system

Abstract

The system according to this embodiment aims to improve safe driving skills by providing training tailored to the individual driver's weaknesses in specific situations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a feedback unit, and a support unit. The collection unit collects driving data. The analysis unit analyzes the data collected by the collection unit and identifies situations that the driver finds difficult. The generation unit generates training scenarios based on the situations identified by the analysis unit. The feedback unit provides real-time feedback based on the training scenarios generated by the generation unit. The support unit supports driving during disasters by combining real-time weather data and traffic information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 prior art, it is difficult to provide training according to individual difficult situations of drivers, and there is room for improvement in improving safe driving skills.

[0005] The system according to the embodiment aims to provide training according to individual difficult situations of drivers and improve safe driving skills.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a feedback unit, and a support unit. The collection unit collects driving data. The analysis unit analyzes the data collected by the collection unit and identifies situations that the driver finds difficult. The generation unit generates training scenarios based on the situations identified by the analysis unit. The feedback unit provides real-time feedback based on the training scenarios generated by the generation unit. The support unit supports driving during disasters by combining real-time weather data and traffic information. [Effects of the Invention]

[0007] The system according to this embodiment can provide training tailored to each driver's individual weaknesses in specific driving situations, thereby improving their safe driving skills. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 training system according to an embodiment of the present invention is a system for improving an individual's driving skills using generative AI. This driving training system collects the driver's driving data, and the generative AI analyzes this data to generate an optimal training scenario for each individual driver. Furthermore, it provides real-time feedback and immediately points out areas for improvement to the driver. It also supports driving during disasters by combining real-time weather data and traffic information. Finally, it supports the long-term skill improvement of the driver by accumulating driving history and performance data and providing regular reports. For example, the driving training system collects the driver's driving data. This data includes driving habits, past mistakes, reaction time, etc. Next, the generative AI analyzes this data and identifies situations in which the driver struggles. For example, for a driver who has difficulty parking, it generates a parking training scenario. Based on the generated training scenario, the driver performs a simulation. This simulation provides real-time feedback and immediately points out areas for improvement to the driver. For example, if the driver is late in changing lanes while driving, the driving training system immediately points this out and instructs the driver to change lanes at the appropriate time. Furthermore, the driving training system supports driving during disasters by combining real-time weather data and traffic information. For example, in the event of an earthquake or flood, the driver training system immediately suggests appropriate driving actions and evacuation routes, allowing drivers to evacuate safely. The system also measures drivers' reaction times and provides more appropriate guidance. For instance, it can instruct drivers on when to brake before a traffic light turns red, shortening reaction times and improving safe driving skills. Finally, the system accumulates individual driving history and performance data, providing regular reports. This allows drivers to track their progress and improve their skills over the long term. For example, monthly evaluations of driving skills allow drivers to see their own growth. In this way, the driver training system helps improve drivers' skills and supports safe driving.

[0029] The driving training system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, a feedback unit, and a support unit. The data collection unit collects driving data. For example, the data collection unit collects the driver's driving data. Driving data includes, but is not limited to, speed, frequency of brake use, and steering wheel operation. For example, the data collection unit collects driving data using sensors mounted on the vehicle. The data collection unit can also acquire data from the driver's smart device. For example, it collects GPS data and acceleration sensor data from a smartphone. Furthermore, the data collection unit can also accumulate the driver's driving history and analyze past driving data. The analysis unit analyzes the data collected by the data collection unit and identifies situations in which the driver has difficulty. For example, the analysis unit analyzes the data using a generation AI. The generation AI takes driving data as input and outputs situations in which the driver has difficulty. For example, the generation AI analyzes the driver's driving pattern and identifies situations in which the driver has difficulty, such as sharp curves and traffic jams. The generation unit generates training scenarios based on the situations identified by the analysis unit. The generation unit generates training scenarios using, for example, a generation AI. The generation AI takes a specified situation as input and outputs a training scenario. For example, the generation AI generates a parking training scenario for a driver who has difficulty parking. The feedback unit provides real-time feedback based on the training scenario generated by the generation unit. The feedback unit provides immediate feedback while driving, for example. The feedback unit points out areas for improvement to the driver using voice instructions and visual alerts. For example, if a lane change is delayed, the feedback unit immediately points this out and instructs the driver to change lanes at the appropriate time. The support unit supports driving during disasters by combining real-time weather data and traffic information. For example, the support unit suggests appropriate driving actions and evacuation routes when an earthquake or flood occurs. The support unit acquires data from weather sensors and traffic cameras and provides information to the driver in real time. For example, the support unit suggests a safe route to the driver based on weather data.As a result, the driving training system according to this embodiment can improve the driver's driving skills and support safe driving.

[0030] The data collection unit collects driving data. For example, the data collection unit collects driving data from the driver. Driving data includes, but is not limited to, speed, frequency of brake use, and steering wheel operation. The data collection unit collects driving data using, for example, sensors mounted on the vehicle. Specifically, the vehicle's speed sensor measures speed in real time, and the brake sensor records the frequency and intensity of brake pedal use. Regarding steering wheel operation, the steering sensor detects the rotation angle and speed of the steering wheel and collects this data. The data collection unit can also acquire data from the driver's smart device. For example, it can collect GPS data and accelerometer data from a smartphone. The smartphone's GPS data records the driver's location and travel route in detail, and the accelerometer detects sudden acceleration and deceleration. Furthermore, the data collection unit can accumulate the driver's driving history and analyze past driving data. This makes it possible to understand the driver's driving patterns and tendencies over the long term and provide training programs tailored to individual drivers. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to identify situations in which the driver struggles. For example, the analysis unit uses generative AI to analyze the data. The generative AI takes driving data as input and outputs situations in which the driver struggles. Specifically, the generative AI analyzes the driver's driving patterns and identifies situations in which the driver struggles, such as sharp curves and traffic jams. The generative AI uses machine learning algorithms to extract features from the driving data and model the driver's behavior patterns. For example, it analyzes data such as speed reduction on sharp curves and delays in steering to identify that the driver struggles with sharp curves. It also analyzes the frequency of brake use and acceleration timing in traffic jams to reveal that the driver struggles with driving in traffic jams. Furthermore, the analysis unit can also utilize past driving data and statistical information to perform long-term driving trend and risk assessments. For example, based on past data, it evaluates driving performance at specific times of day or under specific weather conditions to identify what situations the driver struggles with under those conditions. This allows the analysis unit to quickly and accurately analyze the collected data and identify situations in which the driver struggles. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates training scenarios based on situations identified by the analysis unit. For example, the generation unit uses a generation AI to generate training scenarios. The generation AI takes the identified situations as input and outputs training scenarios. Specifically, the generation AI generates a parking training scenario for a driver who struggles with parking. The generation AI automatically designs training content tailored to the driver's weaknesses and provides scenarios to efficiently improve the driver's skills. For example, for a driver who struggles with parking, it generates a scenario that simulates various parking lot situations to teach basic parking procedures and points to watch out for. For a driver who struggles with sharp turns, it provides a scenario that simulates driving on sharp turns to practice appropriate speed control and steering. Furthermore, the generation unit evaluates the effectiveness of the training scenarios and can modify and improve them as needed. For example, it adjusts the difficulty and content of the scenarios based on driver feedback and training results to provide a more effective training program. This allows the generation unit to generate training scenarios tailored to the individual needs of drivers and support the improvement of their driving skills.

[0033] The feedback unit provides real-time feedback based on training scenarios generated by the generation unit. For example, the feedback unit provides immediate feedback while driving. The feedback unit points out areas for improvement to the driver using voice instructions and visual alerts. Specifically, if a lane change is delayed, the feedback unit immediately points this out and instructs the driver to change lanes at the appropriate time. Voice instructions provide specific advice to the driver, and visual alerts draw the driver's attention through visual warnings displayed on the dashboard or head-up display. For example, if the driver brakes suddenly, the feedback unit will say, "You brake suddenly too often. Slow down earlier," and a visual alert will display the frequency of brake use. The feedback unit also evaluates the driver's driving performance in real time and provides immediate feedback on areas for improvement while driving, allowing the driver to make corrections on the spot. Furthermore, the feedback unit can collect driver feedback and evaluate the effectiveness of the training scenarios. For example, it can record how the driver reacted to the feedback and use this to improve the training scenarios. This allows the feedback unit to provide drivers with quick and specific feedback and support the improvement of their driving skills.

[0034] The support unit combines real-time weather data and traffic information to assist driving during disasters. For example, in the event of an earthquake or flood, the support unit will suggest appropriate driving actions and evacuation routes. The support unit acquires data from weather sensors and traffic cameras and provides information to drivers in real time. Specifically, the support unit suggests safe routes to drivers based on weather data. For example, in the event of heavy rain, it will suggest routes with a low risk of flooding, and in the event of an earthquake, it will select roads with a low risk of collapse. The support unit also analyzes images from traffic cameras and provides drivers with real-time information on congestion and accidents. This allows drivers to drive safely and efficiently based on the latest information. Furthermore, the support unit calculates the optimal evacuation route considering the driver's current location and destination and provides instructions to the driver. For example, in the event of a flood, it will suggest the shortest route to an evacuation center, and in the event of an earthquake, it will guide the driver to a safe evacuation location. The support unit displays this information on the driver's smart device or vehicle's navigation system to support drivers in making quick and appropriate decisions. In this way, the support unit can ensure the safety of drivers and support rapid evacuation during disasters.

[0035] The data collection unit can collect driver driving data. For example, the data collection unit collects driver driving data using sensors mounted on the vehicle. For example, the data collection unit collects driver speed data using a speed sensor. The data collection unit can also collect brake usage frequency using a brake sensor. Furthermore, the data collection unit can collect steering operation data using a steering operation sensor. In this way, the data collection unit can understand individual driving skills by collecting driver driving 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 data acquired from sensors mounted on the vehicle into a generating AI and have the generating AI perform the collection of driving data.

[0036] The analysis unit can analyze the collected data and identify situations in which the driver struggles. For example, the analysis unit can analyze the data using a generative AI. The generative AI takes driving data as input and outputs situations in which the driver struggles. For example, the generative AI analyzes the driver's driving patterns and identifies situations in which the driver struggles, such as sharp curves or traffic jams. The analysis unit can also analyze the driver's past mistakes and identify areas that need improvement. For example, based on past driving data, the analysis unit identifies situations in which the driver frequently makes mistakes. In this way, the analysis unit can provide effective training scenarios by identifying situations in which the driver struggles. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the collected driving data into a generative AI and have the generative AI identify situations in which the driver struggles.

[0037] The generation unit can generate training scenarios based on specified situations. For example, the generation unit generates training scenarios using a generation AI. The generation AI takes a specified situation as input and outputs a training scenario. For example, the generation AI generates a parking training scenario for a driver who has difficulty parking. The generation unit can also generate training scenarios considering the driver's reaction time. For example, the generation unit generates a training scenario that instructs the driver on when to apply the brakes before the traffic light turns red. Furthermore, the generation unit can customize training scenarios based on the driver's driving history. For example, the generation unit generates training scenarios that match the driver's progress based on past driving data. In this way, the generation unit improves the driver's driving skills by providing the driver with the optimal training scenario. 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 a specified situation into the generation AI and have the generation AI generate training scenarios.

[0038] The feedback unit can provide real-time feedback and immediately point out areas for improvement to the driver. For example, the feedback unit provides immediate feedback while driving. The feedback unit points out areas for improvement to the driver using voice instructions and visual alerts. For example, if a lane change is delayed, the feedback unit will immediately point this out and instruct the driver to change lanes at the appropriate time. The feedback unit can also measure the driver's reaction time and provide more appropriate guidance. For example, the feedback unit will instruct the driver on when to apply the brakes before the traffic light turns red. Furthermore, the feedback unit can customize the feedback based on the driver's driving history. For example, the feedback unit will provide feedback tailored to the driver's progress based on past driving data. In this way, the feedback unit promotes driver improvement by providing immediate feedback while driving. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input driving data into a generating AI and have the generating AI perform real-time feedback.

[0039] The support unit can support driving during disasters by combining real-time weather data and traffic information. For example, in the event of an earthquake or flood, the support unit can suggest appropriate driving actions and evacuation routes. The support unit acquires data from weather sensors and traffic cameras and provides information to drivers in real time. For example, the support unit can suggest safe routes to drivers based on weather data. It can also suggest routes that avoid congestion based on traffic information. Furthermore, the support unit can update evacuation routes in real time during disasters and provide drivers with the latest information. In this way, the support unit enhances safety by suggesting appropriate driving actions and evacuation routes during disasters. 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 weather data and traffic information into a generating AI and have the generating AI perform disaster driving support.

[0040] The support unit can suggest driving actions and evacuation routes in the event of an earthquake or flood. For example, in the event of an earthquake, the support unit can suggest a safe evacuation route to the driver. The support unit calculates the optimal evacuation route in real time based on weather data and traffic information. For example, in the event of a flood, the support unit can suggest a route that avoids flooded areas. The support unit can also suggest a safe route based on information about road damage caused by the earthquake. Furthermore, the support unit can predict potential hazards that may occur along the evacuation route and issue warnings to the driver. In this way, the support unit supports safe evacuation in the event of a disaster. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input earthquake and flood data into a generating AI and have the generating AI execute the suggestions for driving actions and evacuation routes.

[0041] The feedback unit can measure the driver's reaction time and provide appropriate guidance. For example, the feedback unit can instruct the driver on when to apply the brakes before the traffic light turns red in order to measure their reaction time. The feedback unit can also provide training scenarios to shorten the driver's reaction time. For example, the feedback unit can train the driver to respond quickly to changes in traffic lights. The feedback unit can also periodically evaluate the driver's reaction time and check their progress. For example, the feedback unit can evaluate whether the driver's reaction time has improved and provide appropriate feedback. In this way, the feedback unit shortens the driver's reaction time and improves their safe driving skills. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the driver's reaction time data into a generating AI and have the generating AI perform appropriate guidance.

[0042] The data collection unit can accumulate individual driving history and performance data and provide reports periodically. For example, the data collection unit can accumulate a driver's driving history and analyze past driving data. The data collection unit can also evaluate a driver's driving performance and provide reports periodically. For example, the data collection unit can evaluate driving skills monthly and provide feedback to the driver. The data collection unit can also compare past and current data to track the driver's progress. For example, the data collection unit can evaluate how much a driver's driving skills have improved and reflect this in the report. In this way, the data collection unit supports the driver's long-term skill improvement. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input driving history and performance data into a generating AI and have the generating AI generate reports.

[0043] The data collection unit can analyze the driver's past driving data and select the optimal data collection method. For example, the data collection unit can concentrate data collection during specific time periods based on past driving data. The data collection unit can also prioritize data collection under specific driving conditions based on past driving data. Furthermore, the data collection unit can analyze past driving data and select a data collection method that matches the driver's driving pattern. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on past driving 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 past driving data into a generating AI and have the generating AI select the optimal data collection method.

[0044] The data collection unit can filter driving data based on the driver's current driving situation and areas of interest. For example, if the driver is driving on a highway, the data collection unit will prioritize collecting data related to highways. If the driver is driving in an urban area, the data collection unit can also prioritize collecting data related to urban areas. Furthermore, if the driver is interested in a particular driving skill, the data collection unit can prioritize collecting data related to that skill. In this way, the data collection unit can obtain more useful data by prioritizing the collection of data that matches the driver's current situation and interests. 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 data on the driver's current driving situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0045] 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 the driver is driving in a specific area, the data collection unit will prioritize the collection of data related to that area. If the driver is driving on a specific route, the data collection unit can also prioritize the collection of data related to that route. Furthermore, if the driver is heading to a specific destination, the data collection unit can prioritize the collection of data related to that destination. In this way, the data collection unit can obtain more useful data by collecting highly relevant data based on 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 the driver's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0046] 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 data based on the driver's driving experiences shared on social media. The data collection unit can also collect data from driving-related accounts that the driver follows on social media. Furthermore, the data collection unit can collect data from driving communities that the driver participates in on social media. This allows the data collection unit to obtain data tailored to the driver's interests by collecting relevant data based on the driver's social media activity. 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 social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0047] 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 performs a detailed analysis on important driving data. The analysis unit can also perform a simplified analysis on driving data of lower importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the driving data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail according to 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 the importance of the driving data into the generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0048] 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 parking-specific analysis algorithm to parking data. The analysis unit can also apply a highway-specific analysis algorithm to highway data. Furthermore, the analysis unit can apply an urban-specific analysis algorithm to urban area data. By doing so, the analysis unit can obtain more accurate analysis results by applying an analysis algorithm appropriate to the category of 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 category of driving data into the generating AI and have the generating AI execute the application of different analysis algorithms.

[0049] The analysis unit can determine the priority of analysis based on the submission timing of the operational data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent operational data. The analysis unit may also postpone the analysis of older operational data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission timing. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the submission timing of the operational 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 the submission timing of the operational data into the generating AI and have the generating AI determine the priority of analysis.

[0050] 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 may prioritize the analysis of highly relevant driving data. The analysis unit may also postpone the analysis of less relevant driving data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the driving data. This allows the analysis unit to prioritize the analysis of more important data by adjusting the order of analysis based on the relevance 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 the relevance of the driving data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0051] The generation unit can adjust the level of detail of training scenarios based on the importance of the driving data when generating training scenarios. For example, the generation unit can generate detailed training scenarios based on important driving data. The generation unit can also generate simplified training scenarios based on less important driving data. Furthermore, the generation unit can determine the priority of training scenarios according to the importance of the driving data. This allows the generation unit to perform efficient training by adjusting the level of detail of scenarios according to 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 the importance of the driving data into the generation AI and have the generation AI perform the adjustment of the level of detail of the scenarios.

[0052] The generation unit can apply different generation algorithms depending on the category of driving data when generating training scenarios. For example, the generation unit can generate a training scenario specifically for parking based on parking data. The generation unit can also generate a training scenario specifically for highways based on highway data. Furthermore, the generation unit can generate a training scenario specifically for urban areas based on urban area data. This allows the generation unit to provide more accurate training scenarios by applying a generation algorithm appropriate to the category of driving data. Some or all of the above-described processes 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 category of driving data into the generation AI and cause the generation AI to apply different generation algorithms.

[0053] The generation unit can determine the priority of training scenarios based on the timing of driving data submission when generating training scenarios. For example, the generation unit can prioritize generating training scenarios based on the most recent driving data. The generation unit can also postpone the generation of training scenarios based on older driving data submission dates. Furthermore, the generation unit can adjust the schedule of training scenarios based on the submission dates. This enables efficient training by allowing the generation unit to prioritize scenarios based on the timing of driving data submission. 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 submissions to the generation AI and have the generation AI determine the priority of scenarios.

[0054] The generation unit can adjust the order of training scenarios based on the relevance of driving data when generating training scenarios. For example, the generation unit can prioritize generating training scenarios based on highly relevant driving data. The generation unit can also postpone the generation of training scenarios based on less relevant driving data. Furthermore, the generation unit can adjust the schedule of training scenarios based on the relevance of driving data. This allows the generation unit to prioritize the inclusion of more important data in training by adjusting the order of scenarios based on the relevance of 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 driving data into a generation AI and have the generation AI perform the adjustment of the scenario order.

[0055] The feedback unit can adjust the level of detail of the feedback based on the importance of the driving data when providing feedback. For example, the feedback unit can provide detailed feedback for important driving data. The feedback unit can also provide simplified feedback for less important driving data. Furthermore, the feedback unit can determine the priority of the feedback according to the importance of the driving data. This allows the feedback unit to provide efficient feedback by adjusting the level of detail of the feedback according to the importance of the driving data. Some or all of the above processing in the feedback unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback unit can input the importance of the driving data into the generating AI and have the generating AI adjust the level of detail of the feedback.

[0056] The feedback unit can apply different feedback algorithms depending on the category of driving data when providing feedback. For example, the feedback unit can apply a parking-specific feedback algorithm to parking data. The feedback unit can also apply a highway-specific feedback algorithm to highway data. Furthermore, the feedback unit can apply an urban-specific feedback algorithm to urban area data. This allows the feedback unit to provide more accurate feedback by applying a feedback algorithm appropriate to the category of driving data. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the category of driving data into a generative AI and cause the generative AI to apply different feedback algorithms.

[0057] The feedback unit can determine the priority of feedback based on the timing of the submission of driving data when providing feedback. For example, the feedback unit may prioritize feedback based on the most recent driving data. The feedback unit may also postpone providing feedback based on older driving data. Furthermore, the feedback unit can adjust the feedback schedule based on the submission timing. This enables efficient feedback by allowing the feedback unit to prioritize feedback based on the timing of the driving data submission. Some or all of the above processing in the feedback unit may be performed using, for example, a generating AI, or without a generating AI. For example, the feedback unit can input the timing of the driving data submission into the generating AI and have the generating AI determine the priority of the feedback.

[0058] The feedback unit can adjust the order of feedback based on the relevance of the driving data when providing feedback. For example, the feedback unit may prioritize providing feedback based on highly relevant driving data. The feedback unit may also postpone providing feedback based on less relevant driving data. Furthermore, the feedback unit can adjust the feedback schedule based on the relevance of the driving data. This allows the feedback unit to prioritize feedback on more 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 feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the relevance of the driving data into a generative AI and have the generative AI perform the adjustment of the feedback order.

[0059] The support unit can adjust the level of detail of support provided based on the importance of the driving data. For example, the support unit can provide detailed support for important driving data, and simplified support for less important driving data. The support unit can also determine the priority of support according to the importance of the driving data. This allows the support unit to provide efficient support by adjusting the level of detail according to the importance of the driving data. Some or all of the above processing in the support unit may be performed using, for example, a generating AI, or without a generating AI. For example, the support unit can input the importance of the driving data into a generating AI and have the generating AI adjust the level of detail of the support.

[0060] The support unit can apply different support algorithms depending on the category of driving data when providing support. For example, the support unit can apply a parking-specific support algorithm to parking data. The support unit can also apply a highway-specific support algorithm to highway data. Furthermore, the support unit can apply an urban-specific support algorithm to urban area data. This allows the support unit to provide more accurate support by applying a support algorithm appropriate to the category of driving data. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the category of driving data into a generative AI and have the generative AI execute the application of different support algorithms.

[0061] The support unit can determine the priority of support based on the timing of the submission of operational data when providing support. For example, the support unit may prioritize support based on the most recent operational data. The support unit may also postpone support based on older operational data. Furthermore, the support unit can adjust the support schedule based on the submission timing. This allows the support unit to provide efficient support by prioritizing support based on the timing of operational data submission. Some or all of the above processing in the support unit may be performed using, for example, a generating AI, or without a generating AI. For example, the support unit can input the timing of operational data submission into a generating AI and have the generating AI determine the priority of support.

[0062] The support unit can adjust the order of support based on the relevance of driving data when providing support. For example, the support unit may prioritize support based on highly relevant driving data. The support unit may also postpone support based on less relevant driving data. Furthermore, the support unit can adjust the support schedule based on the relevance of driving data. This allows the support unit to prioritize support based on the relevance of driving data, thereby reflecting more important data in the support. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the relevance of driving data into a generative AI and have the generative AI perform the adjustment of the support order.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The driver training system includes a data collection unit that collects driver driving data. This unit can collect driver driving data using sensors mounted on the vehicle. Furthermore, when collecting driver driving data, the unit can prioritize the collection of highly relevant data by considering the driver's geographical location. For example, if the driver is driving in a specific area, the unit can prioritize the collection of data related to that area. If the driver is driving a specific route, the unit can also prioritize the collection of data related to that route. Additionally, if the driver is heading to a specific destination, the unit can prioritize the collection of data related to that destination. This allows for the collection of more relevant data based on the driver's geographical location, resulting in more useful data.

[0065] The generation unit can generate training scenarios based on specified situations. Furthermore, the generation unit can apply different generation algorithms depending on the category of driving data when generating training scenarios. For example, it can generate a training scenario specifically for parking based on parking data. It can also generate a training scenario specifically for highways based on highway data. It can also generate a training scenario specifically for urban areas based on urban area data. By applying a generation algorithm according to the category of driving data, it is possible to provide more accurate training scenarios.

[0066] The support unit can combine real-time weather data and traffic information to support driving during disasters. Furthermore, the support unit can adjust the level of detail of support provided based on the importance of the driving data. For example, it can provide detailed support for important driving data and simplified support for less important driving data. It can also prioritize support based on the importance of the driving data. This allows for efficient support by adjusting the level of detail of support according to the importance of the driving data.

[0067] The analysis unit can analyze the collected data and identify situations in which the driver struggles. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of driving data during the analysis. For example, a dedicated analysis algorithm for parking can be applied to parking data. A dedicated analysis algorithm for highways can be applied to highway data. In addition, a dedicated analysis algorithm for urban areas can be applied to urban area data. By applying an analysis algorithm appropriate to the category of driving data, more accurate analysis results can be obtained.

[0068] The feedback system provides real-time feedback, allowing drivers to immediately identify areas for improvement. Furthermore, the feedback system can prioritize feedback based on the timing of driving data submission. For example, it can prioritize feedback based on the most recent driving data, or postpone feedback based on older data. It can also adjust the feedback schedule based on submission timing. This allows for efficient feedback by prioritizing feedback based on the timing of driving data submission.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects driving data. The data collection unit acquires data from, for example, sensors installed in the vehicle or the driver's smart device. Driving data includes speed, brake usage frequency, steering wheel operation, GPS data, and acceleration sensor data. The data collection unit can also store the driver's driving history and analyze past driving data. Step 2: The analysis unit analyzes the data collected by the collection unit to identify situations that the driver finds difficult. For example, the analysis unit uses a generation AI to analyze driving data and identify situations that the driver finds difficult, such as sharp curves or traffic jams. Step 3: The generation unit generates training scenarios based on the situations identified by the analysis unit. For example, the generation unit takes situations identified using the generation AI as input and outputs training scenarios. For example, it generates a parking training scenario for a driver who has difficulty parking. Step 4: The feedback unit provides real-time feedback based on the training scenario generated by the generation unit. For example, the feedback unit points out areas for improvement to the driver using voice instructions and visual alerts while driving, and guides them on things like changing lanes at the appropriate time. Step 5: The support unit combines real-time weather data and traffic information to support driving during disasters. For example, in the event of an earthquake or flood, the support unit will suggest appropriate driving actions and evacuation routes. The support unit acquires data from weather sensors and traffic cameras and provides information to drivers in real time.

[0071] (Example of form 2) The driving training system according to an embodiment of the present invention is a system for improving an individual's driving skills using generative AI. This driving training system collects the driver's driving data, and the generative AI analyzes this data to generate an optimal training scenario for each individual driver. Furthermore, it provides real-time feedback and immediately points out areas for improvement to the driver. It also supports driving during disasters by combining real-time weather data and traffic information. Finally, it supports the long-term skill improvement of the driver by accumulating driving history and performance data and providing regular reports. For example, the driving training system collects the driver's driving data. This data includes driving habits, past mistakes, reaction time, etc. Next, the generative AI analyzes this data and identifies situations in which the driver struggles. For example, for a driver who has difficulty parking, it generates a parking training scenario. Based on the generated training scenario, the driver performs a simulation. This simulation provides real-time feedback and immediately points out areas for improvement to the driver. For example, if the driver is late in changing lanes while driving, the driving training system immediately points this out and instructs the driver to change lanes at the appropriate time. Furthermore, the driving training system supports driving during disasters by combining real-time weather data and traffic information. For example, in the event of an earthquake or flood, the driver training system immediately suggests appropriate driving actions and evacuation routes, allowing drivers to evacuate safely. The system also measures drivers' reaction times and provides more appropriate guidance. For instance, it can instruct drivers on when to brake before a traffic light turns red, shortening reaction times and improving safe driving skills. Finally, the system accumulates individual driving history and performance data, providing regular reports. This allows drivers to track their progress and improve their skills over the long term. For example, monthly evaluations of driving skills allow drivers to see their own growth. In this way, the driver training system helps improve drivers' skills and supports safe driving.

[0072] The driving training system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, a feedback unit, and a support unit. The data collection unit collects driving data. For example, the data collection unit collects the driver's driving data. Driving data includes, but is not limited to, speed, frequency of brake use, and steering wheel operation. For example, the data collection unit collects driving data using sensors mounted on the vehicle. The data collection unit can also acquire data from the driver's smart device. For example, it collects GPS data and acceleration sensor data from a smartphone. Furthermore, the data collection unit can also accumulate the driver's driving history and analyze past driving data. The analysis unit analyzes the data collected by the data collection unit and identifies situations in which the driver has difficulty. For example, the analysis unit analyzes the data using a generation AI. The generation AI takes driving data as input and outputs situations in which the driver has difficulty. For example, the generation AI analyzes the driver's driving pattern and identifies situations in which the driver has difficulty, such as sharp curves and traffic jams. The generation unit generates training scenarios based on the situations identified by the analysis unit. The generation unit generates training scenarios using, for example, a generation AI. The generation AI takes a specified situation as input and outputs a training scenario. For example, the generation AI generates a parking training scenario for a driver who has difficulty parking. The feedback unit provides real-time feedback based on the training scenario generated by the generation unit. The feedback unit provides immediate feedback while driving, for example. The feedback unit points out areas for improvement to the driver using voice instructions and visual alerts. For example, if a lane change is delayed, the feedback unit immediately points this out and instructs the driver to change lanes at the appropriate time. The support unit supports driving during disasters by combining real-time weather data and traffic information. For example, the support unit suggests appropriate driving actions and evacuation routes when an earthquake or flood occurs. The support unit acquires data from weather sensors and traffic cameras and provides information to the driver in real time. For example, the support unit suggests a safe route to the driver based on weather data.As a result, the driving training system according to this embodiment can improve the driver's driving skills and support safe driving.

[0073] The data collection unit collects driving data. For example, the data collection unit collects driving data from the driver. Driving data includes, but is not limited to, speed, frequency of brake use, and steering wheel operation. The data collection unit collects driving data using, for example, sensors mounted on the vehicle. Specifically, the vehicle's speed sensor measures speed in real time, and the brake sensor records the frequency and intensity of brake pedal use. Regarding steering wheel operation, the steering sensor detects the rotation angle and speed of the steering wheel and collects this data. The data collection unit can also acquire data from the driver's smart device. For example, it can collect GPS data and accelerometer data from a smartphone. The smartphone's GPS data records the driver's location and travel route in detail, and the accelerometer detects sudden acceleration and deceleration. Furthermore, the data collection unit can accumulate the driver's driving history and analyze past driving data. This makes it possible to understand the driver's driving patterns and tendencies over the long term and provide training programs tailored to individual drivers. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The analysis unit analyzes the data collected by the collection unit to identify situations in which the driver struggles. For example, the analysis unit uses generative AI to analyze the data. The generative AI takes driving data as input and outputs situations in which the driver struggles. Specifically, the generative AI analyzes the driver's driving patterns and identifies situations in which the driver struggles, such as sharp curves and traffic jams. The generative AI uses machine learning algorithms to extract features from the driving data and model the driver's behavior patterns. For example, it analyzes data such as speed reduction on sharp curves and delays in steering to identify that the driver struggles with sharp curves. It also analyzes the frequency of brake use and acceleration timing in traffic jams to reveal that the driver struggles with driving in traffic jams. Furthermore, the analysis unit can also utilize past driving data and statistical information to perform long-term driving trend and risk assessments. For example, based on past data, it evaluates driving performance at specific times of day or under specific weather conditions to identify what situations the driver struggles with under those conditions. This allows the analysis unit to quickly and accurately analyze the collected data and identify situations in which the driver struggles. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0075] The generation unit generates training scenarios based on situations identified by the analysis unit. For example, the generation unit uses a generation AI to generate training scenarios. The generation AI takes the identified situations as input and outputs training scenarios. Specifically, the generation AI generates a parking training scenario for a driver who struggles with parking. The generation AI automatically designs training content tailored to the driver's weaknesses and provides scenarios to efficiently improve the driver's skills. For example, for a driver who struggles with parking, it generates a scenario that simulates various parking lot situations to teach basic parking procedures and points to watch out for. For a driver who struggles with sharp turns, it provides a scenario that simulates driving on sharp turns to practice appropriate speed control and steering. Furthermore, the generation unit evaluates the effectiveness of the training scenarios and can modify and improve them as needed. For example, it adjusts the difficulty and content of the scenarios based on driver feedback and training results to provide a more effective training program. This allows the generation unit to generate training scenarios tailored to the individual needs of drivers and support the improvement of their driving skills.

[0076] The feedback unit provides real-time feedback based on training scenarios generated by the generation unit. For example, the feedback unit provides immediate feedback while driving. The feedback unit points out areas for improvement to the driver using voice instructions and visual alerts. Specifically, if a lane change is delayed, the feedback unit immediately points this out and instructs the driver to change lanes at the appropriate time. Voice instructions provide specific advice to the driver, and visual alerts draw the driver's attention through visual warnings displayed on the dashboard or head-up display. For example, if the driver brakes suddenly, the feedback unit will say, "You brake suddenly too often. Slow down earlier," and a visual alert will display the frequency of brake use. The feedback unit also evaluates the driver's driving performance in real time and provides immediate feedback on areas for improvement while driving, allowing the driver to make corrections on the spot. Furthermore, the feedback unit can collect driver feedback and evaluate the effectiveness of the training scenarios. For example, it can record how the driver reacted to the feedback and use this to improve the training scenarios. This allows the feedback unit to provide drivers with quick and specific feedback and support the improvement of their driving skills.

[0077] The support unit combines real-time weather data and traffic information to assist driving during disasters. For example, in the event of an earthquake or flood, the support unit will suggest appropriate driving actions and evacuation routes. The support unit acquires data from weather sensors and traffic cameras and provides information to drivers in real time. Specifically, the support unit suggests safe routes to drivers based on weather data. For example, in the event of heavy rain, it will suggest routes with a low risk of flooding, and in the event of an earthquake, it will select roads with a low risk of collapse. The support unit also analyzes images from traffic cameras and provides drivers with real-time information on congestion and accidents. This allows drivers to drive safely and efficiently based on the latest information. Furthermore, the support unit calculates the optimal evacuation route considering the driver's current location and destination and provides instructions to the driver. For example, in the event of a flood, it will suggest the shortest route to an evacuation center, and in the event of an earthquake, it will guide the driver to a safe evacuation location. The support unit displays this information on the driver's smart device or vehicle's navigation system to support drivers in making quick and appropriate decisions. In this way, the support unit can ensure the safety of drivers and support rapid evacuation during disasters.

[0078] The data collection unit can collect driver driving data. For example, the data collection unit collects driver driving data using sensors mounted on the vehicle. For example, the data collection unit collects driver speed data using a speed sensor. The data collection unit can also collect brake usage frequency using a brake sensor. Furthermore, the data collection unit can collect steering operation data using a steering operation sensor. In this way, the data collection unit can understand individual driving skills by collecting driver driving 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 data acquired from sensors mounted on the vehicle into a generating AI and have the generating AI perform the collection of driving data.

[0079] The analysis unit can analyze the collected data and identify situations in which the driver struggles. For example, the analysis unit can analyze the data using a generative AI. The generative AI takes driving data as input and outputs situations in which the driver struggles. For example, the generative AI analyzes the driver's driving patterns and identifies situations in which the driver struggles, such as sharp curves or traffic jams. The analysis unit can also analyze the driver's past mistakes and identify areas that need improvement. For example, based on past driving data, the analysis unit identifies situations in which the driver frequently makes mistakes. In this way, the analysis unit can provide effective training scenarios by identifying situations in which the driver struggles. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the collected driving data into a generative AI and have the generative AI identify situations in which the driver struggles.

[0080] The generation unit can generate training scenarios based on specified situations. For example, the generation unit generates training scenarios using a generation AI. The generation AI takes a specified situation as input and outputs a training scenario. For example, the generation AI generates a parking training scenario for a driver who has difficulty parking. The generation unit can also generate training scenarios considering the driver's reaction time. For example, the generation unit generates a training scenario that instructs the driver on when to apply the brakes before the traffic light turns red. Furthermore, the generation unit can customize training scenarios based on the driver's driving history. For example, the generation unit generates training scenarios that match the driver's progress based on past driving data. In this way, the generation unit improves the driver's driving skills by providing the driver with the optimal training scenario. 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 a specified situation into the generation AI and have the generation AI generate training scenarios.

[0081] The feedback unit can provide real-time feedback and immediately point out areas for improvement to the driver. For example, the feedback unit provides immediate feedback while driving. The feedback unit points out areas for improvement to the driver using voice instructions and visual alerts. For example, if a lane change is delayed, the feedback unit will immediately point this out and instruct the driver to change lanes at the appropriate time. The feedback unit can also measure the driver's reaction time and provide more appropriate guidance. For example, the feedback unit will instruct the driver on when to apply the brakes before the traffic light turns red. Furthermore, the feedback unit can customize the feedback based on the driver's driving history. For example, the feedback unit will provide feedback tailored to the driver's progress based on past driving data. In this way, the feedback unit promotes driver improvement by providing immediate feedback while driving. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input driving data into a generating AI and have the generating AI perform real-time feedback.

[0082] The support unit can support driving during disasters by combining real-time weather data and traffic information. For example, in the event of an earthquake or flood, the support unit can suggest appropriate driving actions and evacuation routes. The support unit acquires data from weather sensors and traffic cameras and provides information to drivers in real time. For example, the support unit can suggest safe routes to drivers based on weather data. It can also suggest routes that avoid congestion based on traffic information. Furthermore, the support unit can update evacuation routes in real time during disasters and provide drivers with the latest information. In this way, the support unit enhances safety by suggesting appropriate driving actions and evacuation routes during disasters. 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 weather data and traffic information into a generating AI and have the generating AI perform disaster driving support.

[0083] The support unit can suggest driving actions and evacuation routes in the event of an earthquake or flood. For example, in the event of an earthquake, the support unit can suggest a safe evacuation route to the driver. The support unit calculates the optimal evacuation route in real time based on weather data and traffic information. For example, in the event of a flood, the support unit can suggest a route that avoids flooded areas. The support unit can also suggest a safe route based on information about road damage caused by the earthquake. Furthermore, the support unit can predict potential hazards that may occur along the evacuation route and issue warnings to the driver. In this way, the support unit supports safe evacuation in the event of a disaster. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input earthquake and flood data into a generating AI and have the generating AI execute the suggestions for driving actions and evacuation routes.

[0084] The feedback unit can measure the driver's reaction time and provide appropriate guidance. For example, the feedback unit can instruct the driver on when to apply the brakes before the traffic light turns red in order to measure their reaction time. The feedback unit can also provide training scenarios to shorten the driver's reaction time. For example, the feedback unit can train the driver to respond quickly to changes in traffic lights. The feedback unit can also periodically evaluate the driver's reaction time and check their progress. For example, the feedback unit can evaluate whether the driver's reaction time has improved and provide appropriate feedback. In this way, the feedback unit shortens the driver's reaction time and improves their safe driving skills. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the driver's reaction time data into a generating AI and have the generating AI perform appropriate guidance.

[0085] The data collection unit can accumulate individual driving history and performance data and provide reports periodically. For example, the data collection unit can accumulate a driver's driving history and analyze past driving data. The data collection unit can also evaluate a driver's driving performance and provide reports periodically. For example, the data collection unit can evaluate driving skills monthly and provide feedback to the driver. The data collection unit can also compare past and current data to track the driver's progress. For example, the data collection unit can evaluate how much a driver's driving skills have improved and reflect this in the report. In this way, the data collection unit supports the driver's long-term skill improvement. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input driving history and performance data into a generating AI and have the generating AI generate reports.

[0086] The data collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the driver is tense, the data collection unit may delay data collection until the driver relaxes. If the driver is relaxed, the data collection unit may collect data more frequently and in more detail. The data collection unit may also pause data collection if the driver is tired and resume it after a break. This allows the data collection unit to collect more accurate data by adjusting the timing of data collection according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 or not. For example, the data collection unit can input driver emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0087] The data collection unit can analyze the driver's past driving data and select the optimal data collection method. For example, the data collection unit can concentrate data collection during specific time periods based on past driving data. The data collection unit can also prioritize data collection under specific driving conditions based on past driving data. Furthermore, the data collection unit can analyze past driving data and select a data collection method that matches the driver's driving pattern. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on past driving 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 past driving data into a generating AI and have the generating AI select the optimal data collection method.

[0088] The data collection unit can filter driving data based on the driver's current driving situation and areas of interest. For example, if the driver is driving on a highway, the data collection unit will prioritize collecting data related to highways. If the driver is driving in an urban area, the data collection unit can also prioritize collecting data related to urban areas. Furthermore, if the driver is interested in a particular driving skill, the data collection unit can prioritize collecting data related to that skill. In this way, the data collection unit can obtain more useful data by prioritizing the collection of data that matches the driver's current situation and interests. 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 data on the driver's current driving situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0089] 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 tense, the data collection unit will prioritize collecting data related to tension. If the driver is relaxed, the data collection unit may also prioritize collecting data related to relaxation. Furthermore, if the driver is tired, the data collection unit may also prioritize collecting data related to fatigue. In this way, the data collection unit can prioritize the collection of important data by prioritizing data according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data 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 to collect.

[0090] 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 the driver is driving in a specific area, the data collection unit will prioritize the collection of data related to that area. If the driver is driving on a specific route, the data collection unit can also prioritize the collection of data related to that route. Furthermore, if the driver is heading to a specific destination, the data collection unit can prioritize the collection of data related to that destination. In this way, the data collection unit can obtain more useful data by collecting highly relevant data based on 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 the driver's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0091] 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 data based on the driver's driving experiences shared on social media. The data collection unit can also collect data from driving-related accounts that the driver follows on social media. Furthermore, the data collection unit can collect data from driving communities that the driver participates in on social media. This allows the data collection unit to obtain data tailored to the driver's interests by collecting relevant data based on the driver's social media activity. 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 social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0092] 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 tense, the analysis unit provides simple and easy-to-understand analysis results. If the driver is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the driver is excited, the analysis unit can provide visually stimulating analysis results. In this way, the analysis unit can provide analysis results that are easy for the driver to understand by adjusting the presentation of the analysis according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the driver's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0093] 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 performs a detailed analysis on important driving data. The analysis unit can also perform a simplified analysis on driving data of lower importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the driving data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail according to 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 the importance of the driving data into the generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0094] 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 parking-specific analysis algorithm to parking data. The analysis unit can also apply a highway-specific analysis algorithm to highway data. Furthermore, the analysis unit can apply an urban-specific analysis algorithm to urban area data. By doing so, the analysis unit can obtain more accurate analysis results by applying an analysis algorithm appropriate to the category of 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 category of driving data into the generating AI and have the generating AI execute the application of different analysis algorithms.

[0095] 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 a visually stimulating analysis. In this way, the analysis unit can provide the driver with the optimal analysis result by adjusting the length of the analysis according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the driver's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0096] The analysis unit can determine the priority of analysis based on the submission timing of the operational data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent operational data. The analysis unit may also postpone the analysis of older operational data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission timing. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the submission timing of the operational 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 the submission timing of the operational data into the generating AI and have the generating AI determine the priority of analysis.

[0097] 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 may prioritize the analysis of highly relevant driving data. The analysis unit may also postpone the analysis of less relevant driving data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the driving data. This allows the analysis unit to prioritize the analysis of more important data by adjusting the order of analysis based on the relevance 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 the relevance of the driving data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0098] The generation unit can estimate the driver's emotions and adjust the method of generating training scenarios based on the estimated emotions. For example, if the driver is tense, the generation unit can generate a training scenario to help the driver relax. If the driver is relaxed, the generation unit can also generate a more challenging training scenario. Furthermore, if the driver is excited, the generation unit can generate a visually stimulating training scenario. In this way, the generation unit can provide the driver with the optimal training scenario by adjusting the method of generating training scenarios according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the driver's emotion data into the generation AI and have the generation AI adjust the method of generating training scenarios.

[0099] The generation unit can adjust the level of detail of training scenarios based on the importance of the driving data when generating training scenarios. For example, the generation unit can generate detailed training scenarios based on important driving data. The generation unit can also generate simplified training scenarios based on less important driving data. Furthermore, the generation unit can determine the priority of training scenarios according to the importance of the driving data. This allows the generation unit to perform efficient training by adjusting the level of detail of scenarios according to 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 the importance of the driving data into the generation AI and have the generation AI perform the adjustment of the level of detail of the scenarios.

[0100] The generation unit can apply different generation algorithms depending on the category of driving data when generating training scenarios. For example, the generation unit can generate a training scenario specifically for parking based on parking data. The generation unit can also generate a training scenario specifically for highways based on highway data. Furthermore, the generation unit can generate a training scenario specifically for urban areas based on urban area data. This allows the generation unit to provide more accurate training scenarios by applying a generation algorithm appropriate to the category of driving data. Some or all of the above-described processes 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 category of driving data into the generation AI and cause the generation AI to apply different generation algorithms.

[0101] The generation unit can estimate the driver's emotions and adjust the length of the training scenario based on the estimated emotions. For example, if the driver is in a hurry, the generation unit can generate a short, concise training scenario. If the driver is relaxed, the generation unit can also generate a longer training scenario with detailed explanations. Furthermore, if the driver is excited, the generation unit can generate a visually stimulating training scenario. In this way, the generation unit can provide the driver with the optimal training scenario by adjusting the length of the training scenario according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input driver emotion data into the generative AI and have the generative AI adjust the length of the training scenario.

[0102] The generation unit can determine the priority of training scenarios based on the timing of driving data submission when generating training scenarios. For example, the generation unit can prioritize generating training scenarios based on the most recent driving data. The generation unit can also postpone the generation of training scenarios based on older driving data submission dates. Furthermore, the generation unit can adjust the schedule of training scenarios based on the submission dates. This enables efficient training by allowing the generation unit to prioritize scenarios based on the timing of driving data submission. 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 submissions to the generation AI and have the generation AI determine the priority of scenarios.

[0103] The generation unit can adjust the order of training scenarios based on the relevance of driving data when generating training scenarios. For example, the generation unit can prioritize generating training scenarios based on highly relevant driving data. The generation unit can also postpone the generation of training scenarios based on less relevant driving data. Furthermore, the generation unit can adjust the schedule of training scenarios based on the relevance of driving data. This allows the generation unit to prioritize the inclusion of more important data in training by adjusting the order of scenarios based on the relevance of 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 driving data into a generation AI and have the generation AI perform the adjustment of the scenario order.

[0104] The feedback unit can estimate the driver's emotions and adjust the way it presents the feedback based on the estimated emotions. For example, if the driver is tense, the feedback unit will provide feedback in a calm tone. If the driver is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the driver is excited, the feedback unit can provide visually stimulating feedback. In this way, the feedback unit can provide feedback that is easy for the driver to understand by adjusting the way it presents the feedback according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the driver's emotion data into the generative AI and have the generative AI adjust the way it presents the feedback.

[0105] The feedback unit can adjust the level of detail of the feedback based on the importance of the driving data when providing feedback. For example, the feedback unit can provide detailed feedback for important driving data. The feedback unit can also provide simplified feedback for less important driving data. Furthermore, the feedback unit can determine the priority of the feedback according to the importance of the driving data. This allows the feedback unit to provide efficient feedback by adjusting the level of detail of the feedback according to the importance of the driving data. Some or all of the above processing in the feedback unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback unit can input the importance of the driving data into the generating AI and have the generating AI adjust the level of detail of the feedback.

[0106] The feedback unit can apply different feedback algorithms depending on the category of driving data when providing feedback. For example, the feedback unit can apply a parking-specific feedback algorithm to parking data. The feedback unit can also apply a highway-specific feedback algorithm to highway data. Furthermore, the feedback unit can apply an urban-specific feedback algorithm to urban area data. This allows the feedback unit to provide more accurate feedback by applying a feedback algorithm appropriate to the category of driving data. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the category of driving data into a generative AI and cause the generative AI to apply different feedback algorithms.

[0107] The feedback 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 feedback unit provides short, concise feedback. If the driver is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the driver is excited, the feedback unit can provide visually stimulating feedback. In this way, the feedback unit can provide the driver with optimal feedback by adjusting the length of the feedback according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the driver's emotion data into the generative AI and have the generative AI adjust the length of the feedback.

[0108] The feedback unit can determine the priority of feedback based on the timing of the submission of driving data when providing feedback. For example, the feedback unit may prioritize feedback based on the most recent driving data. The feedback unit may also postpone providing feedback based on older driving data. Furthermore, the feedback unit can adjust the feedback schedule based on the submission timing. This enables efficient feedback by allowing the feedback unit to prioritize feedback based on the timing of the driving data submission. Some or all of the above processing in the feedback unit may be performed using, for example, a generating AI, or without a generating AI. For example, the feedback unit can input the timing of the driving data submission into the generating AI and have the generating AI determine the priority of the feedback.

[0109] The feedback unit can adjust the order of feedback based on the relevance of the driving data when providing feedback. For example, the feedback unit may prioritize providing feedback based on highly relevant driving data. The feedback unit may also postpone providing feedback based on less relevant driving data. Furthermore, the feedback unit can adjust the feedback schedule based on the relevance of the driving data. This allows the feedback unit to prioritize feedback on more 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 feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the relevance of the driving data into a generative AI and have the generative AI perform the adjustment of the feedback order.

[0110] The support unit can estimate the driver's emotions and adjust the way it expresses support based on the estimated emotions. For example, if the driver is tense, the support unit will provide support in a calm tone. If the driver is relaxed, the support unit may also provide detailed support. Furthermore, if the driver is excited, the support unit may provide visually stimulating support. In this way, the support unit can provide support that is easy for the driver to understand by adjusting the way it expresses support according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input the driver's emotion data into the generative AI and have the generative AI adjust the way it expresses support.

[0111] The support unit can adjust the level of detail of support provided based on the importance of the driving data. For example, the support unit can provide detailed support for important driving data, and simplified support for less important driving data. The support unit can also determine the priority of support according to the importance of the driving data. This allows the support unit to provide efficient support by adjusting the level of detail according to the importance of the driving data. Some or all of the above processing in the support unit may be performed using, for example, a generating AI, or without a generating AI. For example, the support unit can input the importance of the driving data into a generating AI and have the generating AI adjust the level of detail of the support.

[0112] The support unit can apply different support algorithms depending on the category of driving data when providing support. For example, the support unit can apply a parking-specific support algorithm to parking data. The support unit can also apply a highway-specific support algorithm to highway data. Furthermore, the support unit can apply an urban-specific support algorithm to urban area data. This allows the support unit to provide more accurate support by applying a support algorithm appropriate to the category of driving data. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the category of driving data into a generative AI and have the generative AI execute the application of different support algorithms.

[0113] The support unit can estimate the driver's emotions and adjust the length of the support based on the estimated emotions. For example, if the driver is in a hurry, the support unit can provide short, concise support. If the driver is relaxed, the support unit can also provide detailed support. Furthermore, if the driver is excited, the support unit can provide visually stimulating support. In this way, the support unit can provide optimal support to the driver by adjusting the length of the support according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the driver's emotion data into the generative AI and have the generative AI adjust the length of the support.

[0114] The support unit can determine the priority of support based on the timing of the submission of operational data when providing support. For example, the support unit may prioritize support based on the most recent operational data. The support unit may also postpone support based on older operational data. Furthermore, the support unit can adjust the support schedule based on the submission timing. This allows the support unit to provide efficient support by prioritizing support based on the timing of operational data submission. Some or all of the above processing in the support unit may be performed using, for example, a generating AI, or without a generating AI. For example, the support unit can input the timing of operational data submission into a generating AI and have the generating AI determine the priority of support.

[0115] The support unit can adjust the order of support based on the relevance of driving data when providing support. For example, the support unit may prioritize support based on highly relevant driving data. The support unit may also postpone support based on less relevant driving data. Furthermore, the support unit can adjust the support schedule based on the relevance of driving data. This allows the support unit to prioritize support based on the relevance of driving data, thereby reflecting more important data in the support. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the relevance of driving data into a generative AI and have the generative AI perform the adjustment of the support order.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The driver training system includes a data collection unit that collects driver driving data, an analysis unit that analyzes the collected data, a generation unit that generates training scenarios based on the analysis results, a feedback unit that provides real-time feedback, and a support unit that assists with driving during disasters. Furthermore, a function can be added to estimate the driver's emotions and adjust the difficulty level of the training scenario based on the estimated emotions. For example, if the driver is tense, an easy training scenario to help them relax can be provided. If the driver is relaxed, a more difficult training scenario can be provided. Also, if the driver is excited, a visually stimulating training scenario can be provided. This allows for the provision of an optimal training scenario tailored to the driver's emotions.

[0118] The driver training system includes a data collection unit that collects driver driving data. This unit can collect driver driving data using sensors mounted on the vehicle. Furthermore, when collecting driver driving data, the unit can prioritize the collection of highly relevant data by considering the driver's geographical location. For example, if the driver is driving in a specific area, the unit can prioritize the collection of data related to that area. If the driver is driving a specific route, the unit can also prioritize the collection of data related to that route. Additionally, if the driver is heading to a specific destination, the unit can prioritize the collection of data related to that destination. This allows for the collection of more relevant data based on the driver's geographical location, resulting in more useful data.

[0119] The analysis unit can analyze the collected data and identify situations that drivers find difficult. Furthermore, the analysis unit can estimate the driver's emotions and adjust the presentation of the analysis based on those emotions. For example, if the driver is tense, it can provide simple and easy-to-understand analysis results. If the driver is relaxed, it can provide detailed analysis results. If the driver is excited, it can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the driver's emotions, it is possible to provide analysis results that are easy for the driver to understand.

[0120] The generation unit can generate training scenarios based on specified situations. Furthermore, the generation unit can apply different generation algorithms depending on the category of driving data when generating training scenarios. For example, it can generate a training scenario specifically for parking based on parking data. It can also generate a training scenario specifically for highways based on highway data. It can also generate a training scenario specifically for urban areas based on urban area data. By applying a generation algorithm according to the category of driving data, it is possible to provide more accurate training scenarios.

[0121] The feedback unit provides real-time feedback, allowing drivers to immediately identify areas for improvement. Furthermore, it can estimate the driver's emotions and adjust the feedback presentation based on that estimation. For example, if the driver is tense, it provides feedback in a calm tone. If the driver is relaxed, it can provide detailed feedback. If the driver is excited, it can provide visually stimulating feedback. This allows the feedback to be presented in a way that is easily understood by the driver, by adjusting the presentation according to their emotions.

[0122] The support unit can combine real-time weather data and traffic information to support driving during disasters. Furthermore, the support unit can adjust the level of detail of support provided based on the importance of the driving data. For example, it can provide detailed support for important driving data and simplified support for less important driving data. It can also prioritize support based on the importance of the driving data. This allows for efficient support by adjusting the level of detail of support according to the importance of the driving data.

[0123] The data collection unit can collect driving data from the driver. Furthermore, the data collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the driver is tense, data collection can be delayed until the driver relaxes. If the driver is relaxed, data collection can be performed more frequently to collect detailed data. Also, if the driver is tired, data collection can be paused and resumed after a break. This allows for the collection of more accurate data by adjusting the timing of data collection according to the driver's emotions.

[0124] The analysis unit can analyze the collected data and identify situations in which the driver struggles. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of driving data during the analysis. For example, a dedicated analysis algorithm for parking can be applied to parking data. A dedicated analysis algorithm for highways can be applied to highway data. In addition, a dedicated analysis algorithm for urban areas can be applied to urban area data. By applying an analysis algorithm appropriate to the category of driving data, more accurate analysis results can be obtained.

[0125] The generation unit can generate training scenarios based on specified situations. Furthermore, the generation unit can estimate the driver's emotions and adjust the length of the training scenario based on the estimated emotions. For example, if the driver is in a hurry, it can generate a short, concise training scenario. If the driver is relaxed, it can generate a longer training scenario with detailed explanations. If the driver is excited, it can generate a visually stimulating training scenario. In this way, by adjusting the length of the training scenario according to the driver's emotions, it is possible to provide the driver with the most optimal training scenario.

[0126] The feedback system provides real-time feedback, allowing drivers to immediately identify areas for improvement. Furthermore, the feedback system can prioritize feedback based on the timing of driving data submission. For example, it can prioritize feedback based on the most recent driving data, or postpone feedback based on older data. It can also adjust the feedback schedule based on submission timing. This allows for efficient feedback by prioritizing feedback based on the timing of driving data submission.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects driving data. The data collection unit acquires data from, for example, sensors installed in the vehicle or the driver's smart device. Driving data includes speed, brake usage frequency, steering wheel operation, GPS data, and acceleration sensor data. The data collection unit can also store the driver's driving history and analyze past driving data. Step 2: The analysis unit analyzes the data collected by the collection unit to identify situations that the driver finds difficult. For example, the analysis unit uses a generation AI to analyze driving data and identify situations that the driver finds difficult, such as sharp curves or traffic jams. Step 3: The generation unit generates training scenarios based on the situations identified by the analysis unit. For example, the generation unit takes situations identified using the generation AI as input and outputs training scenarios. For example, it generates a parking training scenario for a driver who has difficulty parking. Step 4: The feedback unit provides real-time feedback based on the training scenario generated by the generation unit. For example, the feedback unit points out areas for improvement to the driver using voice instructions and visual alerts while driving, and guides them on things like changing lanes at the appropriate time. Step 5: The support unit combines real-time weather data and traffic information to support driving during disasters. For example, in the event of an earthquake or flood, the support unit will suggest appropriate driving actions and evacuation routes. The support unit acquires data from weather sensors and traffic cameras and provides information to drivers in real time.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and the generation unit is similarly implemented in the specific processing unit 290 of the data processing unit 12. The feedback unit is implemented in the control unit 46A of the smart device 14 and provides real-time feedback during driving. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports driving during disasters by combining real-time weather data and traffic information. 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.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback 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 collection unit collects driving data using the camera 42 and sensors of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the generation unit is similarly implemented by the specific processing unit 290 of the data processing unit 12. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides real-time feedback during driving. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports driving during disasters by combining real-time weather data and traffic information. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and sensors of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and the generation unit is similarly implemented in the specific processing unit 290 of the data processing unit 12. The feedback unit is implemented in the control unit 46A of the headset terminal 314 and provides real-time feedback during driving. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports driving during disasters by combining real-time weather data and traffic information. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and sensors of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and the generation unit is similarly implemented in the specific processing unit 290 of the data processing unit 12. The feedback unit is implemented in the control unit 46A of the robot 414 and provides real-time feedback during operation. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports operation during disasters by combining real-time weather data and traffic information. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A data collection unit that collects driving data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify situations that the driver finds difficult, A generation unit generates a training scenario based on the situation identified by the analysis unit, A feedback unit provides real-time feedback based on the training scenario generated by the generation unit, A system equipped with a support unit that combines real-time weather data and traffic information to assist driving during disasters. (Note 2) The aforementioned collection unit is Collect driver data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify situations that drivers find difficult. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate training scenarios based on identified situations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Provide real-time feedback and immediately point out areas for improvement to the driver. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, Combining real-time weather data and traffic information to support driving during disasters. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit, In the event of an earthquake or flood, it will suggest driving actions and evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned feedback unit is Measure the driver's reaction time and provide appropriate guidance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It collects individual driving history and performance data and provides regular reports. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is Analyze the driver's past driving data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting driving data, filtering is performed based on the driver's current driving status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The system estimates the driver's emotions and prioritizes the driving data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) 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 16) 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 17) 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 18) 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 19) 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 20) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the operating data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The generating unit is The system estimates the driver's emotions and adjusts how training scenarios are generated based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating training scenarios, adjust the level of detail in the scenarios based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating training scenarios, different generation algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is The system estimates the driver's emotions and adjusts the length of the training scenario based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating training scenarios, prioritize scenarios based on when the driving data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating training scenarios, adjust the order of scenarios based on the relevance of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback 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 29) The aforementioned feedback unit is When providing 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 30) The aforementioned feedback unit is When providing 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 31) The aforementioned feedback unit is The system estimates the driver's emotions and adjusts the length of the feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, we will prioritize the feedback based on when the driving data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, the order of feedback will be adjusted based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit, The system estimates the driver's emotions and adjusts the way support is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, When providing support, adjust the level of detail based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit, When providing support, different support algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit, The system estimates the driver's emotions and adjusts the length of support based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit, When providing support, the priority of support will be determined based on the timing of submission of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned support unit, When providing support, the order of support will be adjusted based on the relevance of driving data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 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 data collected by the aforementioned collection unit to identify situations that the driver finds difficult, A generation unit generates a training scenario based on the situation identified by the analysis unit, A feedback unit provides real-time feedback based on the training scenario generated by the generation unit, A system equipped with a support unit that combines real-time weather data and traffic information to assist driving during disasters.

2. 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.

3. 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 according to feature 1.

4. The generating unit is When generating training scenarios, different generation algorithms are applied depending on the category of driving data. The system according to feature 1.

5. The aforementioned feedback unit is Provide real-time feedback and immediately point out areas for improvement to the driver. The system according to feature 1.

6. The aforementioned support unit, Combining real-time weather data and traffic information to support driving during disasters. The system according to feature 1.

7. The aforementioned support unit, In the event of an earthquake or flood, it will suggest driving actions and evacuation routes. The system according to feature 1.

8. The aforementioned feedback unit is Measure the driver's reaction time and provide appropriate guidance. The system according to feature 1.

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