System

Abstract

An object of the system according to the embodiment is to simultaneously realize improvement in driving technique and reduction in influence on the environment.SOLUTION: A system according to an embodiment includes a smart driving app, an in-vehicle sensor, generated AI, a driving score, and an incentive program. The smart driving app is installed on a smart phone of the driver, collects and analyzes data during driving, and provides feedback to the driver in real time. The vehicle-mounted sensor collects various data of the vehicle in real time and sends the data to the smart driving app. The generative AI analyzes the information from the onboard sensors and provides a driving score to the driver. The driving score is used as an evaluation index of a driving skill, and facilitates skill improvement of the driver. The incentive program is a mechanism that provides a reward to the driver based on the driving score.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to simultaneously improve driving skills and reduce environmental impact.

[0005] The system according to the embodiment aims to simultaneously improve driving skills and reduce environmental impact. [Means for solving the problem]

[0006] The system according to the embodiment includes a smart driving app, an on-board sensor, a generation AI, a driving score, and an incentive program. The smart driving app is installed on the driver's smartphone, collects and analyzes data during driving, and provides feedback to the driver in real time. The on-board sensor collects various vehicle data in real time and sends it to the smart driving app. The generation AI analyzes the data from the on-board sensor and provides the driver with a driving score. The driving score is used as an evaluation index of driving technique and promotes the improvement of the driver's skills. The incentive program is a mechanism that provides rewards to the driver based on the driving score. [Effects of the Invention]

[0007] The system according to the embodiment can simultaneously improve driving skills and reduce environmental impact. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A driving skill improvement system according to an embodiment of the present invention provides real-time feedback to drivers, encouraging them to improve their driving skills and reduce CO2 emissions. This allows the driving skill improvement system to improve drivers' driving skills and reduce environmental impact.

[0029] A driving skill improvement system according to an embodiment includes a smart driving app, an on-board sensor, a generation AI, a driving score, and an incentive program. The smart driving app is installed on a driver's smartphone and collects and analyzes driving data to provide real-time feedback to the driver. For example, the smart driving app analyzes the number of sudden braking and sudden acceleration, the frequency of speeding, and other factors to alert the driver. The smart driving app can also generate customized feedback tailored to the driver's driving style. The on-board sensor collects various vehicle data in real time and transmits it to the smart driving app. For example, the on-board sensor collects data such as speed, acceleration, brake usage, and fuel consumption. The generation AI analyzes the data from the on-board sensor and provides the driver with a driving score. For example, the generation AI calculates a driving score based on the driving data and suggests specific areas for improvement to the driver. The driving score is used as an evaluation index of driving skill and promotes the improvement of the driver's skills. For example, the driving score may be higher for drivers who rarely brake or accelerate suddenly and rarely exceed the speeding limit. An incentive program is a mechanism for providing rewards to drivers based on their driving scores. For example, drivers who achieve high driving scores are provided with benefits such as fuel discounts and insurance discounts. As a result, the driving skill improvement system according to the embodiment can improve drivers' driving skills and reduce environmental impact. For example, if drivers reduce sudden braking and sudden acceleration, fuel efficiency improves and CO2 emissions are reduced. Furthermore, an incentive program based on driving scores can increase drivers' motivation to improve their driving skills.

[0030] A smart driving app can analyze the number of sudden braking and sudden acceleration, the frequency of speeding, and other factors to warn the driver. For example, a smart driving app can analyze the number of sudden braking and sudden acceleration to warn the driver. For example, if there are many sudden braking incidents, the app can provide advice to the driver to improve how they use the brakes. A smart driving app can also analyze the frequency of speeding incidents to warn the driver. For example, if there are many speeding incidents, the app can provide advice to the driver to maintain the speed limit. This can promote safe driving by drivers.

[0031] The generation AI can suggest specific areas for improvement to the driver based on the driving score. The generation AI can suggest specific areas for improvement to the driver based on the driving score. For example, if the driving score is low, the generation AI can provide advice to the driver to reduce sudden braking and sudden acceleration. The generation AI can also provide advice to the driver to reduce speeding based on the driving score. For example, if the frequency of speeding is high, the generation AI can provide advice to the driver to maintain the speed limit. This can promote improvement of the driver's driving skills.

[0032] The incentive program can provide benefits such as fuel discounts and insurance discounts to drivers who achieve a high driving score. For example, the incentive program provides a fuel discount to drivers who achieve a high driving score. For example, a fuel discount coupon is provided to drivers who achieve a driving score of 90 points or higher. The incentive program also provides an insurance discount to drivers who achieve a high driving score. For example, an insurance discount is provided to drivers who achieve a driving score of 80 points or higher. This can increase the motivation of drivers to improve their driving skills.

[0033] The generating AI can analyze driving data and make specific suggestions to promote the reduction of CO2 emissions. The generating AI can, for example, analyze driving data and make specific suggestions to promote the reduction of CO2 emissions. For example, the generating AI can recommend eco-driving based on the driving data. For example, reducing sudden braking and sudden acceleration can improve fuel efficiency and reduce CO2 emissions. The generating AI can also suggest driving methods to improve fuel efficiency based on the driving data. For example, maintaining a constant speed can improve fuel efficiency and reduce CO2 emissions. This makes it possible to achieve environmentally friendly driving.

[0034] A smart driving app can learn a driver's past driving data and generate customized feedback tailored to their individual driving style. For example, a smart driving app collects a driver's past driving data and learns their driving style. For example, it analyzes the frequency of sudden braking and sudden acceleration, and tendencies toward speeding, and provides feedback tailored to their individual driving style. Furthermore, a smart driving app generates customized feedback that is optimal for the driver's driving style based on the past driving data. For example, it identifies common mistakes and areas for improvement and provides specific advice. Furthermore, to provide customized feedback tailored to the driver's driving style, a generation AI analyzes past driving data and generates advice tailored to the driver's characteristics. For example, it provides feedback that takes into account the driver's habits and patterns. This allows the app to provide feedback tailored to the driver's individual driving style and promote improvement of driving skills.

[0035] A smart driving app can analyze the surrounding environment while driving in real time and provide driving advice based on that analysis. For example, a smart driving app collects and analyzes surrounding environment data (weather, traffic conditions) while driving in real time. For example, it obtains weather forecasts and traffic information and provides appropriate advice to the driver. A smart driving app also provides driving advice to the driver in real time based on the surrounding environment data. For example, it suggests driving methods to prevent slipping in rainy weather and guides drivers to alternative routes in traffic jams. A smart driving app also analyzes data in real time to provide driving advice according to weather and traffic conditions and suggests optimal driving methods to the driver. For example, it provides points to note for safe driving in bad weather. This makes it possible to provide appropriate driving advice to the driver in real time and promote safe driving.

[0036] Smart driving apps can be made compatible with other modes of transportation (bicycles, electric scooters), providing opportunities for a wide range of users to improve their driving skills. Smart driving apps can be made compatible with not only automobiles, but also bicycles and electric scooters. For example, they can collect driving data appropriate for each mode of transportation and provide feedback to improve driving skills. Smart driving apps can also collect driving data for bicycles and electric scooters and provide feedback in real time. For example, they can analyze the number of sudden brakings and sudden accelerations, and the frequency of speeding, and alert the driver. Smart driving apps can also be made compatible with other modes of transportation, providing opportunities for a wide range of users to improve their driving skills. For example, they can provide driving scores to bicycle and electric scooter drivers and evaluate their driving skills. This can provide opportunities for a wide range of users to improve their driving skills and promote road safety.

[0037] A smart driving app may incorporate game elements and add a ranking or badge system that allows drivers to compete to improve their driving skills. For example, a smart driving app may incorporate game elements and add a ranking system that allows drivers to compete to improve their driving skills. For example, a ranking system may be displayed among drivers based on their driving scores, stimulating a competitive spirit. A smart driving app may also incorporate a badge system to promote the improvement of driving skills. For example, a badge may be awarded to drivers who achieve a specific driving skill, providing a sense of accomplishment. A smart driving app may also develop a system that incorporates game elements to allow drivers to have fun while improving their driving skills. For example, a system may be developed that provides drivers with level ups and rewards based on their driving scores, motivating them. This may increase driver motivation and promote the improvement of their driving skills.

[0038] In-vehicle sensors can collect biometric information from drivers and provide driving advice based on their health condition. In-vehicle sensors collect biometric information, such as the driver's heart rate and body temperature, in real time. For example, they measure the driver's stress level and fatigue level and provide driving advice based on their health condition. In addition, in-vehicle sensors can analyze the driver's health condition based on the biometric information and build a system that provides appropriate driving advice. For example, if the driver's heart rate is high, they can suggest a driving method that will help them relax. In addition, in-vehicle sensors can analyze the collected biometric information and provide driving advice based on the driver's health condition. For example, if the driver's body temperature is high, they can encourage them to take a break and suggest a driving method that will help them maintain their health. This makes it possible to understand the driver's health condition and provide appropriate driving advice.

[0039] On-board sensors can monitor the vehicle's maintenance status in real time and predict and notify the driver of required maintenance. On-board sensors, for example, monitor the status of each vehicle part in real time. For example, they constantly check the status of the engine and brakes and notify the driver if an abnormality is detected. On-board sensors can also build a system that monitors the vehicle's maintenance status and predicts required maintenance. For example, they can monitor oil changes and tire wear and suggest maintenance at the appropriate time. On-board sensors can also analyze collected data and monitor the vehicle's maintenance status in real time. For example, they can detect deterioration or abnormalities in parts early and notify the driver of the need for maintenance. This allows for appropriate vehicle maintenance and improves safety.

[0040] Onboard sensors can communicate with other vehicles and infrastructure to collect and analyze data to realize cooperative driving. For example, onboard sensors communicate with other vehicles and infrastructure to collect data to realize cooperative driving. For example, they share the distance and speed between vehicles in real time to maintain a safe distance between vehicles. Furthermore, by communicating with other vehicles and infrastructure, onboard sensors can build systems that analyze data to realize cooperative driving. For example, they can receive information from traffic lights and road signs and suggest appropriate driving methods to the driver. Furthermore, onboard sensors can develop systems that drive in cooperation with other vehicles and infrastructure based on the collected data. For example, they can share traffic congestion and accident information and suggest optimal routes. This can realize cooperative driving and improve safety and efficiency.

[0041] In-vehicle sensors can be linked with household appliances and smart home systems to automate home preparations based on the driver's arrival time. In-vehicle sensors can be linked with smart home systems to automate home preparations based on the driver's arrival time. For example, the air conditioner and lights can be automatically turned on based on the driver's arrival time. In-vehicle sensors can also predict the driver's arrival time and notify the smart home system. For example, the front door can be automatically unlocked when the driver approaches home. In-vehicle sensors can also be linked with smart home systems to build a system that prepares the home based on the driver's arrival time. For example, the bath can be heated before the driver arrives home. This automates home preparations based on the driver's arrival time, improving convenience.

[0042] In-vehicle sensors can use collected data to automatically adjust vehicle settings (seat position, mirror angle) to suit the driver's driving style. For example, in-vehicle sensors automatically adjust the seat position to suit the driver's driving style based on collected data. For example, they adjust the seat position to suit the driver's physique and posture. In-vehicle sensors also use collected data to automatically adjust the mirror angle to suit the driver's driving style. For example, they analyze the driver's line of sight and head movement to optimize the mirror angle. In-vehicle sensors also build a system that automatically adjusts vehicle settings to suit the driver's driving style based on collected data. For example, they automatically adjust the seat position and mirror angle to suit the driver's preferences. This allows vehicle settings to be automatically adjusted to suit the driver's driving style, providing a comfortable driving environment.

[0043] The driving score can evaluate the individual degree of growth by taking into account the driver's past driving history and learning progress. The driving score, for example, takes into account the driver's past driving history. For example, the degree of growth in driving skills is evaluated based on past driving data and reflected in the score. In addition, a system can be constructed to evaluate the individual degree of growth by taking into account the driver's learning progress. For example, the degree of growth is reflected in the score based on training data for improving driving skills. In addition, the driving score evaluates the driver's degree of growth based on the driver's past driving history and learning progress. For example, a driver whose driving skills are improving can receive a high score. This makes it possible to evaluate the driver's degree of growth and provide individual feedback.

[0044] The driving score can increase motivation to improve driving skills by comparing with other drivers and providing a relative evaluation. For example, a system can be constructed to compare driving scores with other drivers and provide a relative evaluation. For example, a ranking of driving scores can be displayed to stimulate a competitive spirit among drivers. The driving score can also increase motivation to improve driving skills based on comparison with other drivers. For example, rewards can be provided to drivers with high driving scores to increase motivation. The driving score can also be evaluated relatively and feedback can be provided to drivers. For example, positive feedback can be provided if a driver's driving skills are superior to those of other drivers. This can stimulate a competitive spirit among drivers and increase motivation to improve driving skills.

[0045] Driving Score can work with insurance companies and rental car companies to provide discounts on insurance premiums and rental fees based on driving scores. Driving Score, for example, works with insurance companies to build a system that provides discounts on insurance premiums based on driving scores. For example, it applies discounts on insurance premiums to drivers with high driving scores. Driving Score can also work with rental car companies to provide discounts on rental fees based on driving scores. For example, it applies discounts on rental fees to drivers with high driving scores. Driving Score can also develop a system that provides discounts on insurance premiums and rental fees based on driving scores. For example, it can provide rewards to drivers with high driving scores, increasing their motivation to improve their driving skills. This can increase drivers' motivation to improve their driving skills.

[0046] The driving score can provide a customized driving training program according to the driver's driving skill. The driving score, for example, builds a system that provides a customized driving training program according to the driver's driving skill. For example, basic training is provided to a driver with low driving skill. The driving score also customizes a training program according to the driver's driving skill based on the score. For example, training to improve a driver who lacks a specific driving skill is provided. The driving score also provides a training program according to the driver's driving skill. For example, more advanced training is provided to a driver with high driving skill to improve their skills. This can promote improvement of the driver's driving skill.

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

[0048] The driving skill improvement system can further include a health management unit that monitors the driver's health condition. For example, the health management unit measures the driver's heart rate and blood pressure in real time and urges the driver to take a break if an abnormality is detected. The health management unit can also provide driving advice based on the driver's health condition. For example, if the driver is tired, the health management unit advises the driver to take a break. This helps maintain the driver's health and promotes safe driving.

[0049] The driving skill improvement system can also provide customized training programs based on the driver's driving style. For example, it can analyze the driver's past driving data and suggest training to improve specific driving skills. By providing a training program that suits the driver's driving style, it can efficiently support the improvement of driving skills. This makes it possible to provide training that meets the individual needs of the driver and promote the improvement of driving skills.

[0050] The driving skill improvement system can also predict the vehicle's maintenance status based on the driver's driving data. For example, it can constantly monitor the condition of the engine and brakes and notify the driver if an abnormality is detected. It can also suggest maintenance at the appropriate time based on the vehicle's maintenance status. This allows for appropriate vehicle maintenance and improves safety.

[0051] The driving skill improvement system can also incorporate game elements to improve driving skills based on the driver's driving data. For example, it can display a ranking of drivers based on their driving scores, stimulating a competitive spirit. It can also award badges to drivers who achieve certain driving skills, providing a sense of accomplishment. This makes it fun to improve driving skills and increases the driver's motivation.

[0052] The driving skill improvement system can also be adapted to other modes of transportation (bicycles, electric scooters) based on the driver's driving data. For example, it can collect driving data appropriate for each mode of transportation and provide feedback to improve driving skills. It can also provide a driving score and evaluate driving skills based on bicycle and electric scooter driving data. This will provide opportunities for a wide range of users to improve their driving skills and promote road safety.

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

[0054] Step 1: The smart driving app is installed on the driver's smartphone, collects and analyzes driving data, and provides real-time feedback to the driver. For example, it analyzes the number of sudden braking and sudden acceleration, the frequency of speeding, etc., and warns the driver to be careful. It can also generate customized feedback tailored to the driver's driving style. Step 2: On-board sensors collect various vehicle data in real time and send it to the smart driving app, such as speed, acceleration, braking, and fuel consumption. Step 3: The generative AI analyzes data from the vehicle sensors and provides the driver with a driving score. For example, it calculates a driving score based on the driving data and suggests specific areas for improvement to the driver. Step 4: The driving score is used as an evaluation index of driving skills to encourage drivers to improve their skills. For example, a high score is given to drivers who brake and accelerate less frequently and speed less frequently. Step 5: Incentive programs provide rewards to drivers based on their driving scores. For example, drivers with high driving scores can receive perks such as fuel discounts or insurance discounts.

[0055] (Example 2) A driving skill improvement system according to an embodiment of the present invention provides real-time feedback to drivers, encouraging them to improve their driving skills and reduce CO2 emissions. This allows the driving skill improvement system to improve drivers' driving skills and reduce environmental impact.

[0056] A driving skill improvement system according to an embodiment includes a smart driving app, an on-board sensor, a generation AI, a driving score, and an incentive program. The smart driving app is installed on a driver's smartphone and collects and analyzes driving data to provide real-time feedback to the driver. For example, the smart driving app analyzes the number of sudden braking and sudden acceleration, the frequency of speeding, and other factors to alert the driver. The smart driving app can also generate customized feedback tailored to the driver's driving style. The on-board sensor collects various vehicle data in real time and transmits it to the smart driving app. For example, the on-board sensor collects data such as speed, acceleration, brake usage, and fuel consumption. The generation AI analyzes the data from the on-board sensor and provides the driver with a driving score. For example, the generation AI calculates a driving score based on the driving data and suggests specific areas for improvement to the driver. The driving score is used as an evaluation index of driving skill and promotes the improvement of the driver's skills. For example, the driving score may be higher for drivers who rarely brake or accelerate suddenly and rarely exceed the speeding limit. An incentive program is a mechanism for providing rewards to drivers based on their driving scores. For example, drivers who achieve high driving scores are provided with benefits such as fuel discounts and insurance discounts. As a result, the driving skill improvement system according to the embodiment can improve drivers' driving skills and reduce environmental impact. For example, if drivers reduce sudden braking and sudden acceleration, fuel efficiency improves and CO2 emissions are reduced. Furthermore, an incentive program based on driving scores can increase drivers' motivation to improve their driving skills.

[0057] A smart driving app can analyze the number of sudden braking and sudden acceleration, the frequency of speeding, and other factors to warn the driver. For example, a smart driving app can analyze the number of sudden braking and sudden acceleration to warn the driver. For example, if there are many sudden braking incidents, the app can provide advice to the driver to improve how they use the brakes. A smart driving app can also analyze the frequency of speeding incidents to warn the driver. For example, if there are many speeding incidents, the app can provide advice to the driver to maintain the speed limit. This can promote safe driving by drivers.

[0058] The generation AI can suggest specific areas for improvement to the driver based on the driving score. The generation AI can suggest specific areas for improvement to the driver based on the driving score. For example, if the driving score is low, the generation AI can provide advice to the driver to reduce sudden braking and sudden acceleration. The generation AI can also provide advice to the driver to reduce speeding based on the driving score. For example, if the frequency of speeding is high, the generation AI can provide advice to the driver to maintain the speed limit. This can promote improvement of the driver's driving skills.

[0059] The incentive program can provide benefits such as fuel discounts and insurance discounts to drivers who achieve a high driving score. For example, the incentive program provides a fuel discount to drivers who achieve a high driving score. For example, a fuel discount coupon is provided to drivers who achieve a driving score of 90 points or higher. The incentive program also provides an insurance discount to drivers who achieve a high driving score. For example, an insurance discount is provided to drivers who achieve a driving score of 80 points or higher. This can increase the motivation of drivers to improve their driving skills.

[0060] The generating AI can analyze driving data and make specific suggestions to promote the reduction of CO2 emissions. The generating AI can, for example, analyze driving data and make specific suggestions to promote the reduction of CO2 emissions. For example, the generating AI can recommend eco-driving based on the driving data. For example, reducing sudden braking and sudden acceleration can improve fuel efficiency and reduce CO2 emissions. The generating AI can also suggest driving methods to improve fuel efficiency based on the driving data. For example, maintaining a constant speed can improve fuel efficiency and reduce CO2 emissions. This makes it possible to achieve environmentally friendly driving.

[0061] A smart driving app is equipped with an emotion estimation function, which can analyze the driver's emotional state in real time and provide driving advice to reduce stress and fatigue. For example, a smart driving app is equipped with an emotion estimation function and analyzes the driver's facial expressions and voice to grasp the driver's emotional state in real time. For example, a camera and microphone can be used to measure the driver's stress level and fatigue level and provide appropriate driving advice. A smart driving app also uses the emotion estimation function to provide driving advice based on the driver's emotional state. For example, if the driver is highly stressed, the app can suggest breathing techniques or a break to help them relax, and if fatigue is apparent, the app can encourage them to take a break. A smart driving app can also analyze the driver's emotional state and automatically select music or podcasts to reduce stress and fatigue and play them while driving. For example, the app can select relaxing music to soothe the driver's mood. This can reduce the driver's stress and fatigue and promote safe driving.

[0062] A smart driving app can learn a driver's past driving data and generate customized feedback tailored to their individual driving style. For example, a smart driving app collects a driver's past driving data and learns their driving style. For example, it analyzes the frequency of sudden braking and sudden acceleration, and tendencies toward speeding, and provides feedback tailored to their individual driving style. Furthermore, a smart driving app generates customized feedback that is optimal for the driver's driving style based on the past driving data. For example, it identifies common mistakes and areas for improvement and provides specific advice. Furthermore, to provide customized feedback tailored to the driver's driving style, a generation AI analyzes past driving data and generates advice tailored to the driver's characteristics. For example, it provides feedback that takes into account the driver's habits and patterns. This allows the app to provide feedback tailored to the driver's individual driving style and promote improvement of driving skills.

[0063] A smart driving app can analyze the surrounding environment while driving in real time and provide driving advice based on that analysis. For example, a smart driving app collects and analyzes surrounding environment data (weather, traffic conditions) while driving in real time. For example, it obtains weather forecasts and traffic information and provides appropriate advice to the driver. A smart driving app also provides driving advice to the driver in real time based on the surrounding environment data. For example, it suggests driving methods to prevent slipping in rainy weather and guides drivers to alternative routes in traffic jams. A smart driving app also analyzes data in real time to provide driving advice according to weather and traffic conditions and suggests optimal driving methods to the driver. For example, it provides points to note for safe driving in bad weather. This makes it possible to provide appropriate driving advice to the driver in real time and promote safe driving.

[0064] Smart driving apps can be made compatible with other modes of transportation (bicycles, electric scooters), providing opportunities for a wide range of users to improve their driving skills. Smart driving apps can be made compatible with not only automobiles, but also bicycles and electric scooters. For example, they can collect driving data appropriate for each mode of transportation and provide feedback to improve driving skills. Smart driving apps can also collect driving data for bicycles and electric scooters and provide feedback in real time. For example, they can analyze the number of sudden brakings and sudden accelerations, and the frequency of speeding, and alert the driver. Smart driving apps can also be made compatible with other modes of transportation, providing opportunities for a wide range of users to improve their driving skills. For example, they can provide driving scores to bicycle and electric scooter drivers and evaluate their driving skills. This can provide opportunities for a wide range of users to improve their driving skills and promote road safety.

[0065] A smart driving app may incorporate game elements and add a ranking or badge system that allows drivers to compete to improve their driving skills. For example, a smart driving app may incorporate game elements and add a ranking system that allows drivers to compete to improve their driving skills. For example, a ranking system may be displayed among drivers based on their driving scores, stimulating a competitive spirit. A smart driving app may also incorporate a badge system to promote the improvement of driving skills. For example, a badge may be awarded to drivers who achieve a specific driving skill, providing a sense of accomplishment. A smart driving app may also develop a system that incorporates game elements to allow drivers to have fun while improving their driving skills. For example, a system may be developed that provides drivers with level ups and rewards based on their driving scores, motivating them. This may increase driver motivation and promote the improvement of their driving skills.

[0066] A smart driving app can use an emotion estimation function to automatically select relaxing music or podcasts for the driver and play them while driving. For example, a smart driving app equipped with an emotion estimation function can analyze the driver's emotional state and automatically select relaxing music. For example, if the driver's stress level is high, it can play relaxing music. The smart driving app also uses the emotion estimation function to automatically select podcasts according to the driver's emotional state and play them while driving. For example, if the driver is tired, it can provide a podcast with refreshing content. Furthermore, a smart driving app will develop a system that analyzes the driver's emotional state in real time and automatically selects relaxing music or podcasts. For example, it can generate a music playlist that matches the driver's mood and play it while driving. This can reduce driver stress and provide a relaxing driving environment.

[0067] In-vehicle sensors can collect biometric information from drivers and provide driving advice based on their health condition. In-vehicle sensors collect biometric information, such as the driver's heart rate and body temperature, in real time. For example, they measure the driver's stress level and fatigue level and provide driving advice based on their health condition. In addition, in-vehicle sensors can analyze the driver's health condition based on the biometric information and build a system that provides appropriate driving advice. For example, if the driver's heart rate is high, they can suggest a driving method that will help them relax. In addition, in-vehicle sensors can analyze the collected biometric information and provide driving advice based on the driver's health condition. For example, if the driver's body temperature is high, they can encourage them to take a break and suggest a driving method that will help them maintain their health. This makes it possible to understand the driver's health condition and provide appropriate driving advice.

[0068] On-board sensors can monitor the vehicle's maintenance status in real time and predict and notify the driver of required maintenance. On-board sensors, for example, monitor the status of each vehicle part in real time. For example, they constantly check the status of the engine and brakes and notify the driver if an abnormality is detected. On-board sensors can also build a system that monitors the vehicle's maintenance status and predicts required maintenance. For example, they can monitor oil changes and tire wear and suggest maintenance at the appropriate time. On-board sensors can also analyze collected data and monitor the vehicle's maintenance status in real time. For example, they can detect deterioration or abnormalities in parts early and notify the driver of the need for maintenance. This allows for appropriate vehicle maintenance and improves safety.

[0069] Onboard sensors can communicate with other vehicles and infrastructure to collect and analyze data to realize cooperative driving. For example, onboard sensors communicate with other vehicles and infrastructure to collect data to realize cooperative driving. For example, they share the distance and speed between vehicles in real time to maintain a safe distance between vehicles. Furthermore, by communicating with other vehicles and infrastructure, onboard sensors can build systems that analyze data to realize cooperative driving. For example, they can receive information from traffic lights and road signs and suggest appropriate driving methods to the driver. Furthermore, onboard sensors can develop systems that drive in cooperation with other vehicles and infrastructure based on the collected data. For example, they can share traffic congestion and accident information and suggest optimal routes. This can realize cooperative driving and improve safety and efficiency.

[0070] In-vehicle sensors can be linked with household appliances and smart home systems to automate home preparations based on the driver's arrival time. In-vehicle sensors can be linked with smart home systems to automate home preparations based on the driver's arrival time. For example, the air conditioner and lights can be automatically turned on based on the driver's arrival time. In-vehicle sensors can also predict the driver's arrival time and notify the smart home system. For example, the front door can be automatically unlocked when the driver approaches home. In-vehicle sensors can also be linked with smart home systems to build a system that prepares the home based on the driver's arrival time. For example, the bath can be heated before the driver arrives home. This automates home preparations based on the driver's arrival time, improving convenience.

[0071] In-vehicle sensors can use collected data to automatically adjust vehicle settings (seat position, mirror angle) to suit the driver's driving style. For example, in-vehicle sensors automatically adjust the seat position to suit the driver's driving style based on collected data. For example, they adjust the seat position to suit the driver's physique and posture. In-vehicle sensors also use collected data to automatically adjust the mirror angle to suit the driver's driving style. For example, they analyze the driver's line of sight and head movement to optimize the mirror angle. In-vehicle sensors also build a system that automatically adjusts vehicle settings to suit the driver's driving style based on collected data. For example, they automatically adjust the seat position and mirror angle to suit the driver's preferences. This allows vehicle settings to be automatically adjusted to suit the driver's driving style, providing a comfortable driving environment.

[0072] Using an emotion estimation function, an in-vehicle sensor can automatically adjust the in-vehicle environment (lighting, air conditioning) according to the driver's emotional state. For example, an in-vehicle sensor equipped with an emotion estimation function analyzes the driver's emotional state and automatically adjusts the in-vehicle environment. For example, if the driver is feeling stressed, the lighting is changed to a softer color and the air conditioning is set to a comfortable temperature. Furthermore, the in-vehicle sensor uses the emotion estimation function to build a system that automatically adjusts the in-vehicle environment according to the driver's emotional state. For example, if the driver is relaxed, the lighting is dimmed and the air conditioning is set to a temperature that promotes relaxation. Furthermore, the in-vehicle sensor automatically adjusts the in-vehicle environment according to the driver's emotional state based on the collected emotion data. For example, if the driver is tired, the air conditioning is cooled and the lighting is brightened to increase the wakefulness effect. This provides an in-vehicle environment according to the driver's emotional state, realizing a comfortable driving environment.

[0073] The driving score can provide a more comprehensive evaluation of driving skills by incorporating an emotion estimation function and reflecting the driver's emotional state in the evaluation. For example, the driving score can incorporate an emotion estimation function to reflect the driver's emotional state in the evaluation. For example, a driver who is less stressed and fatigued can receive a higher score. Furthermore, a system can be constructed that uses the emotion estimation function to reflect the driver's emotional state in the driving score. For example, a driver who is relaxed while driving can receive a higher score. Furthermore, the driving score can incorporate emotion data to comprehensively evaluate the driver's emotional state. For example, a driver who has positive emotions while driving can receive a higher score. This makes it possible to provide a comprehensive evaluation of driving skills that takes the driver's emotional state into consideration.

[0074] The driving score can evaluate the individual degree of growth by taking into account the driver's past driving history and learning progress. The driving score, for example, takes into account the driver's past driving history. For example, the degree of growth in driving skills is evaluated based on past driving data and reflected in the score. In addition, a system can be constructed to evaluate the individual degree of growth by taking into account the driver's learning progress. For example, the degree of growth is reflected in the score based on training data for improving driving skills. In addition, the driving score evaluates the driver's degree of growth based on the driver's past driving history and learning progress. For example, a driver whose driving skills are improving can receive a high score. This makes it possible to evaluate the driver's degree of growth and provide individual feedback.

[0075] The driving score can increase motivation to improve driving skills by comparing with other drivers and providing a relative evaluation. For example, a system can be constructed to compare driving scores with other drivers and provide a relative evaluation. For example, a ranking of driving scores can be displayed to stimulate a competitive spirit among drivers. The driving score can also increase motivation to improve driving skills based on comparison with other drivers. For example, rewards can be provided to drivers with high driving scores to increase motivation. The driving score can also be evaluated relatively and feedback can be provided to drivers. For example, positive feedback can be provided if a driver's driving skills are superior to those of other drivers. This can stimulate a competitive spirit among drivers and increase motivation to improve driving skills.

[0076] Driving Score can work with insurance companies and rental car companies to provide discounts on insurance premiums and rental fees based on driving scores. Driving Score, for example, works with insurance companies to build a system that provides discounts on insurance premiums based on driving scores. For example, it applies discounts on insurance premiums to drivers with high driving scores. Driving Score can also work with rental car companies to provide discounts on rental fees based on driving scores. For example, it applies discounts on rental fees to drivers with high driving scores. Driving Score can also develop a system that provides discounts on insurance premiums and rental fees based on driving scores. For example, it can provide rewards to drivers with high driving scores, increasing their motivation to improve their driving skills. This can increase drivers' motivation to improve their driving skills.

[0077] The driving score can provide a customized driving training program according to the driver's driving skill. The driving score, for example, builds a system that provides a customized driving training program according to the driver's driving skill. For example, basic training is provided to a driver with low driving skill. The driving score also customizes a training program according to the driver's driving skill based on the score. For example, training to improve a driver who lacks a specific driving skill is provided. The driving score also provides a training program according to the driver's driving skill. For example, more advanced training is provided to a driver with high driving skill to improve their skills. This can promote improvement of the driver's driving skill.

[0078] The emotion estimation function can provide emotional feedback based on the driving score to improve the driver's motivation. The emotion estimation function, for example, builds a system that provides emotional feedback based on the driving score. For example, positive feedback is provided to drivers with high driving scores to improve their motivation. The emotion estimation function also analyzes the driver's emotional state based on the driving score and provides appropriate feedback. For example, an encouraging message is provided if the driving score is low to maintain motivation. The emotion estimation function also provides emotional feedback based on the driving score. For example, words of praise are provided to drivers with high driving scores to increase their motivation to improve their driving skills. This can increase the driver's motivation and promote improvement of their driving skills.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The driving skill improvement system can further include a health management unit that monitors the driver's health condition. For example, the health management unit measures the driver's heart rate and blood pressure in real time and urges the driver to take a break if an abnormality is detected. The health management unit can also provide driving advice based on the driver's health condition. For example, if the driver is tired, the health management unit advises the driver to take a break. This helps maintain the driver's health and promotes safe driving.

[0081] The driving skill improvement system can also analyze the driver's emotional state and provide feedback based on the driver's emotions. For example, if the driver feels stressed, the system can provide advice on how to relax. If the driver feels tired, the system can encourage the driver to take a break. This provides feedback that takes into account the driver's emotional state and promotes the improvement of driving skills.

[0082] The driving skill improvement system can also provide customized training programs based on the driver's driving style. For example, it can analyze the driver's past driving data and suggest training to improve specific driving skills. By providing a training program that suits the driver's driving style, it can efficiently support the improvement of driving skills. This makes it possible to provide training that meets the individual needs of the driver and promote the improvement of driving skills.

[0083] The driving skill improvement system can also analyze the driver's emotional state and provide music or podcasts based on the driver's emotions. For example, if the driver is feeling stressed, it can play relaxing music. If the driver is tired, it can provide a refreshing podcast. This provides entertainment that takes the driver's emotional state into account, reducing stress while driving.

[0084] The driving skill improvement system can also predict the vehicle's maintenance status based on the driver's driving data. For example, it can constantly monitor the condition of the engine and brakes and notify the driver if an abnormality is detected. It can also suggest maintenance at the appropriate time based on the vehicle's maintenance status. This allows for appropriate vehicle maintenance and improves safety.

[0085] The driving skill improvement system can also analyze the driver's emotional state and provide driving advice based on the driver's emotions. For example, if the driver is feeling stressed, it can suggest breathing techniques or taking a break to relax. If the driver is tired, it can encourage the driver to take a break. This allows the system to provide driving advice that takes the driver's emotional state into consideration and promote safe driving.

[0086] The driving skill improvement system can also incorporate game elements to improve driving skills based on the driver's driving data. For example, it can display a ranking of drivers based on their driving scores, stimulating a competitive spirit. It can also award badges to drivers who achieve certain driving skills, providing a sense of accomplishment. This makes it fun to improve driving skills and increases the driver's motivation.

[0087] The driving skill improvement system can also analyze the driver's emotional state and provide a driving score based on the driver's emotions. For example, a driver who is less stressed and fatigued can receive a higher score. A driver who is relaxed while driving can also receive a higher score. This allows for a comprehensive driving skill evaluation that takes into account the driver's emotional state.

[0088] The driving skill improvement system can also be adapted to other modes of transportation (bicycles, electric scooters) based on the driver's driving data. For example, it can collect driving data appropriate for each mode of transportation and provide feedback to improve driving skills. It can also provide a driving score and evaluate driving skills based on bicycle and electric scooter driving data. This will provide opportunities for a wide range of users to improve their driving skills and promote road safety.

[0089] The driving skill improvement system can also analyze the driver's emotional state and provide feedback based on the driver's emotions. For example, a driver with a high driving score can be given positive feedback to increase their motivation. On the other hand, a driver with a low driving score can be given an encouraging message to maintain their motivation. This allows the system to provide feedback that takes into account the driver's emotional state and increase their motivation to improve their driving skills.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The smart driving app is installed on the driver's smartphone, collects and analyzes driving data, and provides real-time feedback to the driver. For example, it analyzes the number of sudden braking and sudden acceleration, the frequency of speeding, etc., and warns the driver to be careful. It can also generate customized feedback tailored to the driver's driving style. Step 2: On-board sensors collect various vehicle data in real time and send it to the smart driving app, such as speed, acceleration, braking, and fuel consumption. Step 3: The generative AI analyzes data from the vehicle sensors and provides the driver with a driving score. For example, it calculates a driving score based on the driving data and suggests specific areas for improvement to the driver. Step 4: The driving score is used as an evaluation index of driving skills to encourage drivers to improve their skills. For example, a high score is given to drivers who brake and accelerate less frequently and speed less frequently. Step 5: Incentive programs provide rewards to drivers based on their driving scores. For example, drivers with high driving scores can receive perks such as fuel discounts or insurance discounts.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Smart driving app and In-vehicle sensors and Generative AI and Driving score and an incentive program; The smart driving app is It is installed on the driver's smartphone, collects and analyzes data while driving, and provides feedback to the driver in real time. The on-board sensor is Various vehicle data is collected in real time and sent to the smart driving app. The generated AI is Data from the on-board sensors is analyzed to provide a driving score to the driver. The driving score is It is used as an evaluation indicator of driving technique and promotes the improvement of drivers' skills. The incentive program: A system that provides rewards to drivers based on the driving score. A system characterized by:

2. The smart driving app is Analyzes the number of sudden braking and acceleration, the frequency of speeding, etc., and warns the driver 2. The system of claim 1.

3. The generated AI is Based on the driving score, specific improvements are suggested to the driver.

2. The system of claim 1.

4. The incentive program: Providing benefits such as fuel discounts and insurance discounts to the driver who has achieved the driving score 2. The system of claim 1.

5. The generated AI is Analyze driving data and make specific proposals to promote CO2 emission reductions 2. The system of claim 1.

6. The smart driving app is Analyzes the surrounding environment in real time while driving and provides driving advice based on that analysis 2. The system of claim 1.

7. The smart driving app is Learns the driver's past driving data and generates customized feedback tailored to the individual's driving style 2. The system of claim 1.

8. The smart driving app is Analyzes the surrounding environment in real time while driving and provides driving advice based on that analysis 2. The system of claim 1.

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