors.

[0070] The reporting unit reports the proposed measures to the police. For example, the reporting unit reports details and video data of dangerous driving behavior to the police. Specifically, the reporting unit automatically transmits data on dangerous driving behavior detected by the analysis unit to the police and reports the situation in real time. This allows the police to respond quickly and deter dangerous driving behavior. The reporting unit can also generate and provide detailed reports, including video data, to the police. This allows the police to take appropriate action based on concrete evidence. Furthermore, the reporting unit has a function to automatically notify the police in emergencies, allowing drivers to receive prompt assistance when they find themselves in a dangerous situation. This enables the reporting unit to quickly report dangerous driving behavior to the police and prompt appropriate action.

[0071] The data storage unit accumulates data on dangerous driving behaviors detected by the analysis unit and uses it to improve future driving behavior. For example, the data storage unit can analyze drivers' driving habits and suggest areas for improvement. Specifically, the data storage unit stores past driving data over a long period and analyzes drivers' driving patterns and tendencies in detail. This allows it to identify areas for improvement in drivers' driving skills and habits and provide specific advice. The data storage unit can also compare and analyze data from multiple drivers to extract common problems and areas for improvement. This helps to improve the driving skills of drivers as a whole. Furthermore, the data storage unit can generate statistical information and reports based on driving data and provide them to drivers and managers. This allows drivers to objectively evaluate their own driving behavior and set specific goals for improvement. Through these functions, the data storage unit can support the improvement of drivers' driving behavior and contribute to the overall improvement of traffic safety.

[0072] The suggestion unit can propose measures to issue warnings to the driver. For example, the suggestion unit can issue an audible warning. For example, the suggestion unit can issue a visual warning. For example, the suggestion unit can issue a vibration warning. By issuing warnings to the driver, dangerous driving behaviors can be prevented. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can propose measures to issue warnings to the driver based on dangerous driving behaviors detected by a generative AI.

[0073] The proposal unit can propose measures to automatically control the vehicle's speed. For example, the proposal unit can propose measures to reduce the vehicle's speed. For example, the proposal unit can propose measures to keep the vehicle's speed constant. For example, the proposal unit can propose measures to accelerate the vehicle's speed. By automatically controlling the vehicle's speed, dangerous driving behavior can be suppressed. Some or all of the above-described processes in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can propose measures to automatically control the vehicle's speed based on dangerous driving behavior detected by a generative AI.

[0074] The reporting unit can report details and video data of dangerous driving behavior to the police. For example, the reporting unit can report details such as the type of dangerous driving behavior, the time it occurred, and location information to the police. For example, the reporting unit can report video data of dangerous driving behavior to the police. For example, the reporting unit can report audio data of dangerous driving behavior to the police. This allows for a swift response by reporting to the police. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can report details and video data of dangerous driving behavior detected by a generating AI to the police.

[0075] The data storage unit can analyze the driver's driving habits and suggest areas for improvement. For example, the data storage unit can analyze the driver's driving frequency, driving time, driving style, etc. For example, based on the driver's driving habits, the data storage unit can suggest areas for improvement in driving skills and safe driving. For example, based on the driver's driving history, the data storage unit can analyze patterns of driving behavior and suggest areas for improvement. In this way, by analyzing the driver's driving habits and suggesting areas for improvement, it can be used to improve future driving behavior. Some or all of the above processing in the data storage unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data storage unit can suggest areas for improvement based on the driver's driving habits analyzed by a generative AI.

[0076] The acquisition unit can estimate the driver's emotions and adjust the timing of video data acquisition based on the estimated emotions. For example, if the driver is tense, the acquisition unit can acquire video data frequently to keep a detailed record. For example, if the driver is relaxed, the acquisition unit can acquire video data at normal acquisition intervals. For example, if the driver is angry, the acquisition unit can acquire video data at shorter intervals because sudden movements and abnormal driving behaviors are more likely to occur. By adjusting the timing of video data acquisition according to the driver's emotions, more appropriate data can be obtained. 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 acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the driver's emotion data into the generative AI and have the generative AI adjust the timing of video data acquisition.

[0077] The acquisition unit can analyze past driving data and select the optimal method for acquiring video data. For example, if the acquisition unit can determine from past driving data that dangerous driving behaviors frequently occur during a specific time period, it can focus on acquiring video data during that time period. For example, the acquisition unit can adjust the frequency of video data acquisition on specific roads or in specific areas based on past driving data. For example, the acquisition unit can analyze past driving data and optimize the video data acquisition method according to the driver's driving pattern. This enables efficient data acquisition by selecting the optimal video data acquisition method based on past driving data. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past driving data into a generating AI and have the generating AI select the optimal video data acquisition method.

[0078] The acquisition unit can filter video data based on the driver's current driving status and traffic conditions. For example, if the driver is driving on a highway, the acquisition unit can prioritize video data acquisition based on following distance and speed. For example, if the driver is driving in an urban area, the acquisition unit can prioritize video data acquisition based on traffic signals and pedestrian movements. For example, if the driver is stuck in traffic, the acquisition unit can prioritize video data acquisition based on the movements of surrounding vehicles. This allows for the priority acquisition of important data by filtering according to driving and traffic conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the driver's current driving status and traffic conditions into a generating AI and have the generating AI perform the filtering.

[0079] The acquisition unit can estimate the driver's emotions and determine the priority of video data to acquire based on the estimated emotions. For example, if the driver is tense, the acquisition unit can prioritize acquiring video data of the road ahead. For example, if the driver is relaxed, the acquisition unit can apply the normal video data acquisition method. For example, if the driver is angry, the acquisition unit can prioritize acquiring video data of the movements of surrounding vehicles. In this way, important data can be acquired preferentially by determining the priority of video data 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 acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the driver's emotion data into the generative AI and have the generative AI perform the determination of the priority of video data.

[0080] The acquisition unit can prioritize the acquisition of highly relevant data by considering the driver's geographical location information when acquiring video data. For example, if the driver is in a specific hazardous area, the acquisition unit can prioritize the acquisition of video data from that area. For example, if the driver is at a specific intersection, the acquisition unit can prioritize the acquisition of video data from that intersection. For example, if the driver is on a specific road, the acquisition unit can prioritize the acquisition of video data from that road. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the driver's geographical location information into a generating AI and cause the generating AI to prioritize the acquisition of highly relevant data.

[0081] The acquisition unit can analyze the driver's social media activity when acquiring video data and acquire relevant data. For example, if the driver mentions a specific location on social media, the acquisition unit can prioritize acquiring video data of that location. For example, if the driver mentions a specific event on social media, the acquisition unit can prioritize acquiring video data of that event. For example, if the driver mentions a specific road on social media, the acquisition unit can prioritize acquiring video data of that road. In this way, relevant data can be prioritized by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the driver's social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0082] The analysis unit can estimate the driver's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the driver is tense, the analysis unit can perform the analysis using strict criteria to detect dangerous driving behaviors early. For example, if the driver is relaxed, the analysis unit can perform the analysis using normal criteria. For example, if the driver is angry, the analysis unit can focus on sudden movements and abnormal driving behaviors. By adjusting the analysis criteria 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 generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input driver emotion data into a generative AI and have the generative AI adjust the analysis criteria.

[0083] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of video data during the analysis. For example, the analysis unit can simultaneously analyze front and rear video data to detect dangerous driving behaviors. For example, the analysis unit can simultaneously analyze left and right video data to detect dangerous driving behaviors during lane changes. For example, the analysis unit can integrate and analyze video data from multiple cameras to detect dangerous driving behaviors in all directions. This improves the accuracy of the analysis by considering the interrelationships of the video data. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input video data from multiple cameras into a generation AI and have the generation AI perform an analysis that considers the interrelationships.

[0084] The analysis unit can perform analysis while considering the driver's attribute information. For example, the analysis unit can adjust the detection criteria for dangerous driving behavior by considering the driver's age and gender. For example, the analysis unit can improve the accuracy of the analysis by considering the driver's driving experience. For example, the analysis unit can improve the accuracy of detecting dangerous driving behavior by considering the driver's past driving history. In this way, the accuracy of the analysis is improved by considering the driver's attribute information. 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 attribute information into a generating AI and have the generating AI perform the analysis.

[0085] The analysis unit can estimate the driver's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the driver is tense, the analysis unit can display the most important analysis results first. For example, if the driver is relaxed, the analysis unit can display the analysis results in the normal order. For example, if the driver is angry, the analysis unit can prioritize displaying analysis results related to sudden movements or abnormal driving behaviors. This allows important information to be displayed preferentially by adjusting the display order of the analysis results according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input driver emotion data into a generative AI and have the generative AI adjust the display order of the analysis results.

[0086] The analysis unit can perform analysis while considering the geographical distribution of video data. For example, the analysis unit can focus its analysis on dangerous driving behavior in a specific area. For example, the analysis unit can focus its analysis on dangerous driving behavior on a specific road. For example, the analysis unit can focus its analysis on dangerous driving behavior at a specific intersection. By considering the geographical distribution, it is possible to focus the analysis on dangerous driving behavior in specific areas or on specific roads. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the geographical distribution of video data into a generative AI and have the generative AI perform the analysis.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest traffic safety research. For example, the analysis unit can improve the accuracy of its analysis by referring to past traffic accident data. For example, the analysis unit can improve the accuracy of its analysis by referring to the driving data of other drivers. Thus, the accuracy of the analysis is improved by referring to relevant literature. 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 relevant literature into a generating AI and have the generating AI perform the analysis.

[0088] The suggestion unit can estimate the driver's emotions and adjust the way it expresses countermeasures based on the estimated emotions. For example, if the driver is tense, the suggestion unit can propose countermeasures in calm language. For example, if the driver is relaxed, the suggestion unit can propose countermeasures in normal language. For example, if the driver is angry, the suggestion unit can propose countermeasures in quick and clear language. By adjusting the way the countermeasures are expressed according to the driver's emotions, more effective countermeasures can be proposed. 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 suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input driver emotion data into a generative AI and have the generative AI adjust the way the countermeasures are expressed.

[0089] The proposal unit can adjust the level of detail of countermeasures based on the importance of the dangerous driving behavior when making a proposal. For example, the proposal unit can propose detailed countermeasures for serious dangerous behaviors. For example, the proposal unit can propose concise countermeasures for minor dangerous behaviors. For example, the proposal unit can propose countermeasures with a moderate level of detail for moderate dangerous behaviors. In this way, by adjusting the level of detail of countermeasures according to the importance of the dangerous driving behavior, appropriate countermeasures can be proposed. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can adjust the level of detail of countermeasures based on the importance of the dangerous driving behavior detected by the generative AI.

[0090] The proposal unit can apply different countermeasure algorithms depending on the category of driving behavior when making a proposal. For example, the proposal unit can apply a countermeasure algorithm specifically for lane changes to dangerous behaviors related to lane changes. For example, the proposal unit can apply a countermeasure algorithm specifically for following distance to dangerous behaviors related to following distance. For example, the proposal unit can apply a countermeasure algorithm specifically for speeding to dangerous behaviors related to speeding. By applying a countermeasure algorithm according to the category of driving behavior, more effective countermeasures can be proposed. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can apply a countermeasure algorithm based on the category of driving behavior detected by a generative AI.

[0091] The suggestion unit can estimate the driver's emotions and adjust the length of the suggested response based on the estimated emotions. For example, if the driver is tense, the suggestion unit can suggest a short, concise response. If the driver is relaxed, the suggestion unit can suggest a response that includes a detailed explanation. If the driver is angry, the suggestion unit can suggest a quick and concise response. By adjusting the length of the response according to the driver's emotions, more effective responses can be suggested. 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 processing in the suggestion unit may be performed using a generative AI or not. For example, the suggestion unit can input driver emotion data into a generative AI and have the generative AI adjust the length of the response.

[0092] The proposal unit can determine the priority of countermeasures based on when the dangerous driving behavior occurred. For example, the proposal unit can propose countermeasures with the highest priority for dangerous behavior that occurred recently. For example, the proposal unit can propose countermeasures with the normal priority for dangerous behavior that occurred in the past. For example, the proposal unit can propose preventative measures for dangerous behavior that may occur in the future. In this way, appropriate countermeasures can be proposed by determining the priority of countermeasures according to when the dangerous driving behavior occurred. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can determine the priority of countermeasures based on when the dangerous driving behavior detected by the generative AI occurred.

[0093] The suggestion unit can adjust the order of countermeasures based on the relationships between driving behaviors when making suggestions. For example, if lane changes and dangerous behaviors such as maintaining a safe following distance are related, the suggestion unit can suggest both countermeasures consecutively. For example, if speeding and sudden braking are related, the suggestion unit can suggest both countermeasures consecutively. For example, if running a red light and dangerous behavior at an intersection are related, the suggestion unit can suggest both countermeasures consecutively. By adjusting the order of countermeasures based on the relationships between driving behaviors, more effective countermeasures can be proposed. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can adjust the order of countermeasures based on the relationships between driving behaviors detected by a generative AI.

[0094] The reporting unit can estimate the driver's emotions and adjust the way the report is expressed based on the estimated emotions. For example, if the driver is tense, the reporting unit can generate a calm and detailed report. For example, if the driver is relaxed, the reporting unit can generate a normal report. For example, if the driver is angry, the reporting unit can generate a quick and concise report. This allows for more appropriate reporting by adjusting the way the report is expressed 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 processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input driver emotion data into a generative AI and have the generative AI adjust the way the report is expressed.

[0095] The reporting unit can adjust the content of the report based on the level of detail of the dangerous driving behavior at the time of reporting. For example, the reporting unit can generate detailed reports for serious dangerous behaviors. For example, the reporting unit can generate concise reports for minor dangerous behaviors. For example, the reporting unit can generate reports with a moderate level of detail for moderate dangerous behaviors. This allows for appropriate reporting by adjusting the content of the report according to the level of detail of the dangerous driving behavior. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can adjust the content of the report based on the level of detail of the dangerous driving behavior detected by the generating AI.

[0096] The notification unit can estimate the driver's emotions and determine the priority of notifications based on the estimated emotions. For example, if the driver is tense, the notification unit can prioritize the most important notifications. For example, if the driver is relaxed, the notification unit can make notifications with normal priority. For example, if the driver is angry, the notification unit can make notifications quickly. This allows important notifications to be prioritized by determining the priority of notifications 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input driver emotion data into a generative AI and have the generative AI perform the determination of notification priority.

[0097] The reporting unit can adjust the content of the report based on the location where the dangerous driving behavior occurred. For example, for dangerous behavior occurring in a specific area, the reporting unit can generate a report that includes detailed information about that area. For example, for dangerous behavior occurring on a specific road, the reporting unit can generate a report that includes detailed information about that road. For example, for dangerous behavior occurring at a specific intersection, the reporting unit can generate a report that includes detailed information about that intersection. By adjusting the report content based on the location of occurrence, it becomes possible to provide reports with more detailed information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can adjust the content of the report based on the location of the dangerous driving behavior detected by the generating AI.

[0098] The data storage unit can estimate the driver's emotions and adjust the data storage method based on the estimated emotions. For example, if the driver is tense, the data storage unit can store detailed data. For example, if the driver is relaxed, the data storage unit can apply the normal data storage method. For example, if the driver is angry, the data storage unit can store data at short intervals. This allows for more appropriate data storage by adjusting the data storage 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. 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 data storage unit may be performed using a generative AI, or not using a generative AI. For example, the data storage unit can input the driver's emotion data into a generative AI and have the generative AI adjust the data storage method.

[0099] The data storage unit can optimize its data storage algorithm by referring to past driving data during storage. For example, the data storage unit can concentrate data storage during specific time periods based on past driving data. For example, the data storage unit can adjust the data storage frequency for specific roads or areas based on past driving data. For example, the data storage unit can analyze past driving data and optimize the data storage method according to the driver's driving pattern. This allows the data storage algorithm to be optimized by referring to past driving data. Some or all of the above processing in the data storage unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data storage unit can input past driving data into a generation AI and have the generation AI perform the optimization of the data storage algorithm.

[0100] The data storage unit can store data while considering the driver's attribute information. The data storage unit can adjust the data storage method, for example, by considering the driver's age and gender. The data storage unit can optimize the data storage method, for example, by considering the driver's driving experience. The data storage unit can adjust the data storage method, for example, by considering the driver's past driving history. This improves the accuracy of data storage by considering the driver's attribute information. Some or all of the above processing in the data storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data storage unit can input the driver's attribute information into a generating AI and have the generating AI perform the adjustment of the data storage method.

[0101] The data storage unit can estimate the driver's emotions and adjust the data display method based on the estimated emotions. For example, if the driver is tense, the data storage unit can provide a simple and highly visible display method. For example, if the driver is relaxed, the data storage unit can provide a display method that includes detailed information. For example, if the driver is angry, the data storage unit can provide a quick and concise display method. This allows for a more visually appealing display by adjusting the data display method according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data storage unit may be performed using a generative AI, or not. For example, the data storage unit can input driver emotion data into a generative AI and have the generative AI adjust the data display method.

[0102] The data storage unit can store data while considering the geographical distribution of driving behavior. For example, the data storage unit can focus on storing driving behavior data in a specific area. For example, the data storage unit can focus on storing driving behavior data on a specific road. For example, the data storage unit can focus on storing driving behavior data at a specific intersection. In this way, by considering geographical distribution, driving behavior data in a specific area or on a specific road can be focused on storing. Some or all of the above processing in the data storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data storage unit can input geographical distribution data of driving behavior into a generating AI and have the generating AI perform data storage.

[0103] The storage unit can improve the accuracy of data storage by referring to relevant literature during storage. For example, the storage unit can improve the accuracy of data storage by referring to the latest traffic safety research. For example, the storage unit can improve the accuracy of data storage by referring to past traffic accident data. For example, the storage unit can improve the accuracy of data storage by referring to driving data of other drivers. In this way, the accuracy of data storage is improved by referring to relevant literature. Some or all of the above processing in the storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the storage unit can input relevant literature into a generating AI and have the generating AI perform the improvement of data storage accuracy.

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

[0105] The suggestion unit can estimate the driver's emotions and adjust the way countermeasures are expressed based on the estimated emotions. For example, if the driver is tense, countermeasures can be proposed in calm language. If the driver is relaxed, countermeasures can be proposed in normal language. If the driver is angry, countermeasures can be proposed in quick and clear language. By adjusting the way countermeasures are expressed according to the driver's emotions, more effective countermeasures can be proposed. 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 suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input driver emotion data into a generative AI and have the generative AI adjust the way countermeasures are expressed.

[0106] The acquisition unit can estimate the driver's emotions and adjust the timing of video data acquisition based on the estimated emotions. For example, if the driver is tense, video data can be acquired frequently to create a detailed record. If the driver is relaxed, video data can be acquired at normal intervals. If the driver is angry, sudden movements and abnormal driving behaviors are more likely to occur, so video data can be acquired at shorter intervals. By adjusting the timing of video data acquisition according to the driver's emotions, more appropriate data can be obtained. 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 these examples. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the driver's emotion data into the generative AI and have the generative AI adjust the timing of video data acquisition.

[0107] The analysis unit can estimate the driver's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the driver is tense, the analysis can be performed using stricter criteria to detect dangerous driving behaviors early. If the driver is relaxed, the analysis can be performed using normal criteria. If the driver is angry, the analysis can be performed with emphasis on sudden movements and abnormal driving behaviors. By adjusting the analysis criteria according to the driver's emotions, more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input driver emotion data into a generative AI and have the generative AI adjust the analysis criteria.

[0108] The reporting unit can estimate the driver's emotions and adjust the way the report is expressed based on the estimated emotions. For example, if the driver is tense, it can generate a calm and detailed report. If the driver is relaxed, it can generate a normal report. If the driver is angry, it can generate a quick and concise report. By adjusting the way the report is expressed according to the driver's emotions, more appropriate reporting becomes possible. 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 processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input driver emotion data into a generative AI and have the generative AI adjust the way the report is expressed.

[0109] The data storage unit can estimate the driver's emotions and adjust the data storage method based on the estimated emotions. For example, if the driver is tense, detailed data can be stored. If the driver is relaxed, the normal data storage method can be applied. If the driver is angry, data can be stored at short intervals. This allows for more appropriate data storage by adjusting the data storage method 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 processing in the data storage unit may be performed using a generative AI, or not. For example, the data storage unit can input the driver's emotion data into a generative AI and have the generative AI adjust the data storage method.

[0110] The acquisition unit can analyze past driving data and select the optimal method for acquiring video data. For example, if past driving data shows that dangerous driving behaviors frequently occur during a specific time period, the unit can focus on acquiring video data during that time period. Based on past driving data, the unit can adjust the frequency of video data acquisition on specific roads or in specific areas. By analyzing past driving data, the unit can optimize the video data acquisition method according to the driver's driving pattern. This enables efficient data acquisition by selecting the optimal video data acquisition method based on past driving data. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past driving data into a generating AI and have the generating AI select the optimal video data acquisition method.

[0111] The acquisition unit can filter video data based on the driver's current driving status and traffic conditions. For example, if the driver is driving on a highway, the acquisition unit can prioritize video data acquisition based on following distance and speed. If the driver is driving in an urban area, the acquisition unit can prioritize video data acquisition based on traffic signals and pedestrian movements. If the driver is stuck in traffic, the acquisition unit can prioritize video data acquisition based on the movements of surrounding vehicles. This allows for the priority acquisition of important data by filtering according to driving and traffic conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the driver's current driving status and traffic conditions into a generating AI and have the generating AI perform the filtering.

[0112] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of video data during the analysis process. For example, it can simultaneously analyze front and rear video data to detect dangerous driving behaviors. It can simultaneously analyze left and right video data to detect dangerous driving behaviors during lane changes. It can integrate and analyze video data from multiple cameras to detect dangerous driving behaviors in all directions. This improves the accuracy of the analysis by considering the interrelationships of the video data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input video data from multiple cameras into a generation AI and have the generation AI perform an analysis that considers the interrelationships.

[0113] The proposal unit can adjust the level of detail of countermeasures based on the importance of the dangerous driving behavior when making a proposal. For example, it can propose detailed countermeasures for serious dangerous behaviors, concise countermeasures for minor dangerous behaviors, and countermeasures with a moderate level of detail for moderately dangerous behaviors. In this way, by adjusting the level of detail of countermeasures according to the importance of the dangerous driving behavior, appropriate countermeasures can be proposed. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can adjust the level of detail of countermeasures based on the importance of the dangerous driving behavior detected by the generative AI.

[0114] The data storage unit can optimize its data storage algorithm by referring to past driving data during storage. For example, it can focus on storing data during specific time periods based on past driving data. It can adjust the data storage frequency for specific roads or regions based on past driving data. It can analyze past driving data and optimize the data storage method according to the driver's driving pattern. This allows the data storage algorithm to be optimized by referring to past driving data. Some or all of the above-described processes in the data storage unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data storage unit can input past driving data into a generation AI and have the generation AI perform the optimization of the data storage algorithm.

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

[0116] Step 1: The acquisition unit acquires video data from the dashcam in real time. For example, it can acquire video data from a camera mounted on the vehicle and stream it in real time. It can also send the video data to a cloud server and make it accessible from a remote location. Step 2: The analysis unit analyzes the video data acquired by the acquisition unit to detect dangerous driving behaviors. For example, it uses a generation AI to detect dangerous driving behaviors such as sudden lane changes or dangerously close following distances. It can also evaluate dangerous driving behaviors based on past driving data and traffic rules. Step 3: The proposal unit proposes the optimal countermeasures based on the dangerous driving behavior detected by the analysis unit. For example, it can propose measures such as issuing a warning to the driver or automatically controlling the vehicle's speed. Step 4: The reporting department reports the proposed measures to the police. For example, they report details of dangerous driving behavior and video data to the police. Step 5: The storage unit stores data on dangerous driving behaviors detected by the analysis unit and uses it to improve future driving behavior. For example, it can analyze the driver's driving habits and suggest areas for improvement.

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

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

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

[0120] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, notification unit, and storage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires video data from the drive recorder in real time using the camera 42 of the smart device 14. The analysis unit analyzes the acquired video data by the specific processing unit 290 of the data processing unit 12 and detects dangerous driving behavior. The proposal unit proposes the optimal countermeasure by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the police of the countermeasure proposed by the specific processing unit 290 of the data processing unit 12. The storage unit stores data on dangerous driving behavior detected by the specific processing unit 290 of the data processing unit 12, which can be used to improve future driving behavior. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0125] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

[0136] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, notification unit, and storage unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires video data from a drive recorder in real time using the camera 42 of the smart glasses 214. The analysis unit analyzes the acquired video data by the specific processing unit 290 of the data processing unit 12 and detects dangerous driving behavior. The proposal unit proposes the optimal countermeasure by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the police of the countermeasure proposed by the specific processing unit 290 of the data processing unit 12. The storage unit stores data on dangerous driving behavior detected by the specific processing unit 290 of the data processing unit 12, which can be used to improve future driving behavior. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0141] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

[0152] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, notification unit, and storage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires video data from the drive recorder in real time using the camera 42 of the headset terminal 314. The analysis unit analyzes the acquired video data by the specific processing unit 290 of the data processing unit 12 and detects dangerous driving behavior. The proposal unit proposes the optimal countermeasure by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the police of the countermeasure proposed by the specific processing unit 290 of the data processing unit 12. The storage unit stores data on dangerous driving behavior detected by the specific processing unit 290 of the data processing unit 12, which can be used to improve future driving behavior. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

[0157] The microphone 238 receives voice 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.

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

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

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

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

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

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

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

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

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

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

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

[0169] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, notification unit, and storage unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires video data from a drive recorder in real time using the camera 42 of the robot 414. The analysis unit analyzes the acquired video data by the specific processing unit 290 of the data processing unit 12 and detects dangerous driving behavior. The proposal unit proposes the optimal countermeasure by the specific processing unit 290 of the data processing unit 12. The notification unit notifies the police of the countermeasure proposed by the specific processing unit 290 of the data processing unit 12. The storage unit stores data on dangerous driving behavior detected by the specific processing unit 290 of the data processing unit 12, which can be used to improve future driving behavior. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0188] (Note 1) An acquisition unit that acquires video data from the dashcam in real time, An analysis unit analyzes the video data acquired by the acquisition unit and detects dangerous driving behaviors, The analysis unit proposes the optimal countermeasures based on the dangerous driving behavior detected by the analysis unit, The reporting department will report the measures proposed by the aforementioned proposal department to the police, The system includes a storage unit that stores data on dangerous driving behaviors detected by the analysis unit and uses this data to improve future driving behaviors. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose measures to issue warnings to drivers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose a measure to automatically control the vehicle's speed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting unit, Report details of dangerous driving behavior and video data to the police. The system described in Appendix 1, characterized by the features described herein. (Note 5) The storage unit is We analyze the driver's driving habits and suggest areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the driver's emotions and adjusts the timing of video data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze past driving data and select the optimal method for acquiring video data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring video data, filtering is performed based on the driver's current driving status and traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the driver's emotions and prioritizes the video data to be acquired based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring video data, the system prioritizes the acquisition of highly relevant data, taking into account the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring video data, the driver's social media activity is analyzed and relevant data is obtained. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts the analysis criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the interrelationships between video data are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the driver's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts the display order of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the geographical distribution of the video data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, The system estimates the driver's emotions and adjusts the way countermeasures are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail of the countermeasures based on the importance of the dangerous driving behavior. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different countermeasure algorithms are applied depending on the category of driving behavior. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, The system estimates the driver's emotions and adjusts the length of the countermeasures based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, prioritize countermeasures based on when the dangerous driving behavior occurred. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the order of countermeasures based on the relevance of driving behaviors. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, The system estimates the driver's emotions and adjusts the wording of the report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, When reporting a dangerous driving behavior, the content of the report will be adjusted based on the level of detail of the dangerous driving behavior. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, The system estimates the driver's emotions and determines the priority of reporting based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, When reporting, adjust the report content based on the location where the dangerous driving behavior occurred. The system described in Appendix 1, characterized by the features described herein. (Note 28) The storage unit is We estimate the driver's emotions and adjust the data collection method based on the estimated driver's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The storage unit is During data storage, the data storage algorithm is optimized by referring to past operating data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The storage unit is When accumulating data, the system takes into account the driver's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The storage unit is The system estimates the driver's emotions and adjusts how the data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The storage unit is When accumulating data, the geographical distribution of driving behavior is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 33) The storage unit is During data storage, we improve the accuracy of data storage by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. An acquisition unit that acquires video data from the dashcam in real time, An analysis unit analyzes the video data acquired by the acquisition unit and detects dangerous driving behaviors, The analysis unit proposes the optimal countermeasures based on the dangerous driving behavior detected by the analysis unit, The reporting department will report the measures proposed by the aforementioned proposal department to the police, The system includes a storage unit that stores data on dangerous driving behaviors detected by the analysis unit and uses this data to improve future driving behaviors. A system characterized by the following features.

2. The aforementioned proposal section is, We propose measures to issue warnings to drivers. The system according to feature 1.

3. The aforementioned proposal section is, We propose a measure to automatically control the vehicle's speed. The system according to feature 1.

4. The aforementioned reporting unit, Report details of dangerous driving behavior and video data to the police. The system according to feature 1.

5. The storage unit is We analyze the driver's driving habits and suggest areas for improvement. The system according to feature 1.

6. The acquisition unit is, The system estimates the driver's emotions and adjusts the timing of video data acquisition based on the estimated emotions. The system according to feature 1.

7. The acquisition unit is, Analyze past driving data and select the optimal method for acquiring video data. The system according to feature 1.

8. The acquisition unit is, When acquiring video data, filtering is performed based on the driver's current driving status and traffic conditions. The system according to feature 1.

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