system

Abstract

The system according to this embodiment aims to analyze driving data and provide customized feedback in real time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a support unit. The collection unit collects operating data. The analysis unit analyzes the operating data collected by the collection unit. The generation unit generates feedback based on the operating data analyzed by the analysis unit. The support unit provides the feedback generated by the generation unit in real time.

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, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, real-time customized feedback based on driving data is not provided, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze driving data and provide customized feedback in real time.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, and a support unit. The data collection unit collects operating data. The analysis unit analyzes the operating data collected by the data collection unit. The generation unit generates feedback based on the operating data analyzed by the analysis unit. The support unit provides the feedback generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can analyze operating data and provide customized feedback in real time. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable 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 reception 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 reception 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 driving data analysis system according to an embodiment of the present invention is a system that uses generative AI to analyze driving data and provides customized feedback based on individual driving patterns. This driving data analysis system allows drivers to receive tips for safe driving and improve their driving skills and fuel efficiency. For example, the driving data analysis system collects driving data. Driving data includes speed, acceleration, frequency of brake use, steering wheel operation, etc. This data is obtained from sensors and GPS installed in the vehicle. Next, the driving data analysis system uses generative AI to analyze the collected driving data. The generative AI analyzes the driving data in detail and identifies the driver's driving patterns. For example, it analyzes the frequency of sudden braking and the tendency for sudden acceleration. Based on the analysis results, the driving data analysis system uses generative AI to generate feedback that is optimal for each individual driver. The feedback includes tips for safe driving and advice on improving fuel efficiency. For example, specific advice such as "reducing sudden braking will improve fuel efficiency" or "maintaining a constant speed will result in safer driving" is provided. Furthermore, the driving data analysis system uses generative AI to provide real-time support while driving. If the driver brakes suddenly while driving, the generative AI immediately provides feedback that can be used to improve future driving. This system allows drivers to objectively evaluate and improve their driving style. Furthermore, receiving tips for safe driving is expected to reduce accident rates. In addition, efficient driving instruction will lead to improved fuel efficiency. Thus, by utilizing AI-generated driving data analysis and providing customized feedback, drivers' safe driving and driving skills will be improved. This allows the driving data analysis system to objectively evaluate and improve drivers' driving style. Furthermore, receiving tips for safe driving is expected to reduce accident rates. In addition, efficient driving instruction will lead to improved fuel efficiency.

[0029] The driving data analysis system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a support unit. The collection unit collects driving data. Driving data includes, but is not limited to, speed, frequency of brake use, and acceleration. The collection unit acquires driving data from, for example, sensors or GPS mounted on the vehicle. Sensors include, for example, acceleration sensors, gyro sensors, and temperature sensors. GPS acquires driving data considering the frequency and accuracy range of location information updates. The analysis unit analyzes the driving data collected by the collection unit. The analysis unit analyzes, for example, the frequency of sudden braking and the tendency of sudden acceleration. Specific definitions and criteria for sudden braking include deceleration thresholds and brake pedal operation times. Specific definitions and criteria for sudden acceleration include acceleration thresholds and accelerator pedal operation times. The generation unit generates feedback based on the driving data analyzed by the analysis unit. The generation unit generates, for example, hints for safe driving and advice for improving fuel efficiency. Hints for safe driving include, for example, adhering to speed limits and maintaining a safe following distance. Advice for improving fuel efficiency includes, for example, recommendations for eco-driving and the use of idle stop. The support unit provides feedback generated by the generation unit in real time. The support unit provides immediate feedback, for example, when sudden braking occurs while driving. Specific definitions and criteria for real time include the acceptable delay time before feedback is provided and the update frequency. This enables the driving data analysis system according to the embodiment to collect, analyze, generate, and provide driving data in real time.

[0030] The data collection unit collects driving data. This driving data includes, but is not limited to, speed, brake usage frequency, and acceleration. The data collection unit acquires driving data from, for example, sensors and GPS mounted on the vehicle. Sensors include, for example, acceleration sensors, gyroscopes, and temperature sensors. These sensors are placed in various parts of the vehicle and collect various data in real time during driving. The acceleration sensor measures the degree of acceleration and deceleration of the vehicle and identifies situations of sudden acceleration and deceleration. The gyroscope detects the rotation and tilt of the vehicle and records the driving conditions of sudden changes of direction and curves. The temperature sensor monitors the temperature of the engine and brakes and detects abnormal temperature increases. GPS acquires driving data considering the frequency and accuracy range of location information updates. GPS records the vehicle's current position and travel path with high accuracy and is also used to measure speed and distance traveled. This allows the data collection unit to understand the vehicle's driving conditions in detail and manage driving data centrally. Furthermore, the data collection unit can transmit this data to a cloud server and link with other systems and departments. For example, the collected data can be made accessible to the analysis and generation units, enabling real-time data sharing. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the driving data collected by the data collection unit. For example, the analysis unit analyzes the frequency of sudden braking and the tendency of sudden acceleration. Specific definitions and criteria for sudden braking include deceleration thresholds and brake pedal operation times. Specific definitions and criteria for sudden acceleration include acceleration thresholds and accelerator pedal operation times. Based on these criteria, the analysis unit analyzes the driving data in detail and identifies patterns of driving behavior. For example, if sudden braking is frequent, it can be determined that the driver may not be maintaining an appropriate distance from the vehicle in front. Similarly, if a tendency for sudden acceleration is observed, it can be determined that the driver may be wasting fuel. Based on these analysis results, the analysis unit evaluates the driver's driving style and risk factors. Furthermore, the analysis unit uses AI to process the data in real time and detect and predict anomalies in driving behavior. For example, the AI ​​learns from past driving data and detects behavior that deviates from normal driving patterns. The AI ​​can also analyze trends in driving data and predict future risks. This allows the analysis unit to quickly and accurately analyze driving data and provide information to improve driver safety and efficiency. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term driving behavior trends and assess risks. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term driving management and risk assessment, improving the reliability and safety of the entire system.

[0032] The generation unit generates feedback based on driving data analyzed by the analysis unit. For example, the generation unit generates tips for safe driving and advice for improving fuel efficiency. Tips for safe driving include, for example, adhering to speed limits and maintaining a safe following distance. Advice for improving fuel efficiency includes, for example, recommending eco-driving and utilizing idle stop. The generation unit customizes this feedback according to the driver's driving style and circumstances. For example, drivers who frequently brake suddenly will be given specific advice on how to use the brakes and maintain a safe following distance. Drivers who tend to accelerate suddenly will be given advice on how to accelerate smoothly and practice eco-driving. The generation unit uses AI to analyze driving data and identify driver behavior patterns and risk factors. This allows the generation unit to provide optimal feedback to drivers and support safe driving and improved fuel efficiency. Furthermore, the generation unit supports long-term improvement of driving behavior and risk management based on trends and historical data of driving data. For example, it monitors changes in the driver's driving style and the degree of improvement, and provides continuous feedback. Furthermore, the generation unit can collect driver feedback and continuously improve the accuracy and effectiveness of the feedback. This allows the generation unit to provide effective feedback to drivers, promoting safer driving and improved fuel efficiency.

[0033] The support unit provides feedback generated by the generation unit in real time. For example, the support unit provides immediate feedback if the driver applies the brakes suddenly while driving. The specific definition and criteria of real time include the acceptable delay time before feedback is provided and the update frequency. The support unit monitors the driver's driving situation in real time and provides appropriate feedback at the necessary time. For example, immediately after applying the brakes suddenly, it provides advice on how to use the brakes and maintain a safe distance from other vehicles. Also, if it detects sudden acceleration, it provides advice on how to accelerate smoothly and practice eco-driving. The support unit notifies the driver of this feedback on their device and alerts them through voice guidance and vibration notifications. This allows the driver to receive feedback in real time and immediately improve their driving behavior. Furthermore, the support unit can monitor the driver's reactions to the feedback and changes in their behavior, and continuously improve the accuracy and effectiveness of the feedback. For example, if the driver improves their driving behavior in accordance with the feedback, the support unit evaluates the effect and provides further advice. The support unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the support department to provide drivers with quick and reliable feedback, supporting safe driving and improved fuel efficiency.

[0034] The data collection unit can acquire driving data from sensors and GPS mounted on the vehicle. For example, the data collection unit can acquire acceleration data during driving using an acceleration sensor mounted on the vehicle. The data collection unit can also acquire tilt and rotation data of the vehicle during driving using a gyro sensor mounted on the vehicle. Furthermore, the data collection unit can acquire temperature data inside and outside the vehicle during driving using a temperature sensor mounted on the vehicle. This enables accurate data collection by acquiring driving data from sensors and GPS mounted on the vehicle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from sensors mounted on the vehicle into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze the frequency of sudden braking and the tendency of sudden acceleration. For example, the analysis unit analyzes the frequency of sudden braking from driving data. Specific definitions and criteria for sudden braking include deceleration thresholds and brake pedal operation times. The analysis unit can also analyze the tendency of sudden acceleration from driving data. Specific definitions and criteria for sudden acceleration include acceleration thresholds and accelerator pedal operation times. Furthermore, the analysis unit can combine data on sudden braking and sudden acceleration to comprehensively analyze the driver's driving pattern. This makes it possible to identify driving patterns by analyzing the frequency of sudden braking and the tendency of sudden acceleration. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input driving data into a generative AI and have the generative AI perform the analysis of the frequency of sudden braking and the tendency of sudden acceleration.

[0036] The generation unit can generate tips for safe driving and advice for improving fuel efficiency. For example, the generation unit generates tips for safe driving based on driving data. These tips include, for example, adhering to speed limits and maintaining a safe following distance. The generation unit also generates advice for improving fuel efficiency based on driving data. This advice includes, for example, recommending eco-driving and utilizing idle stop. Furthermore, the generation unit can also generate customized feedback tailored to the driver's driving style based on driving data. This enables the driver to improve their driving skills by generating tips for safe driving and advice for improving fuel efficiency. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input driving data into a generation AI and have the generation AI generate tips for safe driving and advice for improving fuel efficiency.

[0037] The support unit can provide immediate feedback when sudden braking occurs while driving. For example, the support unit can provide feedback using an audio alert when sudden braking occurs while driving. The audio alert may include specific instructions such as, "Reduce sudden braking." The support unit can also provide feedback using a visual indicator when sudden braking occurs while driving. The visual indicator may include, for example, the illumination of a warning light or the display of a message on the screen. Furthermore, the support unit can also provide feedback using a text message when sudden braking occurs while driving. The text message may include specific advice such as, "Reducing sudden braking will improve fuel efficiency." This enables real-time driving support by providing immediate feedback when sudden braking occurs while driving. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input sudden braking data into a generating AI and have the generating AI generate the feedback.

[0038] The data collection unit can analyze past driving data and select the optimal data collection method. For example, the data collection unit can analyze driving patterns in a specific time period from past driving data and select the optimal data collection method for that time period. For example, the data collection unit can also analyze driving patterns in specific road conditions from past driving data and select the optimal data collection method for those conditions. Furthermore, the data collection unit can analyze driving patterns in specific weather conditions from past driving data and select the optimal data collection method for those conditions. By analyzing past driving data and selecting the optimal data collection method, efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past driving data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter the collected driving data based on the driver's current driving status and road conditions. For example, if the driver is driving on a highway, the data collection unit will prioritize collecting speed and acceleration data. For example, if the driver is driving in an urban area, the data collection unit can also prioritize collecting brake usage frequency and steering operation data. Furthermore, if the driver is driving in bad weather, the data collection unit can also prioritize collecting visibility and road surface condition data. This allows for efficient collection of necessary data by filtering based on the driver's current driving status and road conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input driving status and road condition data into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the driver's geographical location information when collecting driving data. For example, if a driver frequently drives in a particular area, the data collection unit can prioritize the collection of driving data for that area. For example, if a driver frequently uses a particular route, the data collection unit can also prioritize the collection of driving data for that route. Furthermore, if a driver drives in a particular area during a particular time period, the data collection unit can also prioritize the collection of driving data for that time period. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the driver's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0041] The data collection unit can analyze the driver's social media activity and collect relevant data when collecting driving data. For example, the data collection unit can collect relevant driving data based on the driving experiences the driver has shared on social media. The data collection unit can also collect relevant data by referring to the driving data of other drivers the driver follows on social media. Furthermore, the data collection unit can collect driving data for a route based on the driving route the driver has shared on social media. This enables more multifaceted data collection by analyzing the driver's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the driving data during the analysis. For example, the analysis unit can perform a detailed analysis on important driving data. For example, the analysis unit can perform a simplified analysis on driving data of low importance. Furthermore, the analysis unit can apply multiple analysis methods to highly important driving data to provide detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the driving data. 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 driving data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of driving data during analysis. For example, the analysis unit can apply a speed fluctuation analysis algorithm to speed data. For example, the analysis unit can also apply a brake usage frequency analysis algorithm to brake data. Furthermore, the analysis unit can apply an algorithm to analyze steering stability to steering operation data. By applying different analysis algorithms depending on the category of driving data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input driving data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of operation data collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected operation data. The analysis unit may also analyze current operation data by referring to past operation data. Furthermore, the analysis unit may prioritize the analysis of operation data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the timing of operation data collection. Some or all of the above-described processes 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 timing of operation data collection into the generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the driving data during the analysis. For example, the analysis unit can prioritize the analysis of important driving data. The analysis unit can also group highly relevant driving data together for analysis. Furthermore, the analysis unit can postpone the analysis of less relevant driving data. This allows for prioritization of important data by adjusting the order of analysis based on the relevance of the driving data. Some or all of the above-described processes 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 relevance of the driving data into the generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The generation unit can adjust the level of detail of the feedback based on the importance of the driving data when generating feedback. For example, the generation unit can provide detailed feedback for important driving data. For example, the generation unit can also provide simplified feedback for less important driving data. Furthermore, the generation unit can provide multiple feedback options for highly important driving data. This allows for appropriate feedback to be provided for important data by adjusting the level of detail of the feedback based on the importance of the driving data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input driving data into a generation AI and have the generation AI perform the adjustment of the level of detail of the feedback.

[0047] The generation unit can apply different feedback algorithms depending on the category of driving data when generating feedback. For example, the generation unit can provide feedback regarding speed fluctuations for speed data. For example, the generation unit can also provide feedback regarding brake usage frequency for brake data. Furthermore, the generation unit can provide feedback regarding steering stability for steering operation data. By applying different feedback algorithms depending on the category of driving data, more accurate feedback can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input driving data into a generation AI and have the generation AI execute the application of the feedback algorithm.

[0048] The generation unit can determine the priority of feedback based on the timing of driving data collection when generating feedback. For example, the generation unit can provide feedback based on recently collected driving data. The generation unit can also provide feedback based on current driving data, for example, by referring to past driving data. Furthermore, the generation unit can also provide feedback based on driving data collected over a specific period. This allows for efficient feedback by determining the priority of feedback based on the timing of driving data collection. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the timing of driving data collection to the generation AI and have the generation AI determine the priority of feedback.

[0049] The generation unit can adjust the order of feedback based on the relevance of the driving data when generating feedback. For example, the generation unit can prioritize providing feedback based on important driving data. The generation unit can also group and provide feedback based on highly relevant driving data. Furthermore, the generation unit can postpone providing feedback based on less relevant driving data. This allows for appropriate feedback to be provided for important data by adjusting the order of feedback based on the relevance of the driving data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the driving data into the generation AI and have the generation AI perform the adjustment of the feedback order.

[0050] The support unit can provide optimal feedback by referring to the driver's past driving history when providing real-time feedback. For example, if the driver has frequently used sudden braking in the past, the support unit can provide feedback to reduce sudden braking. For example, if the driver has frequently used sudden acceleration in the past, the support unit can also provide feedback to reduce sudden acceleration. Furthermore, if the driver has had difficulty maintaining a constant speed in the past, the support unit can provide feedback to maintain a constant speed. This enables more effective driving support by providing optimal feedback by referring to the driver's past driving history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input past driving history into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0051] The support unit can customize the content of real-time feedback based on the driver's current driving situation. For example, if the driver is driving on a highway, the support unit can provide tips for safe driving on highways. For example, if the driver is driving in an urban area, the support unit can also provide tips for safe driving in urban areas. Furthermore, if the driver is driving in bad weather, the support unit can provide points to be aware of when driving in bad weather. This allows for more appropriate driving support by customizing the content of feedback based on the driver's current driving situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input data on the current driving situation into a generating AI and have the generating AI customize the content of the feedback.

[0052] The support unit can provide optimal feedback by considering the driver's geographical location when providing real-time feedback. For example, if the driver is driving in a specific area, the support unit can provide feedback regarding driving in that area. For example, if the driver is driving on a specific route, the support unit can also provide feedback regarding that route. Furthermore, if the driver is driving in a specific area at a specific time of day, the support unit can also provide feedback regarding driving during that time period. This enables more appropriate driving support by providing optimal feedback while considering the driver's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0053] The support unit can analyze the driver's social media activity and adjust the content of the feedback when providing real-time feedback. For example, the support unit can provide relevant feedback based on the driver's driving experiences shared on social media. The support unit can also provide relevant feedback by referring to the driving data of other drivers that the driver follows on social media. Furthermore, the support unit can provide feedback on a driving route based on the driving route shared by the driver on social media. This enables more multifaceted driving support by analyzing the driver's social media activity and adjusting the content of the feedback. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input social media activity data into a generating AI and have the generating AI adjust the content of the feedback.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The driving data analysis system can adjust the vehicle's performance during operation based on the driver's driving style. For example, if the driver frequently uses rapid acceleration, the engine output can be limited. If the driver frequently uses sudden braking, the brake sensitivity can be adjusted. Furthermore, if the driver prioritizes fuel efficiency, eco mode can be automatically activated. This allows for adjustment of vehicle performance according to the driver's driving style, improving both safety and fuel efficiency.

[0056] The driving data analysis system can adjust tire pressure during driving based on the driver's driving style. For example, if the driver is driving on a highway, the tire pressure can be increased. If the driver is driving in an urban area, the tire pressure can be decreased. Furthermore, if the driver is driving in bad weather, the tire pressure can be appropriately adjusted. This allows for tire pressure adjustments according to the driver's driving style and road conditions, leading to improved safety and fuel efficiency.

[0057] The driving data analysis system can automatically open and close the windows while driving, based on the driver's driving style. For example, if the driver is relaxed, the windows can be opened to let in natural air. If the driver is driving on a highway, the windows can be closed to reduce wind noise. Furthermore, if the driver is driving in bad weather, the windows can be automatically closed. This allows for window opening and closing according to the driver's driving style and road conditions, improving driving comfort.

[0058] The driving data analysis system can automatically adjust the mirror angle while driving based on the driver's driving style. For example, if the driver is driving on a highway, the mirror angle can be adjusted to a wide angle. If the driver is driving in an urban area, the mirror angle can be adjusted to a narrow angle. Furthermore, when the driver is parking, the mirror angle can be adjusted to a position suitable for parking. This allows for adjustment of the mirror angle according to the driver's driving style and road conditions, improving safety.

[0059] The driving data analysis system can automatically adjust the wiper operation while driving based on the driver's driving style. For example, if the driver is driving on a highway, the wiper speed can be increased. If the driver is driving in an urban area, the wiper speed can be decreased. Furthermore, if the driver is driving in adverse weather conditions, the wiper operation can be appropriately adjusted. This allows for adjustment of wiper operation according to the driver's driving style and road conditions, improving safety.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects driving data. This driving data includes speed, brake usage frequency, and acceleration. The data collection unit acquires driving data from sensors and GPS mounted on the vehicle. Sensors include accelerometers, gyroscopes, and temperature sensors. GPS acquires driving data considering the frequency and accuracy of location information updates. Step 2: The analysis unit analyzes the driving data collected by the data collection unit. The analysis unit analyzes the frequency of sudden braking and the tendency of sudden acceleration. Specific definitions and criteria for sudden braking include deceleration thresholds and brake pedal operation times. Specific definitions and criteria for sudden acceleration include acceleration thresholds and accelerator pedal operation times. Step 3: The generation unit generates feedback based on the driving data analyzed by the analysis unit. The generation unit generates tips for safe driving and advice for improving fuel efficiency. Tips for safe driving include adhering to speed limits and maintaining a safe following distance. Advice for improving fuel efficiency includes recommendations for eco-driving and utilizing idle stop. Step 4: The support unit provides feedback generated by the generation unit in real time. The support unit provides immediate feedback in the event of sudden braking during driving. The specific definition and criteria of real time include the acceptable delay time before feedback is provided and the update frequency.

[0062] (Example of form 2) The driving data analysis system according to an embodiment of the present invention is a system that uses generative AI to analyze driving data and provides customized feedback based on individual driving patterns. This driving data analysis system allows drivers to receive tips for safe driving and improve their driving skills and fuel efficiency. For example, the driving data analysis system collects driving data. Driving data includes speed, acceleration, frequency of brake use, steering wheel operation, etc. This data is obtained from sensors and GPS installed in the vehicle. Next, the driving data analysis system uses generative AI to analyze the collected driving data. The generative AI analyzes the driving data in detail and identifies the driver's driving patterns. For example, it analyzes the frequency of sudden braking and the tendency for sudden acceleration. Based on the analysis results, the driving data analysis system uses generative AI to generate feedback that is optimal for each individual driver. The feedback includes tips for safe driving and advice on improving fuel efficiency. For example, specific advice such as "reducing sudden braking will improve fuel efficiency" or "maintaining a constant speed will result in safer driving" is provided. Furthermore, the driving data analysis system uses generative AI to provide real-time support while driving. If the driver brakes suddenly while driving, the generative AI immediately provides feedback that can be used to improve future driving. This system allows drivers to objectively evaluate and improve their driving style. Furthermore, receiving tips for safe driving is expected to reduce accident rates. In addition, efficient driving instruction will lead to improved fuel efficiency. Thus, by utilizing AI-generated driving data analysis and providing customized feedback, drivers' safe driving and driving skills will be improved. This allows the driving data analysis system to objectively evaluate and improve drivers' driving style. Furthermore, receiving tips for safe driving is expected to reduce accident rates. In addition, efficient driving instruction will lead to improved fuel efficiency.

[0063] The driving data analysis system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a support unit. The collection unit collects driving data. Driving data includes, but is not limited to, speed, frequency of brake use, and acceleration. The collection unit acquires driving data from, for example, sensors or GPS mounted on the vehicle. Sensors include, for example, acceleration sensors, gyro sensors, and temperature sensors. GPS acquires driving data considering the frequency and accuracy range of location information updates. The analysis unit analyzes the driving data collected by the collection unit. The analysis unit analyzes, for example, the frequency of sudden braking and the tendency of sudden acceleration. Specific definitions and criteria for sudden braking include deceleration thresholds and brake pedal operation times. Specific definitions and criteria for sudden acceleration include acceleration thresholds and accelerator pedal operation times. The generation unit generates feedback based on the driving data analyzed by the analysis unit. The generation unit generates, for example, hints for safe driving and advice for improving fuel efficiency. Hints for safe driving include, for example, adhering to speed limits and maintaining a safe following distance. Advice for improving fuel efficiency includes, for example, recommendations for eco-driving and the use of idle stop. The support unit provides feedback generated by the generation unit in real time. The support unit provides immediate feedback, for example, when sudden braking occurs while driving. Specific definitions and criteria for real time include the acceptable delay time before feedback is provided and the update frequency. This enables the driving data analysis system according to the embodiment to collect, analyze, generate, and provide driving data in real time.

[0064] The data collection unit collects driving data. This driving data includes, but is not limited to, speed, brake usage frequency, and acceleration. The data collection unit acquires driving data from, for example, sensors and GPS mounted on the vehicle. Sensors include, for example, acceleration sensors, gyroscopes, and temperature sensors. These sensors are placed in various parts of the vehicle and collect various data in real time during driving. The acceleration sensor measures the degree of acceleration and deceleration of the vehicle and identifies situations of sudden acceleration and deceleration. The gyroscope detects the rotation and tilt of the vehicle and records the driving conditions of sudden changes of direction and curves. The temperature sensor monitors the temperature of the engine and brakes and detects abnormal temperature increases. GPS acquires driving data considering the frequency and accuracy range of location information updates. GPS records the vehicle's current position and travel path with high accuracy and is also used to measure speed and distance traveled. This allows the data collection unit to understand the vehicle's driving conditions in detail and manage driving data centrally. Furthermore, the data collection unit can transmit this data to a cloud server and link with other systems and departments. For example, the collected data can be made accessible to the analysis and generation units, enabling real-time data sharing. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes the driving data collected by the data collection unit. For example, the analysis unit analyzes the frequency of sudden braking and the tendency of sudden acceleration. Specific definitions and criteria for sudden braking include deceleration thresholds and brake pedal operation times. Specific definitions and criteria for sudden acceleration include acceleration thresholds and accelerator pedal operation times. Based on these criteria, the analysis unit analyzes the driving data in detail and identifies patterns of driving behavior. For example, if sudden braking is frequent, it can be determined that the driver may not be maintaining an appropriate distance from the vehicle in front. Similarly, if a tendency for sudden acceleration is observed, it can be determined that the driver may be wasting fuel. Based on these analysis results, the analysis unit evaluates the driver's driving style and risk factors. Furthermore, the analysis unit uses AI to process the data in real time and detect and predict anomalies in driving behavior. For example, the AI ​​learns from past driving data and detects behavior that deviates from normal driving patterns. The AI ​​can also analyze trends in driving data and predict future risks. This allows the analysis unit to quickly and accurately analyze driving data and provide information to improve driver safety and efficiency. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term driving behavior trends and assess risks. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term driving management and risk assessment, improving the reliability and safety of the entire system.

[0066] The generation unit generates feedback based on driving data analyzed by the analysis unit. For example, the generation unit generates tips for safe driving and advice for improving fuel efficiency. Tips for safe driving include, for example, adhering to speed limits and maintaining a safe following distance. Advice for improving fuel efficiency includes, for example, recommending eco-driving and utilizing idle stop. The generation unit customizes this feedback according to the driver's driving style and circumstances. For example, drivers who frequently brake suddenly will be given specific advice on how to use the brakes and maintain a safe following distance. Drivers who tend to accelerate suddenly will be given advice on how to accelerate smoothly and practice eco-driving. The generation unit uses AI to analyze driving data and identify driver behavior patterns and risk factors. This allows the generation unit to provide optimal feedback to drivers and support safe driving and improved fuel efficiency. Furthermore, the generation unit supports long-term improvement of driving behavior and risk management based on trends and historical data of driving data. For example, it monitors changes in the driver's driving style and the degree of improvement, and provides continuous feedback. Furthermore, the generation unit can collect driver feedback and continuously improve the accuracy and effectiveness of the feedback. This allows the generation unit to provide effective feedback to drivers, promoting safer driving and improved fuel efficiency.

[0067] The support unit provides feedback generated by the generation unit in real time. For example, the support unit provides immediate feedback if the driver applies the brakes suddenly while driving. The specific definition and criteria of real time include the acceptable delay time before feedback is provided and the update frequency. The support unit monitors the driver's driving situation in real time and provides appropriate feedback at the necessary time. For example, immediately after applying the brakes suddenly, it provides advice on how to use the brakes and maintain a safe distance from other vehicles. Also, if it detects sudden acceleration, it provides advice on how to accelerate smoothly and practice eco-driving. The support unit notifies the driver of this feedback on their device and alerts them through voice guidance and vibration notifications. This allows the driver to receive feedback in real time and immediately improve their driving behavior. Furthermore, the support unit can monitor the driver's reactions to the feedback and changes in their behavior, and continuously improve the accuracy and effectiveness of the feedback. For example, if the driver improves their driving behavior in accordance with the feedback, the support unit evaluates the effect and provides further advice. The support unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the support department to provide drivers with quick and reliable feedback, supporting safe driving and improved fuel efficiency.

[0068] The data collection unit can acquire driving data from sensors and GPS mounted on the vehicle. For example, the data collection unit can acquire acceleration data during driving using an acceleration sensor mounted on the vehicle. The data collection unit can also acquire tilt and rotation data of the vehicle during driving using a gyro sensor mounted on the vehicle. Furthermore, the data collection unit can acquire temperature data inside and outside the vehicle during driving using a temperature sensor mounted on the vehicle. This enables accurate data collection by acquiring driving data from sensors and GPS mounted on the vehicle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from sensors mounted on the vehicle into a generating AI and have the generating AI perform data analysis.

[0069] The analysis unit can analyze the frequency of sudden braking and the tendency of sudden acceleration. For example, the analysis unit analyzes the frequency of sudden braking from driving data. Specific definitions and criteria for sudden braking include deceleration thresholds and brake pedal operation times. The analysis unit can also analyze the tendency of sudden acceleration from driving data. Specific definitions and criteria for sudden acceleration include acceleration thresholds and accelerator pedal operation times. Furthermore, the analysis unit can combine data on sudden braking and sudden acceleration to comprehensively analyze the driver's driving pattern. This makes it possible to identify driving patterns by analyzing the frequency of sudden braking and the tendency of sudden acceleration. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input driving data into a generative AI and have the generative AI perform the analysis of the frequency of sudden braking and the tendency of sudden acceleration.

[0070] The generation unit can generate tips for safe driving and advice for improving fuel efficiency. For example, the generation unit generates tips for safe driving based on driving data. These tips include, for example, adhering to speed limits and maintaining a safe following distance. The generation unit also generates advice for improving fuel efficiency based on driving data. This advice includes, for example, recommending eco-driving and utilizing idle stop. Furthermore, the generation unit can also generate customized feedback tailored to the driver's driving style based on driving data. This enables the driver to improve their driving skills by generating tips for safe driving and advice for improving fuel efficiency. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input driving data into a generation AI and have the generation AI generate tips for safe driving and advice for improving fuel efficiency.

[0071] The support unit can provide immediate feedback when sudden braking occurs while driving. For example, the support unit can provide feedback using an audio alert when sudden braking occurs while driving. The audio alert may include specific instructions such as, "Reduce sudden braking." The support unit can also provide feedback using a visual indicator when sudden braking occurs while driving. The visual indicator may include, for example, the illumination of a warning light or the display of a message on the screen. Furthermore, the support unit can also provide feedback using a text message when sudden braking occurs while driving. The text message may include specific advice such as, "Reducing sudden braking will improve fuel efficiency." This enables real-time driving support by providing immediate feedback when sudden braking occurs while driving. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input sudden braking data into a generating AI and have the generating AI generate the feedback.

[0072] The data collection unit can estimate the driver's emotions and adjust the timing of driving data collection based on the estimated emotions. For example, if the driver is stressed, the data collection unit can increase the frequency of driving data collection and collect more detailed data. For example, if the driver is relaxed, the data collection unit can also decrease the frequency of driving data collection and collect only the minimum necessary data. Furthermore, if the driver is tired, the data collection unit can adjust the timing of driving data collection and also collect data during breaks. This allows for more appropriate data collection by adjusting the timing of driving data collection based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, 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 collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the driver's emotion data into the generative AI and have the generative AI adjust the timing of driving data collection.

[0073] The data collection unit can analyze past driving data and select the optimal data collection method. For example, the data collection unit can analyze driving patterns in a specific time period from past driving data and select the optimal data collection method for that time period. For example, the data collection unit can also analyze driving patterns in specific road conditions from past driving data and select the optimal data collection method for those conditions. Furthermore, the data collection unit can analyze driving patterns in specific weather conditions from past driving data and select the optimal data collection method for those conditions. By analyzing past driving data and selecting the optimal data collection method, efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past driving data into a generating AI and have the generating AI select the optimal data collection method.

[0074] The data collection unit can filter the collected driving data based on the driver's current driving status and road conditions. For example, if the driver is driving on a highway, the data collection unit will prioritize collecting speed and acceleration data. For example, if the driver is driving in an urban area, the data collection unit can also prioritize collecting brake usage frequency and steering operation data. Furthermore, if the driver is driving in bad weather, the data collection unit can also prioritize collecting visibility and road surface condition data. This allows for efficient collection of necessary data by filtering based on the driver's current driving status and road conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input driving status and road condition data into a generating AI and have the generating AI perform the filtering.

[0075] The data collection unit can estimate the driver's emotions and determine the priority of driving data to collect based on the estimated emotions. For example, if the driver is stressed, the data collection unit may prioritize collecting data on brake usage frequency and sudden acceleration. For example, if the driver is relaxed, the data collection unit may prioritize collecting data on speed and steering. Furthermore, if the driver is tired, the data collection unit may prioritize collecting data on rest periods and attention levels while driving. This allows for the priority collection of important data by determining the priority of driving data to collect based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, 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 collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the driver's emotion data into a generative AI and have the generative AI determine the priority of driving data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the driver's geographical location information when collecting driving data. For example, if a driver frequently drives in a particular area, the data collection unit can prioritize the collection of driving data for that area. For example, if a driver frequently uses a particular route, the data collection unit can also prioritize the collection of driving data for that route. Furthermore, if a driver drives in a particular area during a particular time period, the data collection unit can also prioritize the collection of driving data for that time period. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the driver's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0077] The data collection unit can analyze the driver's social media activity and collect relevant data when collecting driving data. For example, the data collection unit can collect relevant driving data based on the driving experiences the driver has shared on social media. The data collection unit can also collect relevant data by referring to the driving data of other drivers the driver follows on social media. Furthermore, the data collection unit can collect driving data for a route based on the driving route the driver has shared on social media. This enables more multifaceted data collection by analyzing the driver's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0078] The analysis unit can estimate the driver's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the driver is stressed, the analysis unit can provide the analysis results in a simple and easy-to-understand format. For example, if the driver is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the driver is tired, the analysis unit can provide the analysis results in a concise manner. This allows for more easily understandable analysis results by adjusting the presentation of the analysis based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the driver's emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the driving data during the analysis. For example, the analysis unit can perform a detailed analysis on important driving data. For example, the analysis unit can perform a simplified analysis on driving data of low importance. Furthermore, the analysis unit can apply multiple analysis methods to highly important driving data to provide detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the driving data. 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 driving data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of driving data during analysis. For example, the analysis unit can apply a speed fluctuation analysis algorithm to speed data. For example, the analysis unit can also apply a brake usage frequency analysis algorithm to brake data. Furthermore, the analysis unit can apply an algorithm to analyze steering stability to steering operation data. By applying different analysis algorithms depending on the category of driving data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input driving data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0081] The analysis unit can estimate the driver's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the driver is in a hurry, the analysis unit can provide a short, concise analysis. If the driver is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the driver is excited, the analysis unit can provide the analysis in a visually easy-to-understand format. By adjusting the length of the analysis based on the driver's emotions, the analysis unit can provide analysis results tailored to the driver's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the driver's emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the timing of operation data collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected operation data. The analysis unit may also analyze current operation data by referring to past operation data. Furthermore, the analysis unit may prioritize the analysis of operation data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the timing of operation data collection. Some or all of the above-described processes 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 timing of operation data collection into the generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the driving data during the analysis. For example, the analysis unit can prioritize the analysis of important driving data. The analysis unit can also group highly relevant driving data together for analysis. Furthermore, the analysis unit can postpone the analysis of less relevant driving data. This allows for prioritization of important data by adjusting the order of analysis based on the relevance of the driving data. Some or all of the above-described processes 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 relevance of the driving data into the generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The generation unit can estimate the driver's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if the driver is stressed, the generation unit can provide feedback in a simple and easy-to-understand format. If the driver is relaxed, the generation unit can also provide detailed feedback. Furthermore, if the driver is tired, the generation unit can provide feedback that is focused only on the essentials. This allows for more easily understood feedback by adjusting the way feedback is presented based on 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 generation unit may be performed using a generative AI, or not. For example, the generation unit can input driver emotion data into a generative AI and have the generative AI adjust the way feedback is presented.

[0085] The generation unit can adjust the level of detail of the feedback based on the importance of the driving data when generating feedback. For example, the generation unit can provide detailed feedback for important driving data. For example, the generation unit can also provide simplified feedback for less important driving data. Furthermore, the generation unit can provide multiple feedback options for highly important driving data. This allows for appropriate feedback to be provided for important data by adjusting the level of detail of the feedback based on the importance of the driving data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input driving data into a generation AI and have the generation AI perform the adjustment of the level of detail of the feedback.

[0086] The generation unit can apply different feedback algorithms depending on the category of driving data when generating feedback. For example, the generation unit can provide feedback regarding speed fluctuations for speed data. For example, the generation unit can also provide feedback regarding brake usage frequency for brake data. Furthermore, the generation unit can provide feedback regarding steering stability for steering operation data. By applying different feedback algorithms depending on the category of driving data, more accurate feedback can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input driving data into a generation AI and have the generation AI execute the application of the feedback algorithm.

[0087] The generation unit can estimate the driver's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the driver is in a hurry, the generation unit can provide short, concise feedback. If the driver is relaxed, the generation unit can also provide detailed feedback. Furthermore, if the driver is excited, the generation unit can provide feedback in a visually easy-to-understand format. By adjusting the length of the feedback based on the driver's emotions, feedback tailored to the driver's situation can be provided. 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 generation unit may be performed using a generative AI, or not. For example, the generation unit can input driver emotion data into a generative AI and have the generative AI adjust the length of the feedback.

[0088] The generation unit can determine the priority of feedback based on the timing of driving data collection when generating feedback. For example, the generation unit can provide feedback based on recently collected driving data. The generation unit can also provide feedback based on current driving data, for example, by referring to past driving data. Furthermore, the generation unit can also provide feedback based on driving data collected over a specific period. This allows for efficient feedback by determining the priority of feedback based on the timing of driving data collection. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the timing of driving data collection to the generation AI and have the generation AI determine the priority of feedback.

[0089] The generation unit can adjust the order of feedback based on the relevance of the driving data when generating feedback. For example, the generation unit can prioritize providing feedback based on important driving data. The generation unit can also group and provide feedback based on highly relevant driving data. Furthermore, the generation unit can postpone providing feedback based on less relevant driving data. This allows for appropriate feedback to be provided for important data by adjusting the order of feedback based on the relevance of the driving data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the driving data into the generation AI and have the generation AI perform the adjustment of the feedback order.

[0090] The support unit can estimate the driver's emotions and adjust how real-time feedback is provided based on the estimated emotions. For example, if the driver is stressed, the support unit can provide feedback in a calm tone. If the driver is relaxed, the support unit can also provide detailed feedback. Furthermore, if the driver is in a hurry, the support unit can provide concise and quick feedback. This allows for more appropriate feedback to be provided by adjusting how real-time feedback is provided based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input driver emotion data into a generative AI and have the generative AI adjust how real-time feedback is provided.

[0091] The support unit can provide optimal feedback by referring to the driver's past driving history when providing real-time feedback. For example, if the driver has frequently used sudden braking in the past, the support unit can provide feedback to reduce sudden braking. For example, if the driver has frequently used sudden acceleration in the past, the support unit can also provide feedback to reduce sudden acceleration. Furthermore, if the driver has had difficulty maintaining a constant speed in the past, the support unit can provide feedback to maintain a constant speed. This enables more effective driving support by providing optimal feedback by referring to the driver's past driving history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input past driving history into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0092] The support unit can customize the content of real-time feedback based on the driver's current driving situation. For example, if the driver is driving on a highway, the support unit can provide tips for safe driving on highways. For example, if the driver is driving in an urban area, the support unit can also provide tips for safe driving in urban areas. Furthermore, if the driver is driving in bad weather, the support unit can provide points to be aware of when driving in bad weather. This allows for more appropriate driving support by customizing the content of feedback based on the driver's current driving situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input data on the current driving situation into a generating AI and have the generating AI customize the content of the feedback.

[0093] The support unit can estimate the driver's emotions and prioritize real-time feedback based on the estimated emotions. For example, if the driver is stressed, the support unit will prioritize important feedback. For example, if the driver is relaxed, the support unit may also provide detailed feedback. Furthermore, if the driver is in a hurry, the support unit may prioritize concise and important feedback. This ensures that important feedback is prioritized by prioritizing real-time feedback based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input driver emotion data into a generative AI and have the generative AI determine the priority of real-time feedback.

[0094] The support unit can provide optimal feedback by considering the driver's geographical location when providing real-time feedback. For example, if the driver is driving in a specific area, the support unit can provide feedback regarding driving in that area. For example, if the driver is driving on a specific route, the support unit can also provide feedback regarding that route. Furthermore, if the driver is driving in a specific area at a specific time of day, the support unit can also provide feedback regarding driving during that time period. This enables more appropriate driving support by providing optimal feedback while considering the driver's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0095] The support unit can analyze the driver's social media activity and adjust the content of the feedback when providing real-time feedback. For example, the support unit can provide relevant feedback based on the driver's driving experiences shared on social media. The support unit can also provide relevant feedback by referring to the driving data of other drivers that the driver follows on social media. Furthermore, the support unit can provide feedback on a driving route based on the driving route shared by the driver on social media. This enables more multifaceted driving support by analyzing the driver's social media activity and adjusting the content of the feedback. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input social media activity data into a generating AI and have the generating AI adjust the content of the feedback.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The driving data analysis system can suggest music and entertainment for drivers based on their driving style. For example, if the driver is relaxed, it can suggest relaxing music. If the driver is stressed, it can suggest music or podcasts to reduce stress. Furthermore, if the driver is driving for a long time, it can suggest entertainment to reduce fatigue. This allows for entertainment suggestions tailored to the driver's emotions and driving situation, improving driving comfort.

[0098] The driving data analysis system can adjust the seat while driving based on the driver's driving style. For example, if the driver is driving for a long time, the seat angle and cushion firmness can be automatically adjusted. If the driver is tired, the seat's massage function can be activated. Furthermore, if the driver is relaxed, the seat's heater or cooler can be adjusted. This allows for seat adjustments tailored to the driver's driving style and emotions, improving driving comfort.

[0099] The driving data analysis system can suggest navigation routes based on the driver's driving style. For example, if the driver is in a hurry, it can suggest the shortest route. If the driver is relaxed, it can suggest a scenic route. Furthermore, if the driver is stressed, it can suggest a route with less traffic. This allows for navigation suggestions tailored to the driver's driving style and emotions, improving driving comfort.

[0100] The driving data analysis system can adjust the air conditioning while driving based on the driver's driving style. For example, if the driver is feeling hot, the air conditioner temperature can be lowered. If the driver is feeling cold, the heater temperature can be increased. Furthermore, if the driver is relaxed, the air conditioning fan speed can be adjusted. This allows for air conditioning adjustments that are tailored to the driver's driving style and emotions, improving driving comfort.

[0101] The driving data analysis system can adjust the lighting while driving based on the driver's driving style. For example, if the driver is driving at night, the interior lights can be dimmed. If the driver is relaxed, the lighting color can be changed to a warmer tone. Furthermore, if the driver is concentrating, the brightness of the lights can be adjusted. This allows for lighting adjustments that match the driver's driving style and emotions, improving driving comfort.

[0102] The driving data analysis system can adjust the vehicle's performance during operation based on the driver's driving style. For example, if the driver frequently uses rapid acceleration, the engine output can be limited. If the driver frequently uses sudden braking, the brake sensitivity can be adjusted. Furthermore, if the driver prioritizes fuel efficiency, eco mode can be automatically activated. This allows for adjustment of vehicle performance according to the driver's driving style, improving both safety and fuel efficiency.

[0103] The driving data analysis system can adjust tire pressure during driving based on the driver's driving style. For example, if the driver is driving on a highway, the tire pressure can be increased. If the driver is driving in an urban area, the tire pressure can be decreased. Furthermore, if the driver is driving in bad weather, the tire pressure can be appropriately adjusted. This allows for tire pressure adjustments according to the driver's driving style and road conditions, leading to improved safety and fuel efficiency.

[0104] The driving data analysis system can automatically open and close the windows while driving, based on the driver's driving style. For example, if the driver is relaxed, the windows can be opened to let in natural air. If the driver is driving on a highway, the windows can be closed to reduce wind noise. Furthermore, if the driver is driving in bad weather, the windows can be automatically closed. This allows for window opening and closing according to the driver's driving style and road conditions, improving driving comfort.

[0105] The driving data analysis system can automatically adjust the mirror angle while driving based on the driver's driving style. For example, if the driver is driving on a highway, the mirror angle can be adjusted to a wide angle. If the driver is driving in an urban area, the mirror angle can be adjusted to a narrow angle. Furthermore, when the driver is parking, the mirror angle can be adjusted to a position suitable for parking. This allows for adjustment of the mirror angle according to the driver's driving style and road conditions, improving safety.

[0106] The driving data analysis system can automatically adjust the wiper operation while driving based on the driver's driving style. For example, if the driver is driving on a highway, the wiper speed can be increased. If the driver is driving in an urban area, the wiper speed can be decreased. Furthermore, if the driver is driving in adverse weather conditions, the wiper operation can be appropriately adjusted. This allows for adjustment of wiper operation according to the driver's driving style and road conditions, improving safety.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects driving data. This driving data includes speed, brake usage frequency, and acceleration. The data collection unit acquires driving data from sensors and GPS mounted on the vehicle. Sensors include accelerometers, gyroscopes, and temperature sensors. GPS acquires driving data considering the frequency and accuracy of location information updates. Step 2: The analysis unit analyzes the driving data collected by the data collection unit. The analysis unit analyzes the frequency of sudden braking and the tendency of sudden acceleration. Specific definitions and criteria for sudden braking include deceleration thresholds and brake pedal operation times. Specific definitions and criteria for sudden acceleration include acceleration thresholds and accelerator pedal operation times. Step 3: The generation unit generates feedback based on the driving data analyzed by the analysis unit. The generation unit generates tips for safe driving and advice for improving fuel efficiency. Tips for safe driving include adhering to speed limits and maintaining a safe following distance. Advice for improving fuel efficiency includes recommendations for eco-driving and utilizing idle stop. Step 4: The support unit provides feedback generated by the generation unit in real time. The support unit provides immediate feedback in the event of sudden braking during driving. The specific definition and criteria of real time include the acceptable delay time before feedback is provided and the update frequency.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit acquires driving data using the sensors and GPS of the smart device 14. The analysis unit analyzes the driving data using, for example, the specific processing unit 290 of the data processing unit 12. The generation unit generates feedback using, for example, the specific processing unit 290 of the data processing unit 12. The support unit provides real-time feedback using, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit acquires driving data using the sensors and GPS of the smart glasses 214. The analysis unit analyzes the driving data using, for example, the specific processing unit 290 of the data processing unit 12. The generation unit generates feedback using, for example, the specific processing unit 290 of the data processing unit 12. The support unit provides real-time feedback using, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and support unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the data collection unit acquires driving data using the sensors and GPS of the headset terminal 314. The analysis unit analyzes the driving data using, for example, the specific processing unit 290 of the data processing unit 12. The generation unit generates feedback using, for example, the specific processing unit 290 of the data processing unit 12. The support unit provides real-time feedback using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and support unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit acquires driving data using the robot 414's sensors and GPS. The analysis unit analyzes the driving data using, for example, the specific processing unit 290 of the data processing unit 12. The generation unit generates feedback using, for example, the specific processing unit 290 of the data processing unit 12. The support unit provides real-time feedback using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects driving data, An analysis unit analyzes the operating data collected by the aforementioned collection unit, A generation unit that generates feedback based on the operating data analyzed by the analysis unit, The system includes a support unit that provides feedback generated by the generation unit in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is Driving data is acquired from sensors and GPS installed in the vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the frequency of sudden braking and the tendency of sudden acceleration. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generates tips for safe driving and advice for improving fuel efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is Provides immediate feedback in case of sudden braking while driving. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the driver's emotions and adjusts the timing of driving data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past driving data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting driving data, filtering is performed based on the driver's current driving status and road conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the driver's emotions and prioritizes the driving data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting driving data, the system prioritizes collecting highly relevant data by considering the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting driving data, analyze the driver's social media activity and collect relevant data. 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 representation of the analysis 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 level of detail of the analysis is adjusted based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the driving data. 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 length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of the collection of operating data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the driver's emotions and adjusts the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating feedback, adjust the level of detail in the feedback based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating feedback, different feedback algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the driver's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating feedback, the priority of the feedback is determined based on when the driving data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating feedback, adjust the order of feedback based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is It estimates the driver's emotions and adjusts how real-time feedback is provided based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is When providing real-time feedback, the system refers to the driver's past driving history to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When providing real-time feedback, the content of the feedback is customized based on the driver's current driving situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is It estimates the driver's emotions and prioritizes real-time feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is When providing real-time feedback, the system takes the driver's geographical location into consideration to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing real-time feedback, we analyze the driver's social media activity to adjust the content of the feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects driving data, An analysis unit analyzes the operating data collected by the aforementioned collection unit, A generation unit that generates feedback based on the operating data analyzed by the analysis unit, The system includes a support unit that provides feedback generated by the generation unit in real time. A system characterized by the following features.

2. The aforementioned collection unit is Driving data is acquired from sensors and GPS installed in the vehicle. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the frequency of sudden braking and the tendency of sudden acceleration. The system according to feature 1.

4. The generating unit is Generates tips for safe driving and advice for improving fuel efficiency. The system according to feature 1.

5. The aforementioned support unit is Provides immediate feedback in case of sudden braking while driving. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze past driving data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting driving data, filtering is performed based on the driver's current driving status and road conditions. The system according to feature 1.

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