System

Abstract

An object of a system according to an embodiment is to appropriately change an insurance fee based on a driving style of a driver.SOLUTION: A system includes a collection unit, an analysis unit, and a variation unit. The collection unit collects driving data from a sensor or a camera mounted on the vehicle. The analysis unit analyzes the data collected by the collection unit and evaluates the driving style of the driver. The variation unit varies the insurance fee based on the driving style evaluated 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 technology does not adequately adjust insurance premiums based on a driver's driving style, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately vary insurance premiums based on the driving style of the driver. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a variation unit. The collection unit collects driving data from sensors or cameras mounted on the vehicle. The analysis unit analyzes the data collected by the collection unit and evaluates the driver's driving style. The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately vary insurance premiums based on the driving style of the driver. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A driving-style-linked insurance system according to an embodiment of the present invention utilizes a generation AI to analyze a driver's driving conditions and adjust insurance premiums based on safe driving. The driving-style-linked insurance system collects driving data from sensors and cameras mounted on vehicles, and a generation AI analyzes the collected data to evaluate the driver's driving style. Based on the evaluation results, insurance premium discounts are applied to drivers who drive safely, while premiums are increased for drivers who drive dangerously. For example, a driving-style-linked insurance system collects detailed data, such as speed, sudden braking, sudden acceleration, and frequency of lane changes, from sensors and cameras mounted on vehicles. For example, data such as the number of times a vehicle suddenly brakes or suddenly accelerates is collected. Next, in the driving-style-linked insurance system, a generation AI analyzes the collected data and evaluates the driver's driving style. The generation AI evaluates the driver's driving style based on the collected data. For example, a driver who rarely brakes or accelerates suddenly is evaluated as driving safely, while a driver who frequently brakes or accelerates suddenly is evaluated as driving dangerously. Next, the driving-style-linked insurance system adjusts insurance premiums based on the evaluation results. For example, drivers who drive safely will receive a discount on their insurance premiums, while drivers who drive dangerously will be subject to higher premiums. In this way, the driving style-linked insurance system encourages safe driving and contributes to improving drivers' driving styles. This allows the driving style-linked insurance system to gain a detailed understanding of drivers' driving styles and encourage safe driving. For example, avoiding sudden braking and sudden acceleration reduces the risk of traffic accidents. Furthermore, for insurance companies, discounting insurance premiums for drivers with a low accident rate reduces risk and stabilizes profits.

[0029] A driving style-linked insurance system according to an embodiment includes a collection unit, an analysis unit, and a variation unit. The collection unit collects driving data from sensors or cameras mounted on a vehicle. The driving data includes, but is not limited to, speed, acceleration, and frequency of braking. For example, the collection unit measures the vehicle's speed using a speed sensor. The collection unit can also measure the vehicle's acceleration using an acceleration sensor. The collection unit can also capture images of the vehicle's surroundings using a camera and collect the captured images as driving data. For example, the speed sensor can be a GPS-based speed sensor or a wheelbase speed sensor. The acceleration sensor can be a 3-axis acceleration sensor or a MEMS acceleration sensor. The camera can be a front camera, a rear camera, a side camera, or the like. The analysis unit uses a generation AI to analyze the data collected by the collection unit and evaluate the driver's driving style. The evaluation of the driving style includes, but is not limited to, the number of sudden braking and sudden accelerations, the frequency of lane changes, and the like. For example, the generation AI evaluates the driver's driving style based on the number of sudden brakings. The generation AI can also evaluate a driver's driving style based on the number of sudden accelerations. The generation AI can also evaluate a driver's driving style based on the frequency of lane changes. For example, sudden braking refers to braking operations with a certain deceleration or more. Sudden acceleration refers to acceleration operations with a certain acceleration or more. The frequency of lane changes refers to the number of lane changes within a certain distance. The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. Examples of variations in insurance premium include, but are not limited to, applying a discount to drivers who drive safely and increasing insurance premiums for drivers who drive dangerously. For example, the variation unit applies a discount to insurance premiums to drivers who brake suddenly or accelerate suddenly infrequently. The variation unit can also increase insurance premiums for drivers who brake suddenly or accelerate frequently. The variation unit can also apply a discount to insurance premiums to drivers who change lanes infrequently. For example, the variation unit applies a 10% discount to drivers who brake suddenly or accelerate suddenly infrequently and a 10% increase to drivers who brake suddenly or accelerate frequently.As a result, the driving style-linked insurance system according to the embodiment encourages safe driving and contributes to improving the driver's driving style. Some or all of the above-described processing in the variation unit may be performed using, for example, AI, or may be performed without using AI. For example, the variation unit may vary the insurance premium using an AI model that inputs the driving style evaluated by the analysis unit and outputs the insurance premium.

[0030] The driving style-linked insurance system includes a collection unit that installs speed sensors, acceleration sensors, and cameras on the front, rear, left, and right sides of the vehicle. The collection unit installs speed sensors, acceleration sensors, and cameras on the front, rear, left, and right sides of the vehicle. Speed ​​sensors include, for example, GPS-based speed sensors and wheel-based speed sensors. Acceleration sensors include, for example, 3-axis acceleration sensors and MEMS acceleration sensors. Cameras include, for example, a front camera, a rear camera, and a side camera. For example, the collection unit installs a front camera on the front of the vehicle to capture video in the direction of travel of the vehicle. The collection unit can also install a rear camera on the rear of the vehicle to capture video of the rear of the vehicle. The collection unit can also install side cameras on the left and right sides of the vehicle to capture video of the sides of the vehicle. In this way, detailed driving data can be collected by installing sensors on the front, rear, left, and right sides of the vehicle. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video data from cameras installed on the front, rear, left, and right sides of the vehicle to a generation AI and have the generation AI analyze the video data.

[0031] The analysis unit can evaluate the driver's driving style based on the number of sudden braking or sudden acceleration, and the frequency of lane changes. The analysis unit can evaluate the driver's driving style based on, for example, the number of sudden braking. For example, the analysis unit can evaluate a driver who rarely brakes suddenly as driving safely. The analysis unit can also evaluate the driver's driving style based on the number of sudden accelerations. For example, the analysis unit can evaluate a driver who rarely accelerates suddenly as driving safely. The analysis unit can also evaluate the driver's driving style based on the frequency of lane changes. For example, the analysis unit can evaluate a driver who rarely changes lanes as driving safely. In this way, by evaluating the driving style based on the number of sudden braking or sudden acceleration, and the frequency of lane changes, the driver's driving behavior can be understood in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the number of sudden braking or sudden acceleration, and the frequency of lane changes into the generation AI, and cause the generation AI to evaluate the driving style.

[0032] The variation unit can apply a discount to insurance premiums to drivers who drive safely and increase insurance premiums to drivers who drive dangerously. The variation unit, for example, applies a discount to insurance premiums to drivers who drive safely. For example, the variation unit applies a discount to insurance premiums to drivers who rarely brake or accelerate suddenly. The variation unit can also increase insurance premiums to drivers who drive dangerously. For example, the variation unit increases insurance premiums to drivers who frequently brake or accelerate suddenly. The variation unit can also apply a discount to insurance premiums to drivers who change lanes infrequently. In this way, safe driving is encouraged by applying a discount to insurance premiums to drivers who drive safely and increasing insurance premiums to drivers who drive dangerously. Some or all of the above-described processing in the variation unit may be performed, for example, using AI or without AI. For example, the variation unit can vary insurance premiums using an AI model that inputs the driving style evaluated by the analysis unit and outputs insurance premiums.

[0033] The variation unit can apply a 10% discount to drivers who rarely brake suddenly or accelerate suddenly, and a 10% increase to drivers who frequently brake suddenly or accelerate suddenly. For example, the variation unit applies a 10% discount to drivers who rarely brake suddenly or accelerate suddenly. The variation unit can also apply a 10% discount to drivers who rarely brake suddenly or accelerate suddenly. The variation unit can also apply a 10% increase to drivers who frequently brake suddenly or accelerate suddenly. For example, the variation unit applies a 10% increase to drivers who frequently brake suddenly. The variation unit can also apply a 10% increase to drivers who frequently accelerate suddenly. This allows the insurance premium to fluctuate depending on the number of times the vehicle brakes suddenly or accelerates suddenly, thereby improving the driver's driving behavior. Some or all of the above-mentioned processing in the variation unit may be performed using AI, for example, or may be performed without using AI. For example, the variation unit can input data on the number of times the vehicle brakes suddenly or accelerates suddenly into the generation AI and cause the generation AI to apply a discount or increase to the insurance premium.

[0034] The collection unit can automatically adjust the sensitivity of the sensor depending on the weather or road conditions. For example, the collection unit increases the sensitivity of the sensor when it is raining to collect detailed information about slippery road conditions. The collection unit can also return the sensitivity of the sensor to normal when it is sunny to collect standard data. The collection unit can also maximize the sensitivity of the sensor on snowy days to collect detailed information about slippery road conditions or poor visibility. This allows appropriate data to be collected by adjusting the sensitivity of the sensor depending on the weather and road conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data about weather and road conditions into the generation AI and have the generation AI adjust the sensitivity of the sensor.

[0035] The collection unit can change the frequency of data collection based on the maintenance status of the vehicle. For example, the collection unit reduces the frequency of data collection immediately after the vehicle has undergone regular maintenance and collects normal driving data. The collection unit can also increase the frequency of data collection and collect detailed driving data if the vehicle has not undergone maintenance for a long period of time. The collection unit can also maximize the frequency of data collection and collect detailed data about the abnormality if an abnormality is detected in the vehicle. This makes it possible to collect appropriate data by adjusting the frequency of data collection according to the maintenance status of the vehicle. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the maintenance status of the vehicle to the generation AI and cause the generation AI to adjust the frequency of data collection.

[0036] The collection unit can focus on collecting specific driving behaviors based on the driver's past driving history. For example, if the driver has frequently braked suddenly in the past, the collection unit can focus on collecting data on sudden braking. Furthermore, if the driver has frequently accelerated suddenly in the past, the collection unit can focus on collecting data on sudden acceleration. Furthermore, if the driver has frequently changed lanes in the past, the collection unit can focus on collecting data on lane changes. This allows for a detailed understanding of the driver's driving style by focusing on collecting specific driving behaviors based on the driver's past driving history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the driver's past driving history to the generation AI and cause the generation AI to collect data on specific driving behaviors.

[0037] The collection unit can prioritize collecting driving data in a specific area based on vehicle location information. For example, the collection unit prioritizes collecting detailed driving data in areas where traffic accidents occur frequently. The collection unit can also prioritize collecting lane change and speed change data in areas with heavy traffic. The collection unit can also prioritize collecting data on red light ignition and stop signs in areas with strict traffic regulations. In this way, by collecting driving data in a specific area preferentially, it is possible to understand driving styles unique to the area. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input vehicle location information data to the generation AI and cause the generation AI to prioritize collecting driving data in a specific area.

[0038] The collection unit can acquire data from other vehicles in real time and compare them with each other. For example, the collection unit can acquire sudden braking data from other vehicles in real time and compare it with data from the host vehicle. The collection unit can also acquire sudden acceleration data from other vehicles in real time and compare it with data from the host vehicle. The collection unit can also acquire lane change data from other vehicles in real time and compare it with data from the host vehicle. This makes it possible to evaluate relative driving styles by acquiring and comparing data from other vehicles in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from other vehicles to the generation AI and cause the generation AI to perform mutual comparisons.

[0039] The collection unit can also integrate and collect data from the driver's smartphone or wearable device. For example, the collection unit integrates and collects location information from the driver's smartphone. The collection unit can also integrate and collect heart rate data from the driver's wearable device. The collection unit can also integrate and collect driving app data from the driver's smartphone. In this way, by integrating and collecting data from the smartphone or wearable device, more detailed driving data can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from the driver's smartphone or wearable device to the generation AI and cause the generation AI to integrate the data.

[0040] The analysis unit can analyze driving data in real time and provide immediate feedback. For example, the analysis unit can analyze data on sudden braking in real time and provide immediate feedback to the driver. The analysis unit can also analyze data on sudden acceleration in real time and provide immediate feedback to the driver. The analysis unit can also analyze data on lane changes in real time and provide immediate feedback to the driver. This allows the driver's driving behavior to be immediately improved by analyzing driving data in real time and providing immediate feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on sudden braking, sudden acceleration, and lane changes to the generation AI and have the generation AI perform real-time analysis and feedback.

[0041] The analysis unit can switch the evaluation algorithm according to different driving scenarios (urban areas, highways, mountain roads, etc.). For example, in urban areas, the analysis unit may place emphasis on data on ignoring traffic lights and stopping at designated stops in the evaluation. The analysis unit may also place emphasis on data on speed fluctuations and lane changes in the evaluation on highways. The analysis unit may also place emphasis on data on sharp curves and steep gradients in the evaluation on mountain roads. In this way, by switching the evaluation algorithm according to different driving scenarios, it is possible to perform an evaluation appropriate for the scenario. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input data of different driving scenarios into the generation AI and cause the generation AI to switch the evaluation algorithm.

[0042] The analysis unit can evaluate changes in driving style by comparing with past driving data. For example, the analysis unit compares past sudden braking data with current data to evaluate changes in driving style. The analysis unit can also compare past sudden acceleration data with current data to evaluate changes in driving style. The analysis unit can also compare past lane change data with current data to evaluate changes in driving style. In this way, changes in driving style can be evaluated by comparing with past driving data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past driving data and current data into the generation AI and cause the generation AI to evaluate changes in driving style.

[0043] The analysis unit can perform a relative evaluation by comparing with the driving data of other drivers. The analysis unit can perform a relative evaluation by comparing with, for example, sudden braking data of other drivers. The analysis unit can also perform a relative evaluation by comparing with sudden acceleration data of other drivers. The analysis unit can also perform a relative evaluation by comparing with lane change data of other drivers. This makes it possible to perform a relative evaluation by comparing with the driving data of other drivers. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the driving data of other drivers into the generation AI and cause the generation AI to perform a relative evaluation.

[0044] The analysis unit can estimate the driver's health state based on the driving data and reflect it in the driving style. The analysis unit can estimate the driver's stress level based on data on sudden braking and sudden acceleration, for example. The analysis unit can also estimate the driver's attention level based on data on lane changes. The analysis unit can also estimate the driver's fatigue level based on speed fluctuation data. In this way, the driver's health state can be estimated based on the driving data and reflected in the evaluation of the driving style. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI. For example, the analysis unit can input driving data to the generation AI and cause the generation AI to estimate the driver's health state and reflect it in the driving style.

[0045] The analysis unit can evaluate a comprehensive driving style by including vehicle performance data in the analysis. For example, the analysis unit can evaluate sudden braking by including vehicle braking performance data in the analysis. The analysis unit can also evaluate sudden acceleration by including vehicle acceleration performance data in the analysis. The analysis unit can also evaluate lane changes by including vehicle handling performance data in the analysis. This makes it possible to evaluate a comprehensive driving style by including vehicle performance data in the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input vehicle performance data into the generation AI and cause the generation AI to perform a comprehensive driving style evaluation.

[0046] The variation unit can vary the insurance premium in real time based on the driving style evaluation results. The variation unit varies the insurance premium in real time based on, for example, the evaluation results of sudden braking. The variation unit can also vary the insurance premium in real time based on the evaluation results of sudden acceleration. The variation unit can also vary the insurance premium in real time based on the evaluation results of lane changes. This allows for real-time variation of the insurance premium based on the driving style evaluation results, making it possible to immediately set appropriate premiums. Some or all of the above-mentioned processing in the variation unit may be performed using AI, for example, or may be performed without using AI. For example, the variation unit can input the driving style evaluation results into a generation AI and cause the generation AI to perform real-time variation of the insurance premium.

[0047] The variation unit can vary the premium based on the driver's past insurance usage history. For example, the variation unit can apply a discount to the insurance premium based on the driver's past insurance usage history. The variation unit can also apply an increase in the insurance premium based on the driver's past insurance usage history. The variation unit can also vary the premium under specific conditions based on the driver's past insurance usage history. This enables more appropriate premium setting by taking into account the driver's past insurance usage history. Some or all of the above-mentioned processing in the variation unit may be performed using AI, for example, or may be performed without using AI. For example, the variation unit can input data on the driver's past insurance usage history into the generation AI and have the generation AI execute the premium variation.

[0048] The variation unit can apply different fare variations to specific driving behaviors. For example, the variation unit can apply a discount to insurance premiums when driving at night. The variation unit can also apply an increase in insurance premiums when driving long distances. The variation unit can also apply different fare variations to specific driving behaviors (e.g., driving on a highway). This allows for appropriate pricing according to driving behaviors to be set by applying different fare variations to specific driving behaviors. Some or all of the above-described processing in the variation unit may be performed using, or without, AI, for example. For example, the variation unit can input data of specific driving behaviors into a generation AI and have the generation AI apply fare variations.

[0049] The variable section can propose a comprehensive insurance premium in conjunction with other insurance products. The variable section can propose a comprehensive insurance premium based on, for example, health insurance data. The variable section can also propose a comprehensive insurance premium based on home insurance data. The variable section can also propose a comprehensive insurance premium based on data from other insurance products (e.g., travel insurance). This makes it possible to propose a comprehensive insurance premium by linking with other insurance products. Some or all of the above-mentioned processing in the variable section may be performed using, for example, AI, or may be performed without using AI. For example, the variable section can input data from other insurance products into the generation AI and have the generation AI propose a comprehensive insurance premium.

[0050] The variable unit can customize the insurance premium based on the driver's family structure and lifestyle. The variable unit customizes the insurance premium based on, for example, the driver's family structure. The variable unit can also customize the insurance premium based on the driver's lifestyle (e.g., commute distance). The variable unit can also customize the insurance premium based on the driver's hobbies and activities (e.g., outdoor activities). This enables more appropriate premium setting by customizing the insurance premium based on the driver's family structure and lifestyle. Some or all of the above-mentioned processing in the variable unit may be performed using, or without, AI, for example. For example, the variable unit can input data on the driver's family structure and lifestyle into the generation AI and have the generation AI customize the insurance premium.

[0051] The variation unit can vary the insurance premium taking into account the driver's social credit score. The variation unit can, for example, apply a discount to the insurance premium based on the driver's credit score. The variation unit can also apply an increase in the insurance premium based on the driver's credit score. The variation unit can also vary the premium under certain conditions based on the driver's credit score. This allows for more appropriate premium setting by taking the driver's social credit score into account. Some or all of the above-mentioned processing in the variation unit can be performed using AI, for example, or without using AI. For example, the variation unit can input data on the driver's credit score into the generation AI and have the generation AI vary the insurance premium.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit can change the frequency of data collection based on the maintenance status of the vehicle. For example, immediately after the vehicle has undergone scheduled maintenance, the frequency of data collection can be reduced to collect normal driving data. Also, if the vehicle has not undergone maintenance for a long period of time, the frequency of data collection can be increased to collect detailed driving data. Also, if an abnormality is detected in the vehicle, the frequency of data collection can be maximized to collect detailed data about the abnormality. In this way, by adjusting the frequency of data collection according to the maintenance status of the vehicle, appropriate data can be collected.

[0054] The variance unit can apply different fare variations to specific driving behaviors. For example, it can apply a discount to insurance premiums when driving at night. It can also apply an increase in insurance premiums when driving long distances. It can also apply different fare variations to specific driving behaviors (e.g., driving on a highway). By applying different fare variations to specific driving behaviors, it becomes possible to set appropriate fare according to the driving behavior.

[0055] The collection unit can also integrate and collect data from the driver's smartphone or wearable device. For example, the collection unit can integrate and collect location information from the driver's smartphone. The collection unit can also integrate and collect heart rate data from the driver's wearable device. The collection unit can also integrate and collect driving app data from the driver's smartphone. By integrating and collecting data from the smartphone and wearable device, more detailed driving data can be obtained.

[0056] The analysis unit can estimate the driver's health state based on driving data and reflect it in the driving style. For example, the stress level of the driver can be estimated based on data on sudden braking and sudden acceleration. The driver's level of attention can also be estimated based on data on lane changes. The driver's level of fatigue can also be estimated based on data on speed fluctuations. In this way, the driver's health state can be estimated based on driving data, and this can be reflected in the evaluation of driving style.

[0057] The collection unit can acquire data from other vehicles in real time and compare the data with each other. For example, sudden braking data from other vehicles can be acquired in real time and compared with data from the vehicle itself. It can also acquire sudden acceleration data from other vehicles in real time and compare it with data from the vehicle itself. It can also acquire lane change data from other vehicles in real time and compare it with data from the vehicle itself. This makes it possible to evaluate relative driving styles by acquiring and comparing data from other vehicles in real time.

[0058] The analysis unit can switch evaluation algorithms according to different driving scenarios (urban areas, highways, mountain roads, etc.). For example, in urban areas, the evaluation can place emphasis on data on ignoring traffic lights and stopping at designated stops. On highways, the evaluation can also place emphasis on data on speed fluctuations and lane changes. On mountain roads, the evaluation can also place emphasis on data on sharp curves and steep gradients. This allows the evaluation to be tailored to each scenario by switching the evaluation algorithm according to different driving scenarios.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects driving data from sensors or cameras installed in the vehicle. The driving data includes speed, acceleration, frequency of braking, etc. For example, a speed sensor is used to measure the vehicle's speed, and an acceleration sensor is used to measure the vehicle's acceleration. In addition, a camera can be used to capture images of the vehicle's surroundings and collect these as driving data. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and evaluate the driver's driving style. The evaluation of driving style includes the number of sudden braking and sudden acceleration, the frequency of lane changes, etc. For example, the driver's driving style is evaluated based on the number of sudden braking, sudden acceleration, and lane changes. Step 3: The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. For example, a discount on the insurance premium is applied to drivers who drive safely, and an increase on the insurance premium is applied to drivers who drive dangerously. A discount on the insurance premium can be applied to drivers who rarely brake or accelerate suddenly, and an increase on the insurance premium can be applied to drivers who frequently brake or accelerate suddenly.

[0061] (Example 2) A driving-style-linked insurance system according to an embodiment of the present invention utilizes a generation AI to analyze a driver's driving conditions and adjust insurance premiums based on safe driving. The driving-style-linked insurance system collects driving data from sensors and cameras mounted on vehicles, and a generation AI analyzes the collected data to evaluate the driver's driving style. Based on the evaluation results, insurance premium discounts are applied to drivers who drive safely, while premiums are increased for drivers who drive dangerously. For example, a driving-style-linked insurance system collects detailed data, such as speed, sudden braking, sudden acceleration, and frequency of lane changes, from sensors and cameras mounted on vehicles. For example, data such as the number of times a vehicle suddenly brakes or suddenly accelerates is collected. Next, in the driving-style-linked insurance system, a generation AI analyzes the collected data and evaluates the driver's driving style. The generation AI evaluates the driver's driving style based on the collected data. For example, a driver who rarely brakes or accelerates suddenly is evaluated as driving safely, while a driver who frequently brakes or accelerates suddenly is evaluated as driving dangerously. Next, the driving-style-linked insurance system adjusts insurance premiums based on the evaluation results. For example, drivers who drive safely will receive a discount on their insurance premiums, while drivers who drive dangerously will be subject to higher premiums. In this way, the driving style-linked insurance system encourages safe driving and contributes to improving drivers' driving styles. This allows the driving style-linked insurance system to gain a detailed understanding of drivers' driving styles and encourage safe driving. For example, avoiding sudden braking and sudden acceleration reduces the risk of traffic accidents. Furthermore, for insurance companies, discounting insurance premiums for drivers with a low accident rate reduces risk and stabilizes profits.

[0062] A driving style-linked insurance system according to an embodiment includes a collection unit, an analysis unit, and a variation unit. The collection unit collects driving data from sensors or cameras mounted on a vehicle. The driving data includes, but is not limited to, speed, acceleration, and frequency of braking. For example, the collection unit measures the vehicle's speed using a speed sensor. The collection unit can also measure the vehicle's acceleration using an acceleration sensor. The collection unit can also capture images of the vehicle's surroundings using a camera and collect the captured images as driving data. For example, the speed sensor can be a GPS-based speed sensor or a wheelbase speed sensor. The acceleration sensor can be a 3-axis acceleration sensor or a MEMS acceleration sensor. The camera can be a front camera, a rear camera, a side camera, or the like. The analysis unit uses a generation AI to analyze the data collected by the collection unit and evaluate the driver's driving style. The evaluation of the driving style includes, but is not limited to, the number of sudden braking and sudden accelerations, the frequency of lane changes, and the like. For example, the generation AI evaluates the driver's driving style based on the number of sudden brakings. The generation AI can also evaluate a driver's driving style based on the number of sudden accelerations. The generation AI can also evaluate a driver's driving style based on the frequency of lane changes. For example, sudden braking refers to braking operations with a certain deceleration or more. Sudden acceleration refers to acceleration operations with a certain acceleration or more. The frequency of lane changes refers to the number of lane changes within a certain distance. The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. Examples of variations in insurance premium include, but are not limited to, applying a discount to drivers who drive safely and increasing insurance premiums for drivers who drive dangerously. For example, the variation unit applies a discount to insurance premiums to drivers who brake suddenly or accelerate suddenly infrequently. The variation unit can also increase insurance premiums for drivers who brake suddenly or accelerate frequently. The variation unit can also apply a discount to insurance premiums to drivers who change lanes infrequently. For example, the variation unit applies a 10% discount to drivers who brake suddenly or accelerate suddenly infrequently and a 10% increase to drivers who brake suddenly or accelerate frequently.As a result, the driving style-linked insurance system according to the embodiment encourages safe driving and contributes to improving the driver's driving style. Some or all of the above-described processing in the variation unit may be performed using, for example, AI, or may be performed without using AI. For example, the variation unit may vary the insurance premium using an AI model that inputs the driving style evaluated by the analysis unit and outputs the insurance premium.

[0063] The driving style-linked insurance system includes a collection unit that installs speed sensors, acceleration sensors, and cameras on the front, rear, left, and right sides of the vehicle. The collection unit installs speed sensors, acceleration sensors, and cameras on the front, rear, left, and right sides of the vehicle. Speed ​​sensors include, for example, GPS-based speed sensors and wheel-based speed sensors. Acceleration sensors include, for example, 3-axis acceleration sensors and MEMS acceleration sensors. Cameras include, for example, a front camera, a rear camera, and a side camera. For example, the collection unit installs a front camera on the front of the vehicle to capture video in the direction of travel of the vehicle. The collection unit can also install a rear camera on the rear of the vehicle to capture video of the rear of the vehicle. The collection unit can also install side cameras on the left and right sides of the vehicle to capture video of the sides of the vehicle. In this way, detailed driving data can be collected by installing sensors on the front, rear, left, and right sides of the vehicle. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video data from cameras installed on the front, rear, left, and right sides of the vehicle to a generation AI and have the generation AI analyze the video data.

[0064] The analysis unit can evaluate the driver's driving style based on the number of sudden braking or sudden acceleration, and the frequency of lane changes. The analysis unit can evaluate the driver's driving style based on, for example, the number of sudden braking. For example, the analysis unit can evaluate a driver who rarely brakes suddenly as driving safely. The analysis unit can also evaluate the driver's driving style based on the number of sudden accelerations. For example, the analysis unit can evaluate a driver who rarely accelerates suddenly as driving safely. The analysis unit can also evaluate the driver's driving style based on the frequency of lane changes. For example, the analysis unit can evaluate a driver who rarely changes lanes as driving safely. In this way, by evaluating the driving style based on the number of sudden braking or sudden acceleration, and the frequency of lane changes, the driver's driving behavior can be understood in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the number of sudden braking or sudden acceleration, and the frequency of lane changes into the generation AI, and cause the generation AI to evaluate the driving style.

[0065] The variation unit can apply a discount to insurance premiums to drivers who drive safely and increase insurance premiums to drivers who drive dangerously. The variation unit, for example, applies a discount to insurance premiums to drivers who drive safely. For example, the variation unit applies a discount to insurance premiums to drivers who rarely brake or accelerate suddenly. The variation unit can also increase insurance premiums to drivers who drive dangerously. For example, the variation unit increases insurance premiums to drivers who frequently brake or accelerate suddenly. The variation unit can also apply a discount to insurance premiums to drivers who change lanes infrequently. In this way, safe driving is encouraged by applying a discount to insurance premiums to drivers who drive safely and increasing insurance premiums to drivers who drive dangerously. Some or all of the above-described processing in the variation unit may be performed, for example, using AI or without AI. For example, the variation unit can vary insurance premiums using an AI model that inputs the driving style evaluated by the analysis unit and outputs insurance premiums.

[0066] The variation unit can apply a 10% discount to drivers who rarely brake suddenly or accelerate suddenly, and a 10% increase to drivers who frequently brake suddenly or accelerate suddenly. For example, the variation unit applies a 10% discount to drivers who rarely brake suddenly or accelerate suddenly. The variation unit can also apply a 10% discount to drivers who rarely brake suddenly or accelerate suddenly. The variation unit can also apply a 10% increase to drivers who frequently brake suddenly or accelerate suddenly. For example, the variation unit applies a 10% increase to drivers who frequently brake suddenly. The variation unit can also apply a 10% increase to drivers who frequently accelerate suddenly. This allows the insurance premium to fluctuate depending on the number of times the vehicle brakes suddenly or accelerates suddenly, thereby improving the driver's driving behavior. Some or all of the above-mentioned processing in the variation unit may be performed using AI, for example, or may be performed without using AI. For example, the variation unit can input data on the number of times the vehicle brakes suddenly or accelerates suddenly into the generation AI and cause the generation AI to apply a discount or increase to the insurance premium.

[0067] The driving style-linked insurance system includes a collection unit that estimates the driver's emotions and adjusts the timing of driving data collection based on the estimated driver's emotions. The collection unit estimates the driver's emotions and adjusts the timing of driving data collection based on the estimated driver's emotions. For example, if the driver is stressed, the collection unit increases the frequency of driving data collection and acquires detailed data. Also, if the driver is relaxed, the collection unit can reduce the frequency of driving data collection and acquire the minimum necessary data. Also, if the driver is excited, the collection unit can collect data by focusing on specific driving behaviors (such as sudden braking or sudden acceleration). For example, the collection unit captures the driver's facial expressions with a camera and estimates the driver's emotions using an emotion estimation algorithm. The collection unit can also record the driver's voice and estimate the driver's emotions using voice analysis technology. The collection unit can also collect the driver's biometric data (heart rate and electrodermal activity) with a sensor and estimate the driver's emotions using an emotion estimation algorithm. This allows for more accurate data collection by adjusting the timing of driving data collection according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the driver's emotion data into the generation AI and cause the generation AI to adjust the timing of collecting driving data.

[0068] The collection unit can automatically adjust the sensitivity of the sensor depending on the weather or road conditions. For example, the collection unit increases the sensitivity of the sensor when it is raining to collect detailed information about slippery road conditions. The collection unit can also return the sensitivity of the sensor to normal when it is sunny to collect standard data. The collection unit can also maximize the sensitivity of the sensor on snowy days to collect detailed information about slippery road conditions or poor visibility. This allows appropriate data to be collected by adjusting the sensitivity of the sensor depending on the weather and road conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data about weather and road conditions into the generation AI and have the generation AI adjust the sensitivity of the sensor.

[0069] The collection unit can change the frequency of data collection based on the maintenance status of the vehicle. For example, the collection unit reduces the frequency of data collection immediately after the vehicle has undergone regular maintenance and collects normal driving data. The collection unit can also increase the frequency of data collection and collect detailed driving data if the vehicle has not undergone maintenance for a long period of time. The collection unit can also maximize the frequency of data collection and collect detailed data about the abnormality if an abnormality is detected in the vehicle. This makes it possible to collect appropriate data by adjusting the frequency of data collection according to the maintenance status of the vehicle. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the maintenance status of the vehicle to the generation AI and cause the generation AI to adjust the frequency of data collection.

[0070] The collection unit can focus on collecting specific driving behaviors based on the driver's past driving history. For example, if the driver has frequently braked suddenly in the past, the collection unit can focus on collecting data on sudden braking. Furthermore, if the driver has frequently accelerated suddenly in the past, the collection unit can focus on collecting data on sudden acceleration. Furthermore, if the driver has frequently changed lanes in the past, the collection unit can focus on collecting data on lane changes. This allows for a detailed understanding of the driver's driving style by focusing on collecting specific driving behaviors based on the driver's past driving history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the driver's past driving history to the generation AI and cause the generation AI to collect data on specific driving behaviors.

[0071] The collection unit can estimate the driver's emotions and determine the priority of data to be collected based on the estimated driver's emotions. For example, if the driver is stressed, the collection unit can prioritize collecting data on sudden braking and sudden acceleration. Furthermore, if the driver is relaxed, the collection unit can prioritize collecting normal driving data. Furthermore, if the driver is excited, the collection unit can prioritize collecting data on lane changes and speed changes. Thus, by determining the priority of data to be collected according to the driver's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the driver's emotion data into the generation AI and have the generation AI determine the priority of the data to be collected.

[0072] The collection unit can prioritize collecting driving data in a specific area based on vehicle location information. For example, the collection unit prioritizes collecting detailed driving data in areas where traffic accidents occur frequently. The collection unit can also prioritize collecting lane change and speed change data in areas with heavy traffic. The collection unit can also prioritize collecting data on red light ignition and stop signs in areas with strict traffic regulations. In this way, by collecting driving data in a specific area preferentially, it is possible to understand driving styles unique to the area. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input vehicle location information data to the generation AI and cause the generation AI to prioritize collecting driving data in a specific area.

[0073] The collection unit can acquire data from other vehicles in real time and compare them with each other. For example, the collection unit can acquire sudden braking data from other vehicles in real time and compare it with data from the host vehicle. The collection unit can also acquire sudden acceleration data from other vehicles in real time and compare it with data from the host vehicle. The collection unit can also acquire lane change data from other vehicles in real time and compare it with data from the host vehicle. This makes it possible to evaluate relative driving styles by acquiring and comparing data from other vehicles in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from other vehicles to the generation AI and cause the generation AI to perform mutual comparisons.

[0074] The collection unit can also integrate and collect data from the driver's smartphone or wearable device. For example, the collection unit integrates and collects location information from the driver's smartphone. The collection unit can also integrate and collect heart rate data from the driver's wearable device. The collection unit can also integrate and collect driving app data from the driver's smartphone. In this way, by integrating and collecting data from the smartphone or wearable device, more detailed driving data can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from the driver's smartphone or wearable device to the generation AI and cause the generation AI to integrate the data.

[0075] The analysis unit can estimate the driver's emotions and adjust the driving style evaluation criteria based on the estimated driver's emotions. For example, if the driver is stressed, the analysis unit can tighten the evaluation criteria for sudden braking and sudden acceleration. The analysis unit can also apply normal evaluation criteria if the driver is relaxed. The analysis unit can also tighten the evaluation criteria for lane changes and speed changes if the driver is excited. This allows for more accurate evaluation by adjusting the driving style evaluation criteria according to the driver's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the driver's emotion data into the generation AI and cause the generation AI to adjust the driving style evaluation criteria.

[0076] The analysis unit can analyze driving data in real time and provide immediate feedback. For example, the analysis unit can analyze data on sudden braking in real time and provide immediate feedback to the driver. The analysis unit can also analyze data on sudden acceleration in real time and provide immediate feedback to the driver. The analysis unit can also analyze data on lane changes in real time and provide immediate feedback to the driver. This allows the driver's driving behavior to be immediately improved by analyzing driving data in real time and providing immediate feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on sudden braking, sudden acceleration, and lane changes to the generation AI and have the generation AI perform real-time analysis and feedback.

[0077] The analysis unit can switch the evaluation algorithm according to different driving scenarios (urban areas, highways, mountain roads, etc.). For example, in urban areas, the analysis unit may place emphasis on data on ignoring traffic lights and stopping at designated stops in the evaluation. The analysis unit may also place emphasis on data on speed fluctuations and lane changes in the evaluation on highways. The analysis unit may also place emphasis on data on sharp curves and steep gradients in the evaluation on mountain roads. In this way, by switching the evaluation algorithm according to different driving scenarios, it is possible to perform an evaluation appropriate for the scenario. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input data of different driving scenarios into the generation AI and cause the generation AI to switch the evaluation algorithm.

[0078] The analysis unit can evaluate changes in driving style by comparing with past driving data. For example, the analysis unit compares past sudden braking data with current data to evaluate changes in driving style. The analysis unit can also compare past sudden acceleration data with current data to evaluate changes in driving style. The analysis unit can also compare past lane change data with current data to evaluate changes in driving style. In this way, changes in driving style can be evaluated by comparing with past driving data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past driving data and current data into the generation AI and cause the generation AI to evaluate changes in driving style.

[0079] The analysis unit can estimate the driver's emotions and adjust the display method of the evaluation results based on the estimated driver's emotions. For example, if the driver is nervous, the analysis unit can provide a simple, highly visible display method. If the driver is relaxed, the analysis unit can also provide a display method that includes detailed information. If the driver is in a hurry, the analysis unit can also provide a display method that focuses on the main points. By adjusting the display method of the evaluation results according to the driver's emotions, visibility is improved and appropriate feedback is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the driver's emotion data into the generation AI and have the generation AI adjust the display method of the evaluation results.

[0080] The analysis unit can perform a relative evaluation by comparing with the driving data of other drivers. The analysis unit can perform a relative evaluation by comparing with, for example, sudden braking data of other drivers. The analysis unit can also perform a relative evaluation by comparing with sudden acceleration data of other drivers. The analysis unit can also perform a relative evaluation by comparing with lane change data of other drivers. This makes it possible to perform a relative evaluation by comparing with the driving data of other drivers. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the driving data of other drivers into the generation AI and cause the generation AI to perform a relative evaluation.

[0081] The analysis unit can estimate the driver's health state based on the driving data and reflect it in the driving style. The analysis unit can estimate the driver's stress level based on data on sudden braking and sudden acceleration, for example. The analysis unit can also estimate the driver's attention level based on data on lane changes. The analysis unit can also estimate the driver's fatigue level based on speed fluctuation data. In this way, the driver's health state can be estimated based on the driving data and reflected in the evaluation of the driving style. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI. For example, the analysis unit can input driving data to the generation AI and cause the generation AI to estimate the driver's health state and reflect it in the driving style.

[0082] The analysis unit can evaluate a comprehensive driving style by including vehicle performance data in the analysis. For example, the analysis unit can evaluate sudden braking by including vehicle braking performance data in the analysis. The analysis unit can also evaluate sudden acceleration by including vehicle acceleration performance data in the analysis. The analysis unit can also evaluate lane changes by including vehicle handling performance data in the analysis. This makes it possible to evaluate a comprehensive driving style by including vehicle performance data in the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input vehicle performance data into the generation AI and cause the generation AI to perform a comprehensive driving style evaluation.

[0083] The variance unit can estimate the driver's emotions and adjust the fluctuation range of the insurance premium based on the estimated driver's emotions. For example, the variance unit can reduce the fluctuation range of the insurance premium when the driver is stressed. The variance unit can also normalize the fluctuation range of the insurance premium when the driver is relaxed. The variance unit can also increase the fluctuation range of the insurance premium when the driver is excited. This allows for more appropriate premium setting by adjusting the fluctuation range of the insurance premium according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the variance unit can be performed using AI, for example, or without AI. For example, the variance unit can input the driver's emotion data into the generation AI and cause the generation AI to adjust the fluctuation range of the insurance premium.

[0084] The variation unit can vary the insurance premium in real time based on the driving style evaluation results. The variation unit varies the insurance premium in real time based on, for example, the evaluation results of sudden braking. The variation unit can also vary the insurance premium in real time based on the evaluation results of sudden acceleration. The variation unit can also vary the insurance premium in real time based on the evaluation results of lane changes. This allows for real-time variation of the insurance premium based on the driving style evaluation results, making it possible to immediately set appropriate premiums. Some or all of the above-mentioned processing in the variation unit may be performed using AI, for example, or may be performed without using AI. For example, the variation unit can input the driving style evaluation results into a generation AI and cause the generation AI to perform real-time variation of the insurance premium.

[0085] The variation unit can vary the premium based on the driver's past insurance usage history. For example, the variation unit can apply a discount to the insurance premium based on the driver's past insurance usage history. The variation unit can also apply an increase in the insurance premium based on the driver's past insurance usage history. The variation unit can also vary the premium under specific conditions based on the driver's past insurance usage history. This enables more appropriate premium setting by taking into account the driver's past insurance usage history. Some or all of the above-mentioned processing in the variation unit may be performed using AI, for example, or may be performed without using AI. For example, the variation unit can input data on the driver's past insurance usage history into the generation AI and have the generation AI execute the premium variation.

[0086] The variation unit can apply different fare variations to specific driving behaviors. For example, the variation unit can apply a discount to insurance premiums when driving at night. The variation unit can also apply an increase in insurance premiums when driving long distances. The variation unit can also apply different fare variations to specific driving behaviors (e.g., driving on a highway). This allows for appropriate pricing according to driving behaviors to be set by applying different fare variations to specific driving behaviors. Some or all of the above-described processing in the variation unit may be performed using, or without, AI, for example. For example, the variation unit can input data of specific driving behaviors into a generation AI and have the generation AI apply fare variations.

[0087] The variance unit can estimate the driver's emotions and adjust the insurance premium notification method based on the estimated driver's emotions. For example, if the driver is nervous, the variance unit can provide a simple, highly visible notification method. Furthermore, if the driver is relaxed, the variance unit can provide a notification method that includes detailed information. Furthermore, if the driver is in a hurry, the variance unit can provide a notification method that focuses on the main points. This allows the insurance premium notification method to be adjusted according to the driver's emotions, improving visibility and providing appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the variance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the variance unit can input the driver's emotion data into the generation AI and cause the generation AI to adjust the insurance premium notification method.

[0088] The variable section can propose a comprehensive insurance premium in conjunction with other insurance products. The variable section can propose a comprehensive insurance premium based on, for example, health insurance data. The variable section can also propose a comprehensive insurance premium based on home insurance data. The variable section can also propose a comprehensive insurance premium based on data from other insurance products (e.g., travel insurance). This makes it possible to propose a comprehensive insurance premium by linking with other insurance products. Some or all of the above-mentioned processing in the variable section may be performed using, for example, AI, or may be performed without using AI. For example, the variable section can input data from other insurance products into the generation AI and have the generation AI propose a comprehensive insurance premium.

[0089] The variable unit can customize the insurance premium based on the driver's family structure and lifestyle. The variable unit customizes the insurance premium based on, for example, the driver's family structure. The variable unit can also customize the insurance premium based on the driver's lifestyle (e.g., commute distance). The variable unit can also customize the insurance premium based on the driver's hobbies and activities (e.g., outdoor activities). This enables more appropriate premium setting by customizing the insurance premium based on the driver's family structure and lifestyle. Some or all of the above-mentioned processing in the variable unit may be performed using, or without, AI, for example. For example, the variable unit can input data on the driver's family structure and lifestyle into the generation AI and have the generation AI customize the insurance premium.

[0090] The variation unit can vary the insurance premium taking into account the driver's social credit score. The variation unit can, for example, apply a discount to the insurance premium based on the driver's credit score. The variation unit can also apply an increase in the insurance premium based on the driver's credit score. The variation unit can also vary the premium under certain conditions based on the driver's credit score. This allows for more appropriate premium setting by taking the driver's social credit score into account. Some or all of the above-mentioned processing in the variation unit can be performed using AI, for example, or without using AI. For example, the variation unit can input data on the driver's credit score into the generation AI and have the generation AI vary the insurance premium. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and variation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects driving data from sensors and cameras mounted on the vehicle. For example, a speed sensor is installed as part of the smart device 14 and measures the vehicle's speed. For example, an acceleration sensor is installed as part of the smart device 14 and measures the vehicle's acceleration. For example, a camera is installed as part of the smart device 14 and captures images of the vehicle's surroundings. The analysis unit uses a generation AI to analyze the data collected by the collection unit and evaluate the driver's driving style. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12. The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. For example, the variation unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and variation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects driving data from sensors and cameras mounted on the vehicle. For example, a speed sensor is installed as part of the smart glasses 214 and measures the vehicle's speed. For example, an acceleration sensor is installed as part of the smart glasses 214 and measures the vehicle's acceleration. For example, a camera is installed as part of the smart glasses 214 and captures the vehicle's surroundings. The analysis unit uses a generation AI to analyze the data collected by the collection unit and evaluate the driver's driving style. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12. The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. For example, the variation unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and variation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects driving data from sensors and cameras mounted on the vehicle. For example, a speed sensor is installed as part of the headset terminal 314 and measures the vehicle's speed. For example, an acceleration sensor is installed as part of the headset terminal 314 and measures the vehicle's acceleration. For example, a camera is installed as part of the headset terminal 314 and captures images of the vehicle's surroundings. The analysis unit uses a generation AI to analyze the data collected by the collection unit and evaluate the driver's driving style. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12. The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. For example, the variation unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and variation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects driving data from sensors and cameras mounted on the vehicle. For example, a speed sensor is installed as part of the robot 414 and measures the vehicle's speed. For example, an acceleration sensor is installed as part of the robot 414 and measures the vehicle's acceleration. For example, a camera is installed as part of the robot 414 and captures the vehicle's surroundings. The analysis unit uses a generation AI to analyze the data collected by the collection unit and evaluate the driver's driving style. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12. The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. For example, the variation unit is realized by the specific processing unit 290 of the data processing device 12.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can take the driver's health condition into account when evaluating the driver's driving style. For example, sensors can be used to measure the driver's heart rate and stress level, and this data can be reflected in the evaluation of driving style. If the driver is under high stress, the evaluation criteria for sudden braking and sudden acceleration can be relaxed. Also, if the driver is relaxed, the normal evaluation criteria can be applied. This allows for flexible evaluation according to the driver's health condition.

[0093] The collection unit can change the frequency of data collection based on the maintenance status of the vehicle. For example, immediately after the vehicle has undergone scheduled maintenance, the frequency of data collection can be reduced to collect normal driving data. Also, if the vehicle has not undergone maintenance for a long period of time, the frequency of data collection can be increased to collect detailed driving data. Also, if an abnormality is detected in the vehicle, the frequency of data collection can be maximized to collect detailed data about the abnormality. In this way, by adjusting the frequency of data collection according to the maintenance status of the vehicle, appropriate data can be collected.

[0094] The analysis unit can estimate the driver's emotions and adjust the evaluation criteria for driving style based on the estimated driver's emotions. For example, if the driver is stressed, the evaluation criteria for sudden braking and sudden acceleration can be made stricter. If the driver is relaxed, the normal evaluation criteria can be applied. If the driver is excited, the evaluation criteria for lane changes and speed fluctuations can be made stricter. This allows for more accurate evaluation by adjusting the evaluation criteria for driving style according to the driver's emotions.

[0095] The variance unit can apply different fare variations to specific driving behaviors. For example, it can apply a discount to insurance premiums when driving at night. It can also apply an increase in insurance premiums when driving long distances. It can also apply different fare variations to specific driving behaviors (e.g., driving on a highway). By applying different fare variations to specific driving behaviors, it becomes possible to set appropriate fare according to the driving behavior.

[0096] The collection unit can also integrate and collect data from the driver's smartphone or wearable device. For example, the collection unit can integrate and collect location information from the driver's smartphone. The collection unit can also integrate and collect heart rate data from the driver's wearable device. The collection unit can also integrate and collect driving app data from the driver's smartphone. By integrating and collecting data from the smartphone and wearable device, more detailed driving data can be obtained.

[0097] The analysis unit can estimate the driver's health state based on driving data and reflect it in the driving style. For example, the stress level of the driver can be estimated based on data on sudden braking and sudden acceleration. The driver's level of attention can also be estimated based on data on lane changes. The driver's level of fatigue can also be estimated based on data on speed fluctuations. In this way, the driver's health state can be estimated based on driving data, and this can be reflected in the evaluation of driving style.

[0098] The fluctuation unit can estimate the driver's emotions and adjust the fluctuation range of the insurance premium based on the estimated driver's emotions. For example, if the driver is stressed, the fluctuation range of the insurance premium can be reduced. Also, if the driver is relaxed, the fluctuation range of the insurance premium can be normal. Also, if the driver is excited, the fluctuation range of the insurance premium can be increased. In this way, by adjusting the fluctuation range of the insurance premium according to the driver's emotions, more appropriate premium setting becomes possible.

[0099] The collection unit can acquire data from other vehicles in real time and compare the data with each other. For example, sudden braking data from other vehicles can be acquired in real time and compared with data from the vehicle itself. It can also acquire sudden acceleration data from other vehicles in real time and compare it with data from the vehicle itself. It can also acquire lane change data from other vehicles in real time and compare it with data from the vehicle itself. This makes it possible to evaluate relative driving styles by acquiring and comparing data from other vehicles in real time.

[0100] The analysis unit can switch evaluation algorithms according to different driving scenarios (urban areas, highways, mountain roads, etc.). For example, in urban areas, the evaluation can place emphasis on data on ignoring traffic lights and stopping at designated stops. On highways, the evaluation can also place emphasis on data on speed fluctuations and lane changes. On mountain roads, the evaluation can also place emphasis on data on sharp curves and steep gradients. This allows the evaluation to be tailored to each scenario by switching the evaluation algorithm according to different driving scenarios.

[0101] The analysis unit can estimate the driver's emotions and adjust the display method of the evaluation results based on the estimated driver's emotions. For example, if the driver is nervous, a simple, highly visible display method can be provided. If the driver is relaxed, a display method including detailed information can be provided. If the driver is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the evaluation results according to the driver's emotions, visibility is improved and appropriate feedback is provided.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects driving data from sensors or cameras installed in the vehicle. The driving data includes speed, acceleration, frequency of braking, etc. For example, a speed sensor is used to measure the vehicle's speed, and an acceleration sensor is used to measure the vehicle's acceleration. In addition, a camera can be used to capture images of the vehicle's surroundings and collect these as driving data. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and evaluate the driver's driving style. The evaluation of driving style includes the number of sudden braking and sudden acceleration, the frequency of lane changes, etc. For example, the driver's driving style is evaluated based on the number of sudden braking, sudden acceleration, and lane changes. Step 3: The variation unit varies the insurance premium based on the driving style evaluated by the analysis unit. For example, a discount on the insurance premium is applied to drivers who drive safely, and an increase on the insurance premium is applied to drivers who drive dangerously. A discount on the insurance premium can be applied to drivers who rarely brake or accelerate suddenly, and an increase on the insurance premium can be applied to drivers who frequently brake or accelerate suddenly.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. a collection unit that collects driving data from a sensor or a camera mounted on the vehicle; an analysis unit that analyzes the data collected by the collection unit and evaluates the driving style of the driver; and a varying unit that varies the insurance premium based on the driving style evaluated by the analyzing unit. A system characterized by:

2. The collecting unit Speed ​​sensors, acceleration sensors, and cameras are installed on the front, rear, left, and right sides of the vehicle.

2. The system of claim 1.

3. The analysis unit Evaluate the driver's driving style based on the number of sudden braking or accelerations and frequency of lane changes 2. The system of claim 1.

4. The variable section is Offering discounts on insurance premiums to drivers who drive safely and increasing premiums to drivers who drive dangerously 2. The system of claim 1.

5. The variable section is A 10% discount will be applied to drivers who rarely brake or accelerate suddenly, and a 10% increase will be applied to drivers who frequently brake or accelerate suddenly.

2. The system of claim 1.

6. The collecting unit Estimate the driver's emotions and adjust the timing of driving data collection based on the estimated driver emotions.

2. The system of claim 1.

7. The collecting unit Automatically adjusts sensor sensitivity depending on weather or road conditions 2. The system of claim 1.

8. The collecting unit Vary the frequency of data collection based on the vehicle's maintenance status 2. The system of claim 1.

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