System

Abstract

An object of a system according to an embodiment is to analyze driving data and an insurance history and recommend an optimal insurance plan to a user.SOLUTION: A system according to an embodiment includes a driving data collection unit, an insurance history collection unit, an analysis unit, and a recommendation unit. The operation data collection unit collects operation data. The insurance history collection unit collects an insurance history. The analysis unit analyzes the data collected by the driving data collection unit and the insurance history collection unit. The recommendation unit recommends an optimum insurance plan to the user on the basis of a result analyzed by the analysis unit.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] Conventional technologies have not yet effectively utilized driving data and insurance history to provide users with optimal insurance plans, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze driving data and insurance history and recommend the most suitable insurance plan to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a driving data collection unit, an insurance history collection unit, an analysis unit, and a recommendation unit. The driving data collection unit collects driving data. The insurance history collection unit collects insurance histories. The analysis unit analyzes the data collected by the driving data collection unit and the insurance history collection unit. The recommendation unit recommends an optimal insurance plan to the user based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze driving data and insurance history and recommend the most suitable insurance plan to the user. [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) The SmartDrive system according to an embodiment of the present invention is a system that uses AI to perform sophisticated analysis of automobile driving data and insurance history, and recommends the most suitable insurance plan for the user. As a result, the SmartDrive system can provide the most suitable insurance plan for the user, contributing to reducing insurance premiums and promoting safe driving.

[0029] The SmartDrive system according to the embodiment includes a driving data collection unit, an insurance history collection unit, an analysis unit, and a recommendation unit. The driving data collection unit collects driving data, such as driving speed, number of sudden braking attempts, and mileage. The driving data collection unit can also collect driving data in real time using on-board sensors and GPS. The driving data collection unit can also collect driving data through a user's smartphone app. The insurance history collection unit collects insurance history, such as past insurance contract information and insurance claim history. The insurance history collection unit can also acquire insurance history by linking with an insurance company's database. The insurance history collection unit can also collect insurance history information provided by the user. The analysis unit analyzes the data collected by the driving data collection unit and the insurance history collection unit. For example, the generation AI analyzes the correlation between driving data and insurance history and evaluates the impact of the user's driving habits on the insurance history. The generation AI can also evaluate the user's driving style based on the driving data. The generation AI can also select the optimal insurance plan for the user based on the insurance history. The recommendation unit recommends an optimal insurance plan to the user based on the results of the analysis by the analysis unit. For example, the generation AI proposes an optimal insurance plan based on the user's driving style and insurance history. The recommendation unit can also provide detailed information about insurance plans to the user. Furthermore, the recommendation unit can also support the user in selecting an insurance plan. This allows the SmartDrive system according to the embodiment to provide an optimal insurance plan to the user. For example, the user can select an optimal insurance plan based on their driving style and insurance history. The user can also enjoy benefits such as reduced insurance premiums and improved coverage. Furthermore, the insurance company can perform risk assessment based on the user's driving data and insurance history and provide an appropriate insurance plan.

[0030] The analysis unit can analyze at least one of driving data including driving speed, number of sudden braking incidents, and mileage to evaluate the user's driving style. For example, the analysis unit uses generative AI to analyze driving data such as driving speed, number of sudden braking incidents, and mileage, as well as past insurance claim history, to evaluate the impact of driving habits on insurance claims. For example, it analyzes whether drivers who frequently brake suddenly tend to file more insurance claims. The analysis unit also combines the driving data and insurance history to evaluate whether specific driving patterns increase the risk of insurance claims. For example, it analyzes whether users who frequently drive at night are more likely to file more insurance claims. The analysis unit also performs a detailed evaluation of the impact of driving habits on insurance premiums based on the user's driving data and insurance history. For example, it analyzes whether users who drive safely are more likely to receive insurance discounts. This allows for a detailed evaluation of the user's driving style.

[0031] The analysis unit can analyze accident rates in specific areas or time periods and notify users of high-risk areas or time periods. For example, the analysis unit uses generative AI to analyze driving data in real time and evaluate the user's driving style. For example, it can monitor the frequency of sudden braking and sudden acceleration in real time and provide immediate feedback. The analysis unit can also evaluate the user's driving style in real time based on driving data and provide advice to improve driving behavior. For example, if there are frequent sudden braking incidents, the analysis unit can provide advice such as "Keep a sufficient distance from the vehicle in front to avoid sudden braking." The analysis unit can also analyze driving data in real time and build a system to evaluate the user's driving style. For example, the analysis unit can display the evaluation results of the driving style based on driving data on a smartphone or in-car display. This can notify users of high-risk areas and time periods.

[0032] The analysis unit can provide safe driving advice to a user who frequently brakes suddenly. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotional state while driving and evaluate the impact of stress and fatigue on driving. For example, the emotion estimation function analyzes the user's facial expression and voice while driving to evaluate the state of stress and fatigue. For example, the emotion estimation function calculates an emotion score from changes in facial expression and tone of voice to analyze the impact on driving. The analysis unit also monitors the emotional state while driving in real time and evaluates the impact of stress and fatigue on driving behavior. For example, the analysis unit analyzes the frequency of sudden braking and sudden acceleration under high stress conditions. The analysis unit also uses the emotion estimation function to analyze the emotional state while driving and evaluate the impact of stress and fatigue on insurance claims. For example, the analysis unit analyzes whether driving under high stress conditions increases the risk of insurance claims. This makes it possible to provide the user with safe driving advice.

[0033] The analysis unit can apply insurance premium discounts to users who drive with low risk. The analysis unit also collects driving data from other means of transportation, such as bicycles and electric scooters, and proposes a comprehensive insurance plan. For example, the generation AI collects driving data from bicycles and electric scooters and evaluates the user's overall driving style. For example, it analyzes the distance traveled and speed of bicycles, and the frequency of use of electric scooters, and proposes a comprehensive insurance plan. The analysis unit also builds a system that proposes the optimal insurance plan for users based on driving data from other means of transportation. For example, it combines driving data from cars and bicycles to perform a comprehensive risk assessment. The analysis unit also collects driving data from bicycles and electric scooters and proposes a comprehensive insurance plan. For example, it integrates driving data from cars and other means of transportation and recommends the optimal insurance plan for the user. This makes it possible to apply insurance premium discounts to users who drive with low risk.

[0034] The analysis unit can provide incentives to improve driving behavior based on the user's driving data. For example, the analysis unit integrates the driving data of all family members and proposes the optimal insurance plan for the entire family. For example, the generation AI builds a system that collects and comprehensively analyzes the driving data of all family members. For example, it proposes the optimal insurance plan for the entire family based on each member's driving style and risk assessment. The analysis unit also performs a comprehensive risk assessment based on the driving data of all family members and proposes the optimal insurance plan for the entire family. For example, it analyzes the driving data of all family members and recommends an insurance plan based on low-risk members. The analysis unit also develops a system that integrates the driving data of all family members and proposes the optimal insurance plan for the entire family. For example, it performs a comprehensive risk assessment based on the driving data of all family members and customizes the insurance plan. This makes it possible to provide incentives to improve the user's driving behavior.

[0035] The analysis unit can predict the risk of traffic accidents by taking into account at least one external factor, either weather or road conditions. The analysis unit, for example, uses generative AI to analyze weather data and road condition data and predict the risk of traffic accidents. For example, it evaluates the risk of accidents in rainy weather or on snowy roads and notifies the user. The analysis unit also builds a risk prediction system that takes into account weather and road conditions. For example, it predicts the risk of accidents in specific areas or time periods based on real-time weather data. The analysis unit also makes risk predictions that take into account external factors and notifies the user of high-risk areas and time periods. For example, it evaluates risk based on information about road construction and traffic congestion. This makes it possible to predict the risk of traffic accidents by taking into account external factors such as weather and road conditions.

[0036] The analysis unit can perform a detailed analysis of the impact of specific driving patterns on accident risk based on past accident data. The analysis unit, for example, analyzes past accident data and evaluates the impact of specific driving patterns on accident risk. For example, it analyzes the accident risk of drivers who frequently brake suddenly or accelerate suddenly. The analysis unit also uses generative AI to analyze specific driving patterns based on past accident data and predict accident risk. For example, it evaluates the accident risk of users who frequently drive at night. The analysis unit also builds a system that performs a detailed analysis of the impact of specific driving patterns on accident risk based on past accident data. For example, it evaluates the accident risk at specific speed ranges. This makes it possible to perform a detailed analysis of the impact of specific driving patterns on accident risk based on past accident data.

[0037] The analysis unit collects location information of pedestrians or bicycles and can perform comprehensive traffic risk predictions. The analysis unit, for example, collects location information of pedestrians and bicycles and builds a system that performs comprehensive traffic risk predictions. For example, it evaluates the risk of accidents in areas with a high number of pedestrians. The analysis unit also performs comprehensive traffic risk predictions based on the location information of pedestrians and bicycles. For example, it analyzes the accident risk during times when there are a high number of pedestrians or bicycles. The analysis unit also collects location information of pedestrians and bicycles and develops a system that performs comprehensive traffic risk predictions. For example, it monitors the movements of pedestrians and bicycles in real time and predicts the risk of accidents. This makes it possible to collect location information of pedestrians and bicycles and perform comprehensive traffic risk predictions.

[0038] The analysis unit can display the risk prediction results on the user's smartphone or in-vehicle display in real time. The analysis unit, for example, builds a system that displays the risk prediction results on the user's smartphone or in-vehicle display in real time. For example, it displays high-risk areas and time periods on a map. The analysis unit also displays the risk prediction results in real time and notifies the user of high-risk areas and time periods. For example, it displays a warning using the smartphone's notification function. The analysis unit also develops a system that displays the risk prediction results in real time and provides the user with information to avoid risks. For example, it displays the risk prediction results on an in-vehicle display to urge caution while driving. This allows the risk prediction results to be displayed in real time on the user's smartphone or in-vehicle display.

[0039] The analysis unit can analyze the user's driving data in detail and provide individually customized safe driving advice. The analysis unit can, for example, use a generation AI to analyze the user's driving data in detail and provide individually customized safe driving advice. For example, a user who brakes suddenly frequently can be given specific advice on how to avoid braking suddenly. The analysis unit can also build a system that provides individually customized safe driving advice based on the user's driving data. For example, advice can be provided based on driving speed and mileage. The analysis unit can also use a generation AI to analyze the user's driving data in detail and provide individually customized safe driving advice. For example, specific advice can be provided based on driving style. This makes it possible to analyze the user's driving data in detail and provide individually customized safe driving advice.

[0040] The analysis unit can provide immediate advice based on real-time data obtained while driving, thereby improving driving behavior. The analysis unit, for example, builds a system that provides immediate advice based on real-time data obtained while driving. For example, if sudden braking or sudden acceleration is detected, immediate advice is provided. The analysis unit also analyzes driving data in real time and provides immediate advice. For example, if the distance to the vehicle ahead is close while driving, the analysis unit advises the driver to maintain a safe distance. The analysis unit also develops a system that provides immediate advice based on real-time data obtained while driving, thereby improving driving behavior. For example, the analysis unit notifies the driver in real time of areas to improve their driving style based on data obtained while driving. This makes it possible to provide immediate advice based on real-time data obtained while driving, thereby improving driving behavior.

[0041] The analysis unit can collect driving data of bicycles and electric scooters and provide safe driving advice. The analysis unit, for example, collects driving data of bicycles and electric scooters and builds a system that provides safe driving advice. For example, advice is provided based on the distance traveled and speed of the bicycle. The analysis unit also provides safe driving advice based on driving data of other means of transportation. For example, advice is provided based on the frequency of use and driving style of the electric scooter. The analysis unit also collects driving data of bicycles and electric scooters and develops a system that provides safe driving advice. For example, driving data of automobiles and other means of transportation is integrated to provide comprehensive safe driving advice. This makes it possible to collect driving data of bicycles and electric scooters and provide safe driving advice.

[0042] The analysis unit can collect driving data of all family members and provide safe driving advice to the entire family. The analysis unit, for example, builds a system that collects driving data of all family members and analyzes it in an integrated manner. For example, it provides safe driving advice to the entire family based on the driving style and risk assessment of each member. The analysis unit also performs a comprehensive risk assessment based on the driving data of all family members and provides safe driving advice to the entire family. For example, it analyzes the driving data of all family members and recommends a low-risk driving style. The analysis unit also develops a system that integrates the driving data of all family members and provides safe driving advice to the entire family. For example, it performs a comprehensive risk assessment based on the driving data of all family members and customizes safe driving advice. In this way, it is possible to collect driving data of all family members and provide safe driving advice to the entire family.

[0043] The analysis unit can provide individually customized insurance premium discounts based on the user's driving data and risk prediction results. The analysis unit, for example, uses generation AI to build a system that provides individually customized insurance premium discounts based on the user's driving data and risk prediction results. For example, it applies an insurance premium discount to users who drive safely. The analysis unit also provides individually customized insurance premium discounts based on the user's driving data and risk prediction results. For example, it applies an insurance premium discount to users who drive at a low risk. The analysis unit also uses generation AI to develop a system that provides individually customized insurance premium discounts based on the user's driving data and risk prediction results. For example, it provides an insurance premium discount according to driving style. This makes it possible to provide individually customized insurance premium discounts based on the user's driving data and risk prediction results.

[0044] The analysis unit provides an incentive to improve the user's driving behavior based on the driving data, thereby reducing insurance premiums. The analysis unit, for example, builds a system that provides an incentive to improve the user's driving behavior based on the driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides an incentive to improve the user's driving behavior based on the user's driving data. For example, an insurance premium discount is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides an incentive to improve the user's driving behavior based on the driving data and reduces insurance premiums. For example, an insurance premium discount is provided to users who drive safely. In this way, an incentive to improve the user's driving behavior based on the driving data is provided, thereby reducing insurance premiums.

[0045] The analysis unit can collect driving data of bicycles and electric scooters and provide insurance premium discounts. The analysis unit, for example, collects driving data of bicycles and electric scooters and builds a system that provides insurance premium discounts. For example, it provides insurance premium discounts based on bicycle mileage and speed. The analysis unit also provides insurance premium discounts based on driving data of other means of transportation. For example, it provides insurance premium discounts based on the frequency of use and driving style of electric scooters. The analysis unit also collects driving data of bicycles and electric scooters and develops a system that provides insurance premium discounts. For example, it integrates driving data of automobiles and other means of transportation to provide comprehensive insurance premium discounts. This makes it possible to collect driving data of bicycles and electric scooters and provide insurance premium discounts.

[0046] The analysis unit collects driving data of all family members and can provide insurance premium discounts for the entire family. The analysis unit, for example, builds a system that collects driving data of all family members and analyzes it comprehensively. For example, based on the driving style and risk assessment of each member, provides an insurance premium discount for the entire family. The analysis unit also performs a comprehensive risk assessment based on the driving data of all family members and provides an insurance premium discount for the entire family. For example, the analysis unit analyzes the driving data of all family members and recommends a low-risk driving style. The analysis unit also develops a system that integrates driving data of all family members and provides an insurance premium discount for the entire family. For example, based on the driving data of all family members, a comprehensive risk assessment is performed and an insurance premium discount is customized. In this way, it is possible to collect driving data of all family members and provide an insurance premium discount for the entire family.

[0047] The analysis unit can analyze the revenue management situation in detail and propose the optimal revenue distribution method. The analysis unit, for example, uses generation AI to build a system that analyzes the revenue management situation in detail and proposes the optimal revenue distribution method. For example, it calculates the optimal distribution method based on revenue data. The analysis unit also analyzes the revenue management situation in detail and proposes the optimal revenue distribution method. For example, it proposes the optimal method for returning a portion of the revenue to users. The analysis unit also uses generation AI to develop a system that analyzes the revenue management situation in detail and proposes the optimal revenue distribution method. For example, it calculates the optimal distribution method for returning a portion of the revenue to users based on revenue data. This makes it possible to analyze the revenue management situation in detail and propose the optimal revenue distribution method.

[0048] The analysis unit can provide a revenue return incentive based on the user's driving data to promote safe driving. The analysis unit, for example, builds a system that provides a revenue return incentive based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides a revenue return incentive based on the driving data to promote safe driving. For example, revenue return is provided to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides a revenue return incentive based on the user's driving data to promote safe driving. For example, revenue return is provided to users who drive safely. This makes it possible to provide a revenue return incentive based on the user's driving data to promote safe driving.

[0049] The analysis unit can collect driving data of bicycles and electric scooters and provide revenue rebates. The analysis unit, for example, collects driving data of bicycles and electric scooters and builds a system that provides revenue rebates. For example, revenue rebates are provided based on the distance traveled and speed of the bicycle. The analysis unit also provides revenue rebates based on driving data of other means of transportation. For example, revenue rebates are provided based on the frequency of use and driving style of the electric scooter. The analysis unit also collects driving data of bicycles and electric scooters and develops a system that provides revenue rebates. For example, driving data of automobiles and other means of transportation is integrated to provide comprehensive revenue rebates. This makes it possible to collect driving data of bicycles and electric scooters and provide revenue rebates.

[0050] The analysis unit can collect driving data of all family members and provide a return on investment to the entire family. The analysis unit, for example, builds a system that collects driving data of all family members and analyzes it comprehensively. For example, based on the driving style and risk assessment of each member, a return on investment to the entire family is provided. The analysis unit also performs a comprehensive risk assessment based on the driving data of all family members and provides a return on investment to the entire family. For example, the analysis unit analyzes the driving data of all family members and recommends a low-risk driving style. The analysis unit also develops a system that integrates the driving data of all family members and provides a return on investment to the entire family. For example, based on the driving data of all family members, a comprehensive risk assessment is performed and the return on investment is customized. This makes it possible to collect driving data of all family members and provide a return on investment to the entire family.

[0051] The analysis unit can perform a detailed analysis of traffic data for the entire city and propose optimization of the traffic infrastructure. For example, the analysis unit uses generation AI to analyze traffic data for the entire city in detail and build a system that proposes optimization of the traffic infrastructure. For example, it identifies areas where traffic congestion occurs and proposes improvement measures. The analysis unit also proposes optimization of the traffic infrastructure based on traffic data for the entire city. For example, it proposes adjusting traffic signal timing in areas with high traffic volume. The analysis unit also uses generation AI to analyze traffic data for the entire city in detail and develop a system that proposes optimization of the traffic infrastructure. For example, it identifies areas where traffic congestion occurs and proposes improvement measures. This makes it possible to perform a detailed analysis of traffic data for the entire city and propose optimization of the traffic infrastructure.

[0052] The analysis unit can predict future traffic conditions based on past traffic data and make proposals for sustainable urban development. The analysis unit, for example, analyzes past traffic data and builds a system that predicts future traffic conditions. For example, it proposes infrastructure expansion in areas where traffic volume is predicted to increase. The analysis unit also uses generative AI to predict future traffic conditions based on past traffic data and make proposals for sustainable urban development. For example, it proposes expanding public transportation in areas where traffic volume is predicted to increase. The analysis unit also develops a system that predicts future traffic conditions based on past traffic data and makes proposals for sustainable urban development. For example, it proposes infrastructure expansion in areas where traffic volume is predicted to increase. This makes it possible to predict future traffic conditions based on past traffic data and make proposals for sustainable urban development.

[0053] The analysis unit can achieve more effective urban development by sharing traffic data with other cities and learning from each other. The analysis unit, for example, builds a system for sharing traffic data with other cities and learning from each other. For example, they share solutions to traffic congestion and improve public transportation. The analysis unit also achieves effective urban development based on the traffic data of other cities. For example, it can improve its own city's transportation infrastructure by referring to successful examples from other cities. The analysis unit also develops a system for achieving more effective urban development by sharing data with other cities and learning from each other. For example, it can share traffic data and propose solutions to common issues. In this way, it can achieve more effective urban development by sharing traffic data with other cities and learning from each other.

[0054] The analysis unit can propose optimization of public transportation based on traffic data for the entire city. For example, the analysis unit analyzes traffic data for the entire city and builds a system that proposes optimization of public transportation. For example, it makes proposals to optimize bus and train schedules. The analysis unit also uses generative AI to propose optimization of public transportation based on traffic data for the entire city. For example, it adjusts the frequency of bus and train operations to suit times of heavy traffic. The analysis unit also develops a system that proposes optimization of public transportation based on traffic data for the entire city. For example, it identifies locations where traffic congestion occurs and optimizes public transportation routes. This makes it possible to propose optimization of public transportation based on traffic data for the entire city.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The SmartDrive system can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back can be provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums can be applied to users who drive while avoiding sudden braking and sudden acceleration. The analysis unit also provides incentives to improve users' driving behavior based on the driving data, thereby developing a system that reduces insurance premiums. For example, a discount on insurance premiums can be provided to users who drive safely. In this way, insurance premiums can be reduced by providing incentives to improve users' driving behavior based on the driving data.

[0057] The analysis unit can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides incentives to improve users' driving behavior based on the driving data and reduces insurance premiums. For example, a discount on insurance premiums is provided to users who drive safely. In this way, incentives to improve users' driving behavior based on the driving data can be provided and insurance premiums can be reduced.

[0058] The analysis unit can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides incentives to improve users' driving behavior based on the driving data and reduces insurance premiums. For example, a discount on insurance premiums is provided to users who drive safely. In this way, incentives to improve users' driving behavior based on the driving data can be provided and insurance premiums can be reduced.

[0059] The analysis unit can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides incentives to improve users' driving behavior based on the driving data and reduces insurance premiums. For example, a discount on insurance premiums is provided to users who drive safely. In this way, incentives to improve users' driving behavior based on the driving data can be provided and insurance premiums can be reduced.

[0060] The analysis unit can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides incentives to improve users' driving behavior based on the driving data and reduces insurance premiums. For example, a discount on insurance premiums is provided to users who drive safely. In this way, incentives to improve users' driving behavior based on the driving data can be provided and insurance premiums can be reduced.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The driving data collection unit collects driving data. For example, it collects data such as driving speed, number of sudden braking attempts, and distance traveled. The driving data collection unit can also collect driving data in real time using on-board sensors and GPS. Furthermore, the driving data collection unit can also collect driving data through the user's smartphone app. Step 2: The insurance history collection unit collects insurance history. For example, it collects past insurance contract information and insurance claim history. The insurance history collection unit can also obtain insurance history by linking with an insurance company's database. Furthermore, the insurance history collection unit can also collect insurance history information provided by the user. Step 3: The analysis unit analyzes the data collected by the driving data collection unit and the insurance history collection unit. For example, the generation AI analyzes the correlation between driving data and insurance history to evaluate the impact of the user's driving habits on the insurance history. The generation AI can also evaluate the user's driving style based on the driving data. Furthermore, the generation AI can select the most suitable insurance plan for the user based on the insurance history. Step 4: The recommendation unit recommends the optimal insurance plan to the user based on the results of the analysis by the analysis unit. For example, the generation AI may propose the optimal insurance plan based on the user's driving style and insurance history. The recommendation unit can also provide detailed information about insurance plans to the user. Furthermore, the recommendation unit can also support the user in selecting an insurance plan.

[0063] (Example 2) The SmartDrive system according to an embodiment of the present invention is a system that uses AI to perform sophisticated analysis of automobile driving data and insurance history, and recommends the most suitable insurance plan for the user. As a result, the SmartDrive system can provide the most suitable insurance plan for the user, contributing to reducing insurance premiums and promoting safe driving.

[0064] The SmartDrive system according to the embodiment includes a driving data collection unit, an insurance history collection unit, an analysis unit, and a recommendation unit. The driving data collection unit collects driving data, such as driving speed, number of sudden braking attempts, and mileage. The driving data collection unit can also collect driving data in real time using on-board sensors and GPS. The driving data collection unit can also collect driving data through a user's smartphone app. The insurance history collection unit collects insurance history, such as past insurance contract information and insurance claim history. The insurance history collection unit can also acquire insurance history by linking with an insurance company's database. The insurance history collection unit can also collect insurance history information provided by the user. The analysis unit analyzes the data collected by the driving data collection unit and the insurance history collection unit. For example, the generation AI analyzes the correlation between driving data and insurance history and evaluates the impact of the user's driving habits on the insurance history. The generation AI can also evaluate the user's driving style based on the driving data. The generation AI can also select the optimal insurance plan for the user based on the insurance history. The recommendation unit recommends an optimal insurance plan to the user based on the results of the analysis by the analysis unit. For example, the generation AI proposes an optimal insurance plan based on the user's driving style and insurance history. The recommendation unit can also provide detailed information about insurance plans to the user. Furthermore, the recommendation unit can also support the user in selecting an insurance plan. This allows the SmartDrive system according to the embodiment to provide an optimal insurance plan to the user. For example, the user can select an optimal insurance plan based on their driving style and insurance history. The user can also enjoy benefits such as reduced insurance premiums and improved coverage. Furthermore, the insurance company can perform risk assessment based on the user's driving data and insurance history and provide an appropriate insurance plan.

[0065] The analysis unit can analyze at least one of driving data including driving speed, number of sudden braking incidents, and mileage to evaluate the user's driving style. For example, the analysis unit uses generative AI to analyze driving data such as driving speed, number of sudden braking incidents, and mileage, as well as past insurance claim history, to evaluate the impact of driving habits on insurance claims. For example, it analyzes whether drivers who frequently brake suddenly tend to file more insurance claims. The analysis unit also combines the driving data and insurance history to evaluate whether specific driving patterns increase the risk of insurance claims. For example, it analyzes whether users who frequently drive at night are more likely to file more insurance claims. The analysis unit also performs a detailed evaluation of the impact of driving habits on insurance premiums based on the user's driving data and insurance history. For example, it analyzes whether users who drive safely are more likely to receive insurance discounts. This allows for a detailed evaluation of the user's driving style.

[0066] The analysis unit can analyze accident rates in specific areas or time periods and notify users of high-risk areas or time periods. For example, the analysis unit uses generative AI to analyze driving data in real time and evaluate the user's driving style. For example, it can monitor the frequency of sudden braking and sudden acceleration in real time and provide immediate feedback. The analysis unit can also evaluate the user's driving style in real time based on driving data and provide advice to improve driving behavior. For example, if there are frequent sudden braking incidents, the analysis unit can provide advice such as "Keep a sufficient distance from the vehicle in front to avoid sudden braking." The analysis unit can also analyze driving data in real time and build a system to evaluate the user's driving style. For example, the analysis unit can display the evaluation results of the driving style based on driving data on a smartphone or in-car display. This can notify users of high-risk areas and time periods.

[0067] The analysis unit can provide safe driving advice to a user who frequently brakes suddenly. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotional state while driving and evaluate the impact of stress and fatigue on driving. For example, the emotion estimation function analyzes the user's facial expression and voice while driving to evaluate the state of stress and fatigue. For example, the emotion estimation function calculates an emotion score from changes in facial expression and tone of voice to analyze the impact on driving. The analysis unit also monitors the emotional state while driving in real time and evaluates the impact of stress and fatigue on driving behavior. For example, the analysis unit analyzes the frequency of sudden braking and sudden acceleration under high stress conditions. The analysis unit also uses the emotion estimation function to analyze the emotional state while driving and evaluate the impact of stress and fatigue on insurance claims. For example, the analysis unit analyzes whether driving under high stress conditions increases the risk of insurance claims. This makes it possible to provide the user with safe driving advice.

[0068] The analysis unit can apply insurance premium discounts to users who drive with low risk. The analysis unit also collects driving data from other means of transportation, such as bicycles and electric scooters, and proposes a comprehensive insurance plan. For example, the generation AI collects driving data from bicycles and electric scooters and evaluates the user's overall driving style. For example, it analyzes the distance traveled and speed of bicycles, and the frequency of use of electric scooters, and proposes a comprehensive insurance plan. The analysis unit also builds a system that proposes the optimal insurance plan for users based on driving data from other means of transportation. For example, it combines driving data from cars and bicycles to perform a comprehensive risk assessment. The analysis unit also collects driving data from bicycles and electric scooters and proposes a comprehensive insurance plan. For example, it integrates driving data from cars and other means of transportation and recommends the optimal insurance plan for the user. This makes it possible to apply insurance premium discounts to users who drive with low risk.

[0069] The analysis unit can provide incentives to improve driving behavior based on the user's driving data. For example, the analysis unit integrates the driving data of all family members and proposes the optimal insurance plan for the entire family. For example, the generation AI builds a system that collects and comprehensively analyzes the driving data of all family members. For example, it proposes the optimal insurance plan for the entire family based on each member's driving style and risk assessment. The analysis unit also performs a comprehensive risk assessment based on the driving data of all family members and proposes the optimal insurance plan for the entire family. For example, it analyzes the driving data of all family members and recommends an insurance plan based on low-risk members. The analysis unit also develops a system that integrates the driving data of all family members and proposes the optimal insurance plan for the entire family. For example, it performs a comprehensive risk assessment based on the driving data of all family members and customizes the insurance plan. This makes it possible to provide incentives to improve the user's driving behavior.

[0070] The analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate the impact of stress and fatigue on driving. For example, the analysis unit uses the emotion estimation function to analyze the user's facial expressions and voice while driving and evaluate the state of stress and fatigue. For example, the analysis unit calculates an emotion score from changes in facial expressions and tone of voice and analyzes the impact on driving. The analysis unit also monitors the emotional state while driving in real time and evaluates the impact of stress and fatigue on driving behavior. For example, the analysis unit analyzes the frequency of sudden braking and sudden acceleration when under high stress. The analysis unit also uses the emotion estimation function to analyze the emotional state while driving and evaluate the impact of stress and fatigue on insurance claims. For example, the analysis unit analyzes whether driving under high stress increases the risk of filing an insurance claim. In this way, the user's emotional state can be analyzed and the impact of stress and fatigue on driving can be evaluated.

[0071] The analysis unit can use an emotion estimation function to analyze the user's emotional state based on the user's driving data and evaluate the emotions when selecting an insurance plan. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state when selecting an insurance plan. For example, the generation AI uses the emotion estimation function to analyze the user's emotional state when selecting an insurance plan. For example, it calculates an emotion score from facial expressions and voice tone and suggests the plan with the highest satisfaction. The analysis unit also builds a system that suggests the most suitable insurance plan for the user based on the emotion data at the time of insurance plan selection. For example, it prioritizes recommending plans with strong positive emotions. The analysis unit also uses the emotion estimation function to analyze the user's emotional response when selecting an insurance plan and suggests the plan with the highest satisfaction. For example, it customizes the insurance plan based on the plan with the highest emotion score. This makes it possible to analyze the user's emotional state and evaluate the emotions when selecting an insurance plan.

[0072] The analysis unit can predict the risk of traffic accidents by taking into account at least one external factor, either weather or road conditions. The analysis unit, for example, uses generative AI to analyze weather data and road condition data and predict the risk of traffic accidents. For example, it evaluates the risk of accidents in rainy weather or on snowy roads and notifies the user. The analysis unit also builds a risk prediction system that takes into account weather and road conditions. For example, it predicts the risk of accidents in specific areas or time periods based on real-time weather data. The analysis unit also makes risk predictions that take into account external factors and notifies the user of high-risk areas and time periods. For example, it evaluates risk based on information about road construction and traffic congestion. This makes it possible to predict the risk of traffic accidents by taking into account external factors such as weather and road conditions.

[0073] The analysis unit can perform a detailed analysis of the impact of specific driving patterns on accident risk based on past accident data. The analysis unit, for example, analyzes past accident data and evaluates the impact of specific driving patterns on accident risk. For example, it analyzes the accident risk of drivers who frequently brake suddenly or accelerate suddenly. The analysis unit also uses generative AI to analyze specific driving patterns based on past accident data and predict accident risk. For example, it evaluates the accident risk of users who frequently drive at night. The analysis unit also builds a system that performs a detailed analysis of the impact of specific driving patterns on accident risk based on past accident data. For example, it evaluates the accident risk at specific speed ranges. This makes it possible to perform a detailed analysis of the impact of specific driving patterns on accident risk based on past accident data.

[0074] The analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate its impact on the risk of a traffic accident. For example, the analysis unit can use the emotion estimation function to analyze the user's emotional state while driving and evaluate the impact of stress and fatigue on the risk of an accident. For example, the analysis unit analyzes the risk of an accident when stress is high. The analysis unit also monitors the user's emotional state in real time and builds a system that notifies the user of high-risk states in advance. For example, a warning is displayed when the emotion score is high. The analysis unit also uses the emotion estimation function to evaluate the impact of the user's emotional state on the risk of a traffic accident and notifies the user of high-risk states in advance. For example, the analysis unit advises the user to avoid driving when they are highly fatigued. In this way, the emotion estimation function can be used to analyze the user's emotional state and evaluate its impact on the risk of a traffic accident.

[0075] The analysis unit collects location information of pedestrians or bicycles and can perform comprehensive traffic risk predictions. The analysis unit, for example, collects location information of pedestrians and bicycles and builds a system that performs comprehensive traffic risk predictions. For example, it evaluates the risk of accidents in areas with a high number of pedestrians. The analysis unit also performs comprehensive traffic risk predictions based on the location information of pedestrians and bicycles. For example, it analyzes the accident risk during times when there are a high number of pedestrians or bicycles. The analysis unit also collects location information of pedestrians and bicycles and develops a system that performs comprehensive traffic risk predictions. For example, it monitors the movements of pedestrians and bicycles in real time and predicts the risk of accidents. This makes it possible to collect location information of pedestrians and bicycles and perform comprehensive traffic risk predictions.

[0076] The analysis unit can display the risk prediction results on the user's smartphone or in-vehicle display in real time. The analysis unit, for example, builds a system that displays the risk prediction results on the user's smartphone or in-vehicle display in real time. For example, it displays high-risk areas and time periods on a map. The analysis unit also displays the risk prediction results in real time and notifies the user of high-risk areas and time periods. For example, it displays a warning using the smartphone's notification function. The analysis unit also develops a system that displays the risk prediction results in real time and provides the user with information to avoid risks. For example, it displays the risk prediction results on an in-vehicle display to urge caution while driving. This allows the risk prediction results to be displayed in real time on the user's smartphone or in-vehicle display.

[0077] The analysis unit uses the emotion estimation function to analyze the emotional response of the user when receiving risk prediction information and can propose an optimal notification method. The analysis unit, for example, uses the emotion estimation function to analyze the emotional response of the user when receiving risk prediction information. For example, the analysis unit proposes an optimal notification method based on the user's emotion score for a warning display. The analysis unit also builds a system that monitors the emotional response when receiving risk prediction information in real time and proposes an optimal notification method. For example, the notification method is adjusted when the emotion score is high. The analysis unit also uses the emotion estimation function to analyze the emotional response of the user when receiving risk prediction information and proposes an optimal notification method. For example, more detailed information is provided when the emotion score is low. In this way, the emotion estimation function can be used to analyze the emotional response of the user when receiving risk prediction information and propose an optimal notification method.

[0078] The analysis unit can analyze the user's driving data in detail and provide individually customized safe driving advice. The analysis unit can, for example, use a generation AI to analyze the user's driving data in detail and provide individually customized safe driving advice. For example, a user who brakes suddenly frequently can be given specific advice on how to avoid braking suddenly. The analysis unit can also build a system that provides individually customized safe driving advice based on the user's driving data. For example, advice can be provided based on driving speed and mileage. The analysis unit can also use a generation AI to analyze the user's driving data in detail and provide individually customized safe driving advice. For example, specific advice can be provided based on driving style. This makes it possible to analyze the user's driving data in detail and provide individually customized safe driving advice.

[0079] The analysis unit can provide immediate advice based on real-time data obtained while driving, thereby improving driving behavior. The analysis unit, for example, builds a system that provides immediate advice based on real-time data obtained while driving. For example, if sudden braking or sudden acceleration is detected, immediate advice is provided. The analysis unit also analyzes driving data in real time and provides immediate advice. For example, if the distance to the vehicle ahead is close while driving, the analysis unit advises the driver to maintain a safe distance. The analysis unit also develops a system that provides immediate advice based on real-time data obtained while driving, thereby improving driving behavior. For example, the analysis unit notifies the driver in real time of areas to improve their driving style based on data obtained while driving. This makes it possible to provide immediate advice based on real-time data obtained while driving, thereby improving driving behavior.

[0080] The analysis unit uses the emotion estimation function to provide advice according to the user's emotional state, thereby reducing stress. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state while driving and provide advice to reduce stress. For example, advice to relax is provided when stress is high. The analysis unit also builds a system that monitors the user's emotional state in real time and provides advice according to the emotional state. For example, advice to take a break is provided when fatigue is high. The analysis unit also uses the emotion estimation function to provide advice according to the user's emotional state and reduce stress. For example, specific advice to relax is provided when the emotion score is high. In this way, advice according to the user's emotional state is provided using the emotion estimation function, thereby reducing stress.

[0081] The analysis unit can collect driving data of bicycles and electric scooters and provide safe driving advice. The analysis unit, for example, collects driving data of bicycles and electric scooters and builds a system that provides safe driving advice. For example, advice is provided based on the distance traveled and speed of the bicycle. The analysis unit also provides safe driving advice based on driving data of other means of transportation. For example, advice is provided based on the frequency of use and driving style of the electric scooter. The analysis unit also collects driving data of bicycles and electric scooters and develops a system that provides safe driving advice. For example, driving data of automobiles and other means of transportation is integrated to provide comprehensive safe driving advice. This makes it possible to collect driving data of bicycles and electric scooters and provide safe driving advice.

[0082] The analysis unit can collect driving data of all family members and provide safe driving advice to the entire family. The analysis unit, for example, builds a system that collects driving data of all family members and analyzes it in an integrated manner. For example, it provides safe driving advice to the entire family based on the driving style and risk assessment of each member. The analysis unit also performs a comprehensive risk assessment based on the driving data of all family members and provides safe driving advice to the entire family. For example, it analyzes the driving data of all family members and recommends a low-risk driving style. The analysis unit also develops a system that integrates the driving data of all family members and provides safe driving advice to the entire family. For example, it performs a comprehensive risk assessment based on the driving data of all family members and customizes safe driving advice. In this way, it is possible to collect driving data of all family members and provide safe driving advice to the entire family.

[0083] The analysis unit uses the emotion estimation function to analyze the emotional reaction of the user when receiving safe driving advice and can suggest the optimal advice method. The analysis unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user when receiving safe driving advice. For example, the analysis unit suggests the optimal advice method based on the user's emotion score for the advice. The analysis unit also builds a system that monitors the emotional reaction when receiving safe driving advice in real time and suggests the optimal advice method. For example, the advice method is adjusted when the emotion score is high. The analysis unit also uses the emotion estimation function to analyze the emotional reaction of the user when receiving safe driving advice and suggests the optimal advice method. For example, more detailed advice is provided when the emotion score is low. In this way, the emotion estimation function can be used to analyze the emotional reaction of the user when receiving safe driving advice and suggest the optimal advice method.

[0084] The analysis unit can provide individually customized insurance premium discounts based on the user's driving data and risk prediction results. The analysis unit, for example, uses generation AI to build a system that provides individually customized insurance premium discounts based on the user's driving data and risk prediction results. For example, it applies an insurance premium discount to users who drive safely. The analysis unit also provides individually customized insurance premium discounts based on the user's driving data and risk prediction results. For example, it applies an insurance premium discount to users who drive at a low risk. The analysis unit also uses generation AI to develop a system that provides individually customized insurance premium discounts based on the user's driving data and risk prediction results. For example, it provides an insurance premium discount according to driving style. This makes it possible to provide individually customized insurance premium discounts based on the user's driving data and risk prediction results.

[0085] The analysis unit provides an incentive to improve the user's driving behavior based on the driving data, thereby reducing insurance premiums. The analysis unit, for example, builds a system that provides an incentive to improve the user's driving behavior based on the driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides an incentive to improve the user's driving behavior based on the user's driving data. For example, an insurance premium discount is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides an incentive to improve the user's driving behavior based on the driving data and reduces insurance premiums. For example, an insurance premium discount is provided to users who drive safely. In this way, an incentive to improve the user's driving behavior based on the driving data is provided, thereby reducing insurance premiums.

[0086] The analysis unit uses the emotion estimation function to provide an insurance premium discount according to the emotional state of the user, thereby improving satisfaction. The analysis unit, for example, uses the emotion estimation function to build a system that provides an insurance premium discount according to the emotional state of the user. For example, an insurance premium discount is applied to a user with strong positive emotions. The analysis unit also monitors the emotional state of the user in real time and provides an insurance premium discount according to the emotional state. For example, an insurance premium discount is applied when the emotional score is high. The analysis unit also uses the emotion estimation function to provide an insurance premium discount according to the emotional state of the user, thereby improving satisfaction. For example, an insurance premium discount is applied to a user with a high emotional score. In this way, the emotion estimation function can be used to provide an insurance premium discount according to the emotional state of the user, thereby improving satisfaction.

[0087] The analysis unit can collect driving data of bicycles and electric scooters and provide insurance premium discounts. The analysis unit, for example, collects driving data of bicycles and electric scooters and builds a system that provides insurance premium discounts. For example, it provides insurance premium discounts based on bicycle mileage and speed. The analysis unit also provides insurance premium discounts based on driving data of other means of transportation. For example, it provides insurance premium discounts based on the frequency of use and driving style of electric scooters. The analysis unit also collects driving data of bicycles and electric scooters and develops a system that provides insurance premium discounts. For example, it integrates driving data of automobiles and other means of transportation to provide comprehensive insurance premium discounts. This makes it possible to collect driving data of bicycles and electric scooters and provide insurance premium discounts.

[0088] The analysis unit collects driving data of all family members and can provide insurance premium discounts for the entire family. The analysis unit, for example, builds a system that collects driving data of all family members and analyzes it comprehensively. For example, based on the driving style and risk assessment of each member, provides an insurance premium discount for the entire family. The analysis unit also performs a comprehensive risk assessment based on the driving data of all family members and provides an insurance premium discount for the entire family. For example, the analysis unit analyzes the driving data of all family members and recommends a low-risk driving style. The analysis unit also develops a system that integrates driving data of all family members and provides an insurance premium discount for the entire family. For example, based on the driving data of all family members, a comprehensive risk assessment is performed and an insurance premium discount is customized. In this way, it is possible to collect driving data of all family members and provide an insurance premium discount for the entire family.

[0089] The analysis unit uses the emotion estimation function to analyze the emotional response of the user when receiving an insurance premium discount and can propose an optimal discount method. The analysis unit, for example, uses the emotion estimation function to analyze the emotional response of the user when receiving an insurance premium discount. For example, the analysis unit proposes an optimal discount method based on the user's emotion score regarding the discount. The analysis unit also builds a system that monitors the emotional response when receiving an insurance premium discount in real time and proposes an optimal discount method. For example, if the emotion score is high, the discount method is adjusted. The analysis unit also uses the emotion estimation function to analyze the emotional response of the user when receiving an insurance premium discount and proposes an optimal discount method. For example, if the emotion score is low, more detailed discount information is provided. In this way, the emotion estimation function can be used to analyze the emotional response of the user when receiving an insurance premium discount and propose an optimal discount method.

[0090] The analysis unit can analyze the revenue management situation in detail and propose the optimal revenue distribution method. The analysis unit, for example, uses generation AI to build a system that analyzes the revenue management situation in detail and proposes the optimal revenue distribution method. For example, it calculates the optimal distribution method based on revenue data. The analysis unit also analyzes the revenue management situation in detail and proposes the optimal revenue distribution method. For example, it proposes the optimal method for returning a portion of the revenue to users. The analysis unit also uses generation AI to develop a system that analyzes the revenue management situation in detail and proposes the optimal revenue distribution method. For example, it calculates the optimal distribution method for returning a portion of the revenue to users based on revenue data. This makes it possible to analyze the revenue management situation in detail and propose the optimal revenue distribution method.

[0091] The analysis unit can provide a revenue return incentive based on the user's driving data to promote safe driving. The analysis unit, for example, builds a system that provides a revenue return incentive based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides a revenue return incentive based on the driving data to promote safe driving. For example, revenue return is provided to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides a revenue return incentive based on the user's driving data to promote safe driving. For example, revenue return is provided to users who drive safely. This makes it possible to provide a revenue return incentive based on the user's driving data to promote safe driving.

[0092] The analysis unit uses the emotion estimation function to provide a revenue return method according to the user's emotional state, thereby improving satisfaction. The analysis unit, for example, uses the emotion estimation function to build a system that provides a revenue return method according to the user's emotional state. For example, revenue return is provided to users with strong positive emotions. The analysis unit also monitors the user's emotional state in real time and provides a revenue return method according to the emotional state. For example, revenue return is provided when the emotion score is high. The analysis unit also uses the emotion estimation function to provide a revenue return method according to the user's emotional state, thereby improving satisfaction. For example, revenue return is provided to users with a high emotion score. In this way, the emotion estimation function can be used to provide a revenue return method according to the user's emotional state, thereby improving satisfaction.

[0093] The analysis unit can collect driving data of bicycles and electric scooters and provide revenue rebates. The analysis unit, for example, collects driving data of bicycles and electric scooters and builds a system that provides revenue rebates. For example, revenue rebates are provided based on the distance traveled and speed of the bicycle. The analysis unit also provides revenue rebates based on driving data of other means of transportation. For example, revenue rebates are provided based on the frequency of use and driving style of the electric scooter. The analysis unit also collects driving data of bicycles and electric scooters and develops a system that provides revenue rebates. For example, driving data of automobiles and other means of transportation is integrated to provide comprehensive revenue rebates. This makes it possible to collect driving data of bicycles and electric scooters and provide revenue rebates.

[0094] The analysis unit can collect driving data of all family members and provide a return on investment to the entire family. The analysis unit, for example, builds a system that collects driving data of all family members and analyzes it comprehensively. For example, based on the driving style and risk assessment of each member, a return on investment to the entire family is provided. The analysis unit also performs a comprehensive risk assessment based on the driving data of all family members and provides a return on investment to the entire family. For example, the analysis unit analyzes the driving data of all family members and recommends a low-risk driving style. The analysis unit also develops a system that integrates the driving data of all family members and provides a return on investment to the entire family. For example, based on the driving data of all family members, a comprehensive risk assessment is performed and the return on investment is customized. This makes it possible to collect driving data of all family members and provide a return on investment to the entire family.

[0095] The analysis unit uses the emotion estimation function to analyze the emotional response of the user when receiving the revenue return and can propose an optimal return method. The analysis unit, for example, uses the emotion estimation function to analyze the emotional response of the user when receiving the revenue return. For example, the analysis unit proposes an optimal return method based on the user's emotion score regarding the return. The analysis unit also monitors the emotional response when receiving the revenue return in real time and builds a system that proposes an optimal return method. For example, if the emotion score is high, the analysis unit adjusts the return method. The analysis unit also uses the emotion estimation function to analyze the emotional response of the user when receiving the revenue return and proposes an optimal return method. For example, if the emotion score is low, more detailed return information is provided. In this way, the emotion estimation function can be used to analyze the emotional response of the user when receiving the revenue return and propose an optimal return method.

[0096] The analysis unit can perform a detailed analysis of traffic data for the entire city and propose optimization of the traffic infrastructure. For example, the analysis unit uses generation AI to analyze traffic data for the entire city in detail and build a system that proposes optimization of the traffic infrastructure. For example, it identifies areas where traffic congestion occurs and proposes improvement measures. The analysis unit also proposes optimization of the traffic infrastructure based on traffic data for the entire city. For example, it proposes adjusting traffic signal timing in areas with high traffic volume. The analysis unit also uses generation AI to analyze traffic data for the entire city in detail and develop a system that proposes optimization of the traffic infrastructure. For example, it identifies areas where traffic congestion occurs and proposes improvement measures. This makes it possible to perform a detailed analysis of traffic data for the entire city and propose optimization of the traffic infrastructure.

[0097] The analysis unit can predict future traffic conditions based on past traffic data and make proposals for sustainable urban development. The analysis unit, for example, analyzes past traffic data and builds a system that predicts future traffic conditions. For example, it proposes infrastructure expansion in areas where traffic volume is predicted to increase. The analysis unit also uses generative AI to predict future traffic conditions based on past traffic data and make proposals for sustainable urban development. For example, it proposes expanding public transportation in areas where traffic volume is predicted to increase. The analysis unit also develops a system that predicts future traffic conditions based on past traffic data and makes proposals for sustainable urban development. For example, it proposes infrastructure expansion in areas where traffic volume is predicted to increase. This makes it possible to predict future traffic conditions based on past traffic data and make proposals for sustainable urban development.

[0098] The analysis unit can use the emotion estimation function to analyze the emotional state of city residents and determine priorities for transportation infrastructure improvements. The analysis unit, for example, uses the emotion estimation function to analyze the emotional state of city residents and build a system for determining priorities for transportation infrastructure improvements. For example, the analysis unit evaluates residents' stress due to traffic congestion and proposes improvement measures. The analysis unit also monitors the emotional state of city residents in real time and determines priorities for transportation infrastructure improvements. For example, it prioritizes improvement measures for areas with high emotion scores. The analysis unit also uses the emotion estimation function to analyze the emotional state of city residents and determine priorities for transportation infrastructure improvements. For example, it prioritizes improvement measures for areas with low emotion scores. In this way, it is possible to use the emotion estimation function to analyze the emotional state of city residents and determine priorities for transportation infrastructure improvements.

[0099] The analysis unit can achieve more effective urban development by sharing traffic data with other cities and learning from each other. The analysis unit, for example, builds a system for sharing traffic data with other cities and learning from each other. For example, they share solutions to traffic congestion and improve public transportation. The analysis unit also achieves effective urban development based on the traffic data of other cities. For example, it can improve its own city's transportation infrastructure by referring to successful examples from other cities. The analysis unit also develops a system for achieving more effective urban development by sharing data with other cities and learning from each other. For example, it can share traffic data and propose solutions to common issues. In this way, it can achieve more effective urban development by sharing traffic data with other cities and learning from each other.

[0100] The analysis unit can propose optimization of public transportation based on traffic data for the entire city. For example, the analysis unit analyzes traffic data for the entire city and builds a system that proposes optimization of public transportation. For example, it makes proposals to optimize bus and train schedules. The analysis unit also uses generative AI to propose optimization of public transportation based on traffic data for the entire city. For example, it adjusts the frequency of bus and train operations to suit times of heavy traffic. The analysis unit also develops a system that proposes optimization of public transportation based on traffic data for the entire city. For example, it identifies locations where traffic congestion occurs and optimizes public transportation routes. This makes it possible to propose optimization of public transportation based on traffic data for the entire city.

[0101] The analysis unit uses the emotion estimation function to analyze the emotional reactions of city residents when they receive a transportation improvement proposal, and can propose an optimal proposal method. The analysis unit, for example, uses the emotion estimation function to analyze the emotional reactions of city residents when they receive a transportation improvement proposal. For example, the analysis unit proposes an optimal proposal method based on the residents' emotion score regarding the proposal. The analysis unit also builds a system that monitors the emotional reactions of city residents when they receive a transportation improvement proposal in real time and proposes an optimal proposal method. For example, if the emotion score is high, the analysis unit adjusts the proposal method. The analysis unit also uses the emotion estimation function to analyze the emotional reactions of city residents when they receive a transportation improvement proposal, and proposes an optimal proposal method. For example, if the emotion score is low, more detailed proposal information is provided. In this way, the emotion estimation function can be used to analyze the emotional reactions of city residents when they receive a transportation improvement proposal, and propose an optimal proposal method.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The SmartDrive system can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back can be provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums can be applied to users who drive while avoiding sudden braking and sudden acceleration. The analysis unit also provides incentives to improve users' driving behavior based on the driving data, thereby developing a system that reduces insurance premiums. For example, a discount on insurance premiums can be provided to users who drive safely. In this way, insurance premiums can be reduced by providing incentives to improve users' driving behavior based on the driving data.

[0104] The analysis unit can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides incentives to improve users' driving behavior based on the driving data and reduces insurance premiums. For example, a discount on insurance premiums is provided to users who drive safely. In this way, incentives to improve users' driving behavior based on the driving data can be provided and insurance premiums can be reduced.

[0105] The analysis unit can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides incentives to improve users' driving behavior based on the driving data and reduces insurance premiums. For example, a discount on insurance premiums is provided to users who drive safely. In this way, incentives to improve users' driving behavior based on the driving data can be provided and insurance premiums can be reduced.

[0106] The analysis unit can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides incentives to improve users' driving behavior based on the driving data and reduces insurance premiums. For example, a discount on insurance premiums is provided to users who drive safely. In this way, incentives to improve users' driving behavior based on the driving data can be provided and insurance premiums can be reduced.

[0107] The analysis unit can further provide incentives to improve driving behavior based on the user's driving data. For example, points or cash back are provided to users who drive safely. The analysis unit also provides incentives to improve driving behavior based on the user's driving data. For example, a discount on insurance premiums is applied to users who drive in a way that avoids sudden braking and sudden acceleration. The analysis unit also develops a system that provides incentives to improve users' driving behavior based on the driving data and reduces insurance premiums. For example, a discount on insurance premiums is provided to users who drive safely. In this way, incentives to improve users' driving behavior based on the driving data can be provided and insurance premiums can be reduced.

[0108] The analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate the impact of stress and fatigue on driving. For example, the emotion estimation function analyzes the user's facial expressions and voice while driving to evaluate the state of stress and fatigue. For example, the emotion estimation function calculates an emotion score from changes in facial expressions and tone of voice to analyze the impact on driving. The analysis unit also monitors the emotional state while driving in real time and evaluates the impact of stress and fatigue on driving behavior. For example, it analyzes the frequency of sudden braking and sudden acceleration when under high stress. The analysis unit also uses the emotion estimation function to analyze the emotional state while driving and evaluate the impact of stress and fatigue on insurance claims. For example, it analyzes whether driving under high stress increases the risk of insurance claims. This makes it possible to provide the user with advice for safe driving.

[0109] The analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate the impact of stress and fatigue on driving. For example, the emotion estimation function analyzes the user's facial expressions and voice while driving to evaluate the state of stress and fatigue. For example, the emotion estimation function calculates an emotion score from changes in facial expressions and tone of voice to analyze the impact on driving. The analysis unit also monitors the emotional state while driving in real time and evaluates the impact of stress and fatigue on driving behavior. For example, it analyzes the frequency of sudden braking and sudden acceleration when under high stress. The analysis unit also uses the emotion estimation function to analyze the emotional state while driving and evaluate the impact of stress and fatigue on insurance claims. For example, it analyzes whether driving under high stress increases the risk of insurance claims. This makes it possible to provide the user with advice for safe driving.

[0110] The analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate the impact of stress and fatigue on driving. For example, the emotion estimation function analyzes the user's facial expressions and voice while driving to evaluate the state of stress and fatigue. For example, the emotion estimation function calculates an emotion score from changes in facial expressions and tone of voice to analyze the impact on driving. The analysis unit also monitors the emotional state while driving in real time and evaluates the impact of stress and fatigue on driving behavior. For example, it analyzes the frequency of sudden braking and sudden acceleration when under high stress. The analysis unit also uses the emotion estimation function to analyze the emotional state while driving and evaluate the impact of stress and fatigue on insurance claims. For example, it analyzes whether driving under high stress increases the risk of insurance claims. This makes it possible to provide the user with advice for safe driving.

[0111] The analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate the impact of stress and fatigue on driving. For example, the emotion estimation function analyzes the user's facial expressions and voice while driving to evaluate the state of stress and fatigue. For example, the emotion estimation function calculates an emotion score from changes in facial expressions and tone of voice to analyze the impact on driving. The analysis unit also monitors the emotional state while driving in real time and evaluates the impact of stress and fatigue on driving behavior. For example, it analyzes the frequency of sudden braking and sudden acceleration when under high stress. The analysis unit also uses the emotion estimation function to analyze the emotional state while driving and evaluate the impact of stress and fatigue on insurance claims. For example, it analyzes whether driving under high stress increases the risk of insurance claims. This makes it possible to provide the user with advice for safe driving.

[0112] The analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate the impact of stress and fatigue on driving. For example, the emotion estimation function analyzes the user's facial expressions and voice while driving to evaluate the state of stress and fatigue. For example, the emotion estimation function calculates an emotion score from changes in facial expressions and tone of voice to analyze the impact on driving. The analysis unit also monitors the emotional state while driving in real time and evaluates the impact of stress and fatigue on driving behavior. For example, it analyzes the frequency of sudden braking and sudden acceleration when under high stress. The analysis unit also uses the emotion estimation function to analyze the emotional state while driving and evaluate the impact of stress and fatigue on insurance claims. For example, it analyzes whether driving under high stress increases the risk of insurance claims. This makes it possible to provide the user with advice for safe driving.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The driving data collection unit collects driving data. For example, it collects data such as driving speed, number of sudden braking attempts, and distance traveled. The driving data collection unit can also collect driving data in real time using on-board sensors and GPS. Furthermore, the driving data collection unit can also collect driving data through the user's smartphone app. Step 2: The insurance history collection unit collects insurance history. For example, it collects past insurance contract information and insurance claim history. The insurance history collection unit can also obtain insurance history by linking with an insurance company's database. Furthermore, the insurance history collection unit can also collect insurance history information provided by the user. Step 3: The analysis unit analyzes the data collected by the driving data collection unit and the insurance history collection unit. For example, the generation AI analyzes the correlation between driving data and insurance history to evaluate the impact of the user's driving habits on the insurance history. The generation AI can also evaluate the user's driving style based on the driving data. Furthermore, the generation AI can select the most suitable insurance plan for the user based on the insurance history. Step 4: The recommendation unit recommends the optimal insurance plan to the user based on the results of the analysis by the analysis unit. For example, the generation AI may propose the optimal insurance plan based on the user's driving style and insurance history. The recommendation unit can also provide detailed information about insurance plans to the user. Furthermore, the recommendation unit can also support the user in selecting an insurance plan.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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]

[0182] 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. an operation data collection unit that collects operation data; an insurance history collection unit that collects insurance histories; an analysis unit that analyzes the data collected by the driving data collection unit and the insurance history collection unit; a recommendation unit that recommends an optimal insurance plan to the user based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit At least one of driving data of the driving speed, the number of sudden braking, and the distance traveled is analyzed, and the driving style of the user is evaluated.

2. The system of claim 1.

3. The analysis unit Analyzing the accident occurrence rate in a specific area or time period and notifying the user of high-risk areas or time periods 2. The system of claim 1.

4. The analysis unit Providing advice for safe driving to the user who frequently brakes suddenly 2. The system of claim 1.

5. The analysis unit Applying insurance premium discounts to users who drive with low risk 2. The system of claim 1.

6. The analysis unit Providing incentives to improve driving behavior based on the user's driving data 2. The system of claim 1.

7. The analysis unit Based on the driving data of the user, the emotional state of the user is analyzed, and the influence of stress and fatigue on driving is evaluated.

2. The system of claim 1.

8. The analysis unit Analyzing the emotional state of the user based on the driving data of the user and evaluating the emotion when selecting the insurance plan.

2. The system of claim 1.

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