system

Abstract

The system according to the embodiment aims to evaluate the driving safety of elderly drivers and calculate appropriate insurance premiums. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a scoring unit, a calculation unit, a sharing unit, and a notification unit. The collection unit collects data from cameras and sensors. The analysis unit analyzes the data collected by the collection unit and evaluates driving safety. The scoring unit performs scoring based on the evaluation results obtained by the analysis unit. The calculation unit calculates insurance premiums based on the scores obtained by the scoring unit. The sharing unit shares information when the score falls below a certain standard. The notification unit notifies specific recipients of the information via the sharing unit.

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 has the problem that it is difficult to evaluate the driving safety of elderly drivers in real time and calculate appropriate insurance premiums.

[0005] The system according to the embodiment aims to evaluate the driving safety of elderly drivers and calculate appropriate insurance premiums. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a scoring unit, a calculation unit, a sharing unit, and a notification unit. The collection unit collects data from cameras and sensors. The analysis unit analyzes the data collected by the collection unit and evaluates driving safety. The scoring unit performs scoring based on the evaluation results obtained by the analysis unit. The calculation unit calculates insurance premiums based on the scores obtained by the scoring unit. The sharing unit shares information when the scores fall below a certain standard. The notification unit notifies specific recipients of the information via the sharing unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the driving safety of elderly drivers and calculate appropriate insurance premiums. [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 system according to an embodiment of the present invention uses cameras and sensors installed in vehicles to monitor and score the driving behavior of elderly drivers in real time. The system collects data from the cameras and sensors, analyzes it using AI, and scores the driver's driving safety. Insurance companies then calculate insurance premiums based on the scores. If the score falls below a certain threshold, the insurance company shares the information with family members and the police, and uses it as reference information for license surrender recommendations and license renewal recommendations. This allows the system to monitor the elderly driver's driving safety in real time and score the driver's driving behavior. This information can then be used as reference information for insurance premium calculations, license surrender recommendations, and license renewal recommendations. For example, the system records detailed vehicle behavior and driver actions while driving. For example, it collects data on sudden braking, sudden acceleration, lane departures, etc. Next, AI analyzes the collected data. The AI ​​evaluates driving safety based on the collected data and generates a score. For example, it analyzes the frequency of sudden braking, sudden acceleration, and lane departures to quantify driving safety. Insurance premiums are then calculated based on the scoring results. Insurance companies then set premiums based on the scoring results. For example, if the score is high, insurance premiums are set lower, and if the score is low, insurance premiums are set higher. Also, if the score falls below a certain standard, the insurance company will share that information with family members and the police. For example, if the score is low, the family will be notified and they will be asked to surrender their license. The information will also be provided to the police, who will use it as reference when renewing their license. This system can encourage safe driving among elderly drivers and reduce the risk of accidents.

[0029] A driving evaluation system according to an embodiment includes a collection unit, an analysis unit, a scoring unit, a calculation unit, a sharing unit, and a notification unit. The collection unit collects data from cameras and sensors. The collection unit records, for example, detailed vehicle behavior and driver actions during driving. For example, the collection unit can collect data on sudden braking, sudden acceleration, lane departure, etc. The collection unit can also collect behavioral data on the driver's gaze and hand movements. The analysis unit analyzes the data collected by the collection unit to evaluate driving safety. The analysis unit analyzes data on sudden braking, sudden acceleration, lane departure, etc., to quantify the driving safety. For example, the analysis unit can analyze the number of sudden brakings and the frequency of sudden acceleration to evaluate driving safety. The analysis unit can also analyze the number of lane departures to evaluate driving safety. The scoring unit performs scoring based on the evaluation results obtained by the analysis unit. For example, the scoring unit evaluates driving safety and performs scoring. For example, the scoring unit can perform scoring based on the number of sudden brakings or the frequency of sudden accelerations. The scoring unit can also perform scoring based on the number of lane departures. The calculation unit calculates the insurance premium based on the score obtained by the scoring unit. The calculation unit, for example, sets the insurance premium based on the scoring result. For example, the calculation unit can set the insurance premium low if the score is high and high if the score is low. The sharing unit shares information when the score falls below a certain standard. For example, the sharing unit shares the information with family members and the police if the score falls below a certain standard. For example, if the score is low, the sharing unit notifies the family members and suggests that the driver return his / her license. The sharing unit can also provide the police with information that can be used as reference information when renewing a driver's license. The notification unit notifies the information to a specific recipient via the sharing unit. For example, the notification unit notifies the family members and suggests that the driver return his / her license. For example, if the score is low, the notification unit can notify the family members and suggest that the driver return his / her license. The notification unit can also provide information to the police and use it as reference information when renewing a driver's license.As a result, the driving evaluation system of the embodiment can check and score the driving of elderly drivers in real time, and can be used as reference information for calculating insurance premiums, proposing license surrender, and license renewal.

[0030] The collection unit can specifically record the vehicle behavior and driver behavior during driving. The collection unit, for example, records the vehicle behavior during driving. For example, the collection unit can record data such as speed, acceleration, and braking operation. The collection unit also records the driver's behavior. For example, the collection unit can record data such as steering operation, eye movement, and fatigue state. This allows for detailed recording of the vehicle behavior and driver behavior during driving, thereby obtaining basic data for evaluating driving safety. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data acquired from a camera or sensor into AI, which then analyzes and records the data.

[0031] The analysis unit can analyze data such as sudden braking, sudden acceleration, and lane departure to quantify driving safety. The analysis unit, for example, analyzes data on sudden braking. For example, the analysis unit can analyze the number of sudden braking events and deceleration to evaluate driving safety. The analysis unit can also analyze data on sudden acceleration. For example, the analysis unit can analyze the number of sudden acceleration events and acceleration to evaluate driving safety. The analysis unit can also analyze data on lane departure. For example, the analysis unit can analyze the number of lane departure events and the distance of departure to evaluate driving safety. In this way, by analyzing data such as sudden braking, sudden acceleration, and lane departure and quantifying driving safety, driving safety can be objectively evaluated. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired from the collection unit into AI, which can analyze the data and quantify driving safety.

[0032] The scoring unit can evaluate driving safety and perform scoring. The scoring unit can evaluate driving safety and perform scoring, for example. For example, the scoring unit can perform scoring based on the number of sudden brakings and the frequency of sudden accelerations. The scoring unit can also perform scoring based on the number of lane departures. In this way, by evaluating driving safety and performing scoring, driving safety can be quantified and used for calculating insurance premiums. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data and performs scoring.

[0033] The calculation unit can set the insurance premium based on the scoring result. The calculation unit, for example, sets the insurance premium based on the scoring result. For example, the calculation unit can set the insurance premium low if the score is high and high if the score is low. By setting the insurance premium based on the scoring result, it becomes possible to set the insurance premium according to the driving safety. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data obtained from the scoring unit into AI, which analyzes the data and sets the insurance premium.

[0034] The sharing unit can share information with specific recipients when the score falls below a certain standard. For example, the sharing unit shares information with family members or the police when the score falls below a certain standard. For example, if the score is low, the sharing unit notifies family members and suggests that the driver surrender their license. The sharing unit can also provide information to the police, which can use it as reference information when renewing a driver's license. In this way, by sharing information with family members and the police when the score falls below a certain standard, safe driving among elderly drivers can be promoted and the risk of accidents can be reduced. Some or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input data obtained from the scoring unit into AI, which analyzes the data and shares the information.

[0035] The notification unit can notify the family and suggest that the driver return his / her license. The notification unit can, for example, notify the family and suggest that the driver return his / her license. For example, if the score is low, the notification unit can notify the family and suggest that the driver return his / her license. By notifying the family and suggesting that the driver return his / her license, safe driving by elderly drivers can be promoted and the risk of accidents can be reduced. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input data acquired from the sharing unit into AI, which can analyze the data and make a notification.

[0036] The collection unit records the vehicle behavior and driver behavior during driving in detail, and can detect abnormal behavior in real time. The collection unit, for example, records the vehicle behavior during driving in detail. For example, the collection unit may record the frequency of sudden braking and sudden acceleration in real time to detect abnormal behavior. The collection unit may also record the number of lane departures in real time to detect abnormal behavior. The collection unit may also record the driver's gaze and hand movements in real time to detect abnormal behavior. This allows for detailed recording of the vehicle behavior and driver behavior during driving and detection of abnormal behavior in real time, thereby improving driving safety. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data acquired from a camera or sensor into AI, which then analyzes the data to detect abnormal behavior in real time.

[0037] The collection unit can collect data taking into account the driving environment based on the collected data. The collection unit, for example, collects data taking into account the driving environment based on the collected data. For example, the collection unit collects data taking into account slippery road conditions when it is raining. Furthermore, the collection unit can collect data taking into account congestion when there is heavy traffic. Furthermore, the collection unit can collect data taking into account poor visibility when driving at night. This allows for more accurate evaluation of driving safety by collecting data taking into account the driving environment. 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 driving environment data into AI, which analyzes the data and collects data taking into account the driving environment.

[0038] The collection unit can refer to the driver's past driving history and focus on collecting specific driving patterns. The collection unit, for example, refers to the driver's past driving history and focus on collecting specific driving patterns. For example, the collection unit can focus on collecting driving patterns of drivers who have frequently braked suddenly in the past. The collection unit can also focus on collecting driving patterns of drivers who have frequently deviated from their lanes in the past. The collection unit can also focus on collecting driving patterns of drivers who have frequently exceeded the speed limit in the past. In this way, by referring to the driver's past driving history and focusing on collecting specific driving patterns, driving safety can be improved. 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 past driving history data into AI, which analyzes the data and focuses on collecting specific driving patterns.

[0039] The collection unit can monitor the driver's health state based on the collected data. The collection unit monitors the driver's health state based on, for example, the collected data. For example, the collection unit can monitor the driver's heart rate and respiratory rate to evaluate the driver's fatigue state. The collection unit can also monitor the driver's facial expressions and tone of voice to evaluate the driver's stress state. The collection unit can also monitor the driver's driving behavior to comprehensively evaluate the driver's health state. In this way, driving safety can be improved by monitoring the driver's health state. 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 the driver's biometric data into AI, which can analyze the data to monitor the driver's health state.

[0040] The collection unit can evaluate the vehicle's maintenance status based on the collected data and suggest necessary maintenance. The collection unit can, for example, evaluate the vehicle's maintenance status based on the collected data and suggest necessary maintenance. For example, the collection unit can monitor the frequency of brake use and suggest when to replace brake pads. The collection unit can also monitor the engine's operating time and suggest when to change oil. The collection unit can also monitor the tire wear state and suggest when to replace tires. In this way, the vehicle's maintenance status can be evaluated and necessary maintenance can be suggested, thereby improving vehicle safety. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI, for example. For example, the collection unit can input vehicle maintenance data into AI, which can analyze the data and suggest necessary maintenance.

[0041] The collection unit can provide driving advice according to the driver's driving style based on the collected data. The collection unit, for example, provides driving advice according to the driver's driving style based on the collected data. For example, the collection unit can provide advice to a driver who frequently brakes suddenly on how to improve brake usage. The collection unit can also advise a driver who frequently deviates from their lane on how to stay in their lane. The collection unit can also advise a driver who frequently speeds to drive at an appropriate speed. This allows driving safety to be improved by providing driving advice according to the driver's driving style. 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 driving data into AI, which analyzes the data and provides driving advice.

[0042] The analysis unit can identify risk factors for driving based on the analysis results and perform risk assessment. The analysis unit can, for example, identify risk factors for driving based on the analysis results and perform risk assessment. For example, the analysis unit can identify a high frequency of sudden braking as a risk factor and perform risk assessment. The analysis unit can also identify a high frequency of sudden acceleration as a risk factor and perform risk assessment. The analysis unit can also identify a high number of lane departures as a risk factor and perform risk assessment. In this way, by identifying risk factors for driving and performing risk assessment, driving safety can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data acquired from the collection unit into AI, which can analyze the data to identify risk factors and perform risk assessment.

[0043] The analysis unit can identify driving improvements based on the analysis results and propose specific improvement measures. The analysis unit can, for example, identify driving improvements based on the analysis results and propose specific improvement measures. For example, if there are frequent sudden braking, the analysis unit can propose specific measures to improve braking usage. Furthermore, if there are frequent sudden accelerations, the analysis unit can also propose specific measures to improve acceleration methods. Furthermore, if there are frequent lane departures, the analysis unit can also propose specific measures to improve lane-keeping methods. In this way, by identifying driving improvements and proposing specific improvement measures, driving safety can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired from the collection unit into AI, which can analyze the data to identify driving improvements and propose specific improvement measures.

[0044] The analysis unit can evaluate the driver's driving skills based on the analysis data and suggest a training program to improve those skills. For example, the analysis unit can evaluate the driver's driving skills based on the analysis data and suggest a training program to improve those skills. For example, the analysis unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The analysis unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The analysis unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lane. By evaluating the driver's driving skills and suggesting a training program to improve those skills, driving safety can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data acquired from the collection unit into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve the skills.

[0045] The analysis unit can evaluate the influence of the driving environment based on the analysis data and provide driving advice appropriate for the environment. For example, the analysis unit can evaluate the influence of the driving environment based on the analysis data and provide driving advice appropriate for the environment. For example, the analysis unit can analyze driving data in rainy weather and provide driving advice for slippery roads. The analysis unit can also analyze driving data in heavy traffic and provide driving advice for congested situations. The analysis unit can also analyze data from nighttime driving and provide driving advice for poor visibility. This allows for the evaluation of the influence of the driving environment and the provision of driving advice appropriate for the environment, thereby improving driving safety. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input driving environment data to AI, which analyzes the data to evaluate the influence of the driving environment and provide driving advice appropriate for the environment.

[0046] The analysis unit can propose an insurance plan according to the driver's driving style based on the analysis data. The analysis unit, for example, proposes an insurance plan according to the driver's driving style based on the analysis data. For example, the analysis unit can propose an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The analysis unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The analysis unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired from the collection unit into AI, which analyzes the data and proposes an insurance plan according to the driving style.

[0047] The scoring unit can evaluate the risk level of driving based on the scoring result. The scoring unit evaluates the risk level of driving based on, for example, the scoring result. For example, the scoring unit evaluates the risk level as high if the frequency of sudden braking is high. The scoring unit can also evaluate the risk level as high if the frequency of sudden acceleration is high. The scoring unit can also evaluate the risk level as high if the number of lane departures is high. In this way, by evaluating the risk level of driving, driving safety can be improved. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data and evaluates the risk level of driving.

[0048] The scoring unit can identify driving improvement points based on the scoring results and propose specific improvement measures. The scoring unit can identify driving improvement points based on the scoring results, for example, and propose specific improvement measures. For example, if there are frequent sudden braking, the scoring unit can propose specific measures to improve braking usage. Furthermore, if there are frequent sudden accelerations, the scoring unit can also propose specific measures to improve acceleration methods. Furthermore, if there are frequent lane departures, the scoring unit can also propose specific measures to improve lane keeping methods. In this way, by identifying driving improvement points and proposing specific improvement measures, driving safety can be improved. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data to identify driving improvement points and propose specific improvement measures.

[0049] The scoring unit can evaluate the driver's driving skills based on the scoring results and suggest a training program to improve the skills. For example, the scoring unit can evaluate the driver's driving skills based on the scoring results and suggest a training program to improve the skills. For example, the scoring unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The scoring unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The scoring unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lanes. In this way, by evaluating the driver's driving skills and suggesting a training program to improve the skills, driving safety can be improved. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve the skills.

[0050] The scoring unit can evaluate the influence of the driving environment based on the scoring results and provide driving advice appropriate to the environment. For example, the scoring unit can evaluate the influence of the driving environment based on the scoring results and provide driving advice appropriate to the environment. For example, the scoring unit can analyze driving data in rainy weather and provide driving advice for slippery roads. The scoring unit can also analyze driving data in heavy traffic and provide driving advice for congested situations. The scoring unit can also analyze data from nighttime driving and provide driving advice for poor visibility. This allows for the evaluation of the influence of the driving environment and the provision of driving advice appropriate to the environment, thereby improving driving safety. Some or all of the above-described processing in the scoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the scoring unit can input driving environment data into AI, which analyzes the data to evaluate the influence of the driving environment and provide driving advice appropriate to the environment.

[0051] The scoring unit can propose an insurance plan according to the driver's driving style based on the scoring result. The scoring unit, for example, proposes an insurance plan according to the driver's driving style based on the scoring result. For example, the scoring unit can propose an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The scoring unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The scoring unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data and proposes an insurance plan according to the driving style.

[0052] The calculation unit can take into account the driver's past driving history when calculating the insurance premium. The calculation unit, for example, takes into account the driver's past driving history when calculating the insurance premium. For example, the calculation unit sets a high insurance premium for a driver who has had many accidents in the past. The calculation unit can also set a low insurance premium for a driver who has had many safe driving in the past. The calculation unit can also set a medium insurance premium for a driver with a medium driving history in the past. This makes it possible to set a more appropriate insurance premium by taking into account the driver's past driving history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the driver's past driving history data into AI, which analyzes the data and calculates the insurance premium.

[0053] The calculation unit can take the driving environment into consideration when calculating the insurance premium. The calculation unit, for example, takes the driving environment into consideration when calculating the insurance premium. For example, the calculation unit sets the insurance premium taking into consideration driving data in rainy weather. The calculation unit can also set the insurance premium taking into consideration driving data when traffic is heavy. The calculation unit can also set the insurance premium taking into consideration data when driving at night. This makes it possible to set a more appropriate insurance premium by taking the driving environment into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input driving environment data into AI, which analyzes the data and calculates the insurance premium.

[0054] The calculation unit can take the driver's health condition into consideration when calculating the insurance premium. The calculation unit, for example, takes the driver's health condition into consideration when calculating the insurance premium. For example, the calculation unit can monitor the driver's heart rate and respiratory rate, evaluate the driver's fatigue state, and set the insurance premium. The calculation unit can also monitor the driver's facial expression and tone of voice, evaluate the driver's stress state, and set the insurance premium. The calculation unit can also monitor the driver's driving behavior, comprehensively evaluate the driver's health condition, and set the insurance premium. This makes it possible to set a more appropriate insurance premium by taking the driver's health condition into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the driver's biometric data into AI, which can analyze the data and calculate the insurance premium.

[0055] The calculation unit can evaluate the driver's driving skills when calculating insurance premiums and suggest a training program to improve those skills. For example, the calculation unit can evaluate the driver's driving skills and suggest a training program to improve those skills when calculating insurance premiums. For example, the calculation unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The calculation unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The calculation unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lane. In this way, by evaluating the driver's driving skills and suggesting a training program to improve those skills, driving safety can be improved. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input the driver's driving data into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve those skills.

[0056] The calculation unit can propose an insurance plan according to the driver's driving style when calculating the insurance premium. For example, the calculation unit proposes an insurance plan according to the driver's driving style when calculating the insurance premium. For example, the calculation unit proposes an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The calculation unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The calculation unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the driver's driving data into AI, which analyzes the data and proposes an insurance plan according to the driving style.

[0057] The sharing unit can take into account the driver's past driving history when sharing information. For example, the sharing unit takes into account the driver's past driving history when sharing information. For example, the sharing unit shares information about drivers who have had many accidents in the past with family members and the police. The sharing unit can also share information about drivers who have had many safe driving experiences in the past with family members and the police. The sharing unit can also share information about drivers with average driving histories with family members and the police. This enables more appropriate information sharing by taking into account the driver's past driving history. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the driver's past driving history data into AI, which analyzes the data and shares the information.

[0058] The sharing unit can take the driving environment into consideration when sharing information. The sharing unit, for example, takes the driving environment into consideration when sharing information. For example, the sharing unit shares information taking into consideration driving data in rainy weather. The sharing unit can also share information taking into consideration driving data when traffic is heavy. The sharing unit can also share information taking into consideration data when driving at night. This enables more appropriate information sharing by taking the driving environment into consideration. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input driving environment data into AI, which analyzes the data and shares the information.

[0059] The sharing unit can take the driver's health state into consideration when sharing information. The sharing unit, for example, takes the driver's health state into consideration when sharing information. For example, the sharing unit can monitor the driver's heart rate and respiratory rate, evaluate the driver's fatigue state, and share the information. The sharing unit can also monitor the driver's facial expressions and tone of voice, evaluate the driver's stress state, and share the information. The sharing unit can also monitor the driver's driving behavior, comprehensively evaluate the driver's health state, and share the information. This enables more appropriate information sharing by taking the driver's health state into consideration. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the driver's biometric data into AI, which can analyze the data and share the information.

[0060] The sharing unit can evaluate the driver's driving skills when sharing information and suggest a training program to improve those skills. For example, the sharing unit can evaluate the driver's driving skills when sharing information and suggest a training program to improve those skills. For example, the sharing unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The sharing unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The sharing unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lane. In this way, by evaluating the driver's driving skills and suggesting a training program to improve those skills, driving safety can be improved. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the driver's driving data into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve the skills.

[0061] The shared unit can propose an insurance plan according to the driver's driving style when sharing information. For example, the shared unit proposes an insurance plan according to the driver's driving style when sharing information. For example, the shared unit proposes an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The shared unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The shared unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the shared unit may be performed using, for example, AI, or may be performed without using AI. For example, the shared unit can input the driver's driving data into AI, which analyzes the data and proposes an insurance plan according to the driving style.

[0062] The notification unit can take into account the driver's past driving history when making a notification. The notification unit, for example, takes into account the driver's past driving history when making a notification. For example, the notification unit notifies family members and the police of information about drivers who have had many accidents in the past. The notification unit can also notify family members and the police of information about drivers who have had many safe driving experiences in the past. The notification unit can also notify family members and the police of information about drivers with average driving histories in the past. This enables more appropriate notification by taking into account the driver's past driving history. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the driver's past driving history data into AI, which analyzes the data and makes a notification.

[0063] The notification unit can take the driver's health state into consideration when issuing a notification. The notification unit can, for example, take the driver's health state into consideration when issuing a notification. For example, the notification unit can monitor the driver's heart rate and respiratory rate, evaluate the driver's fatigue state, and issue a notification. The notification unit can also monitor the driver's facial expression and tone of voice, evaluate the driver's stress state, and issue a notification. The notification unit can also monitor the driver's driving behavior, comprehensively evaluate the driver's health state, and issue a notification. This allows for more appropriate notification by taking the driver's health state into consideration. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the driver's biometric data into AI, which can analyze the data and issue a notification.

[0064] The notification unit can evaluate the driver's driving skills at the time of notification and suggest a training program to improve the skills. For example, the notification unit can evaluate the driver's driving skills at the time of notification and suggest a training program to improve the skills. For example, the notification unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The notification unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The notification unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lane. In this way, by evaluating the driver's driving skills and suggesting a training program to improve the skills, driving safety can be improved. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the driver's driving data into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve the skills.

[0065] The notification unit can propose an insurance plan according to the driver's driving style at the time of notification. The notification unit, for example, proposes an insurance plan according to the driver's driving style at the time of notification. For example, the notification unit can propose an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The notification unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The notification unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the driver's driving data into AI, which can analyze the data and propose an insurance plan according to the driving style.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The collection unit can monitor the driver's posture while driving and encourage them to improve their posture. For example, if the driver has been driving in the same posture for a long time, the collection unit can encourage them to change their posture. In addition, if the driver is driving in an unnatural posture, the collection unit can also advise the driver to adopt a correct posture. In addition, if the driver is tired, the collection unit can encourage the driver to take a break. In this way, driving safety can be improved by monitoring the driver's posture and maintaining appropriate posture.

[0068] The analysis unit can analyze the driver's eye movements while driving and evaluate the degree of eye concentration. For example, the analysis unit can compare the amount of time the driver spends looking ahead with the amount of time they spend looking to the side to evaluate the degree of eye concentration. The analysis unit can also evaluate the possibility of distraction if the driver frequently moves their eyes. The analysis unit can also evaluate the degree of eye concentration as high if the driver keeps their eyes fixed. In this way, by analyzing the driver's eye movements and evaluating the degree of eye concentration, driving safety can be improved.

[0069] The calculation unit can calculate insurance premiums by taking into account the driver's fuel efficiency data while driving. For example, the calculation unit can set lower insurance premiums for drivers with good fuel efficiency. The calculation unit can also set higher insurance premiums for drivers with poor fuel efficiency. The calculation unit can also provide advice on eco-driving based on the fuel efficiency data. In this way, calculating insurance premiums by taking into account the driver's fuel efficiency data can encourage eco-driving and contribute to environmental protection.

[0070] The notification unit can monitor the driver's behavior while driving in real time and issue a notification if abnormal behavior is detected. For example, if the driver frequently brakes suddenly, the notification unit can notify family members. The notification unit can also notify the police if the driver repeatedly deviates from their lane. The notification unit can also notify the driver to take a break if the driver continues driving for a long period of time. In this way, by detecting abnormal driver behavior in real time and issuing appropriate notifications, driving safety can be improved.

[0071] The analysis unit can analyze the driver's behavior while driving and identify risk factors for driving. For example, the analysis unit can identify a high frequency of sudden braking as a risk factor. The analysis unit can also identify a high number of lane departures as a risk factor. The analysis unit can also identify a high frequency of speeding as a risk factor. In this way, by analyzing the driver's behavior while driving and identifying risk factors, it is possible to improve driving safety.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects data from cameras and sensors. For example, it records the vehicle's behavior and the driver's actions in detail while driving, collecting data such as sudden braking, sudden acceleration, lane departure, and the driver's gaze and hand movements. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates driving safety. For example, it analyzes the number of sudden braking, frequency of sudden acceleration, and number of lane departures to quantify driving safety. Step 3: The scoring unit performs scoring based on the evaluation results obtained by the analysis unit, for example, based on the number of sudden braking incidents, the frequency of sudden acceleration, and the number of lane departures. Step 4: The calculation unit calculates the insurance premium based on the score obtained by the scoring unit. For example, if the score is high, the insurance premium is set low, and if the score is low, the insurance premium is set high. Step 5: The sharing department shares information if the score falls below a certain standard. For example, if the score is low, the information is provided to family members or the police, and used as reference information when proposing license surrender or license renewal. Step 6: The notification unit notifies the specified recipients of the information via the sharing unit. For example, the notification unit notifies family members and suggests that the driver surrender his / her license. The information is also provided to the police, who use it as reference information when renewing the driver's license.

[0074] (Example 2) A system according to an embodiment of the present invention uses cameras and sensors installed in vehicles to monitor and score the driving behavior of elderly drivers in real time. The system collects data from the cameras and sensors, analyzes it using AI, and scores the driver's driving safety. Insurance companies then calculate insurance premiums based on the scores. If the score falls below a certain threshold, the insurance company shares the information with family members and the police, and uses it as reference information for license surrender recommendations and license renewal recommendations. This allows the system to monitor the elderly driver's driving safety in real time and score the driver's driving behavior. This information can then be used as reference information for insurance premium calculations, license surrender recommendations, and license renewal recommendations. For example, the system records detailed vehicle behavior and driver actions while driving. For example, it collects data on sudden braking, sudden acceleration, lane departures, etc. Next, AI analyzes the collected data. The AI ​​evaluates driving safety based on the collected data and generates a score. For example, it analyzes the frequency of sudden braking, sudden acceleration, and lane departures to quantify driving safety. Insurance premiums are then calculated based on the scoring results. Insurance companies then set premiums based on the scoring results. For example, if the score is high, insurance premiums are set lower, and if the score is low, insurance premiums are set higher. Also, if the score falls below a certain standard, the insurance company will share that information with family members and the police. For example, if the score is low, the family will be notified and they will be asked to surrender their license. The information will also be provided to the police, who will use it as reference when renewing their license. This system can encourage safe driving among elderly drivers and reduce the risk of accidents.

[0075] A driving evaluation system according to an embodiment includes a collection unit, an analysis unit, a scoring unit, a calculation unit, a sharing unit, and a notification unit. The collection unit collects data from cameras and sensors. The collection unit records, for example, detailed vehicle behavior and driver actions during driving. For example, the collection unit can collect data on sudden braking, sudden acceleration, lane departure, etc. The collection unit can also collect behavioral data on the driver's gaze and hand movements. The analysis unit analyzes the data collected by the collection unit to evaluate driving safety. The analysis unit analyzes data on sudden braking, sudden acceleration, lane departure, etc., to quantify the driving safety. For example, the analysis unit can analyze the number of sudden brakings and the frequency of sudden acceleration to evaluate driving safety. The analysis unit can also analyze the number of lane departures to evaluate driving safety. The scoring unit performs scoring based on the evaluation results obtained by the analysis unit. For example, the scoring unit evaluates driving safety and performs scoring. For example, the scoring unit can perform scoring based on the number of sudden brakings or the frequency of sudden accelerations. The scoring unit can also perform scoring based on the number of lane departures. The calculation unit calculates the insurance premium based on the score obtained by the scoring unit. The calculation unit, for example, sets the insurance premium based on the scoring result. For example, the calculation unit can set the insurance premium low if the score is high and high if the score is low. The sharing unit shares information when the score falls below a certain standard. For example, the sharing unit shares the information with family members and the police if the score falls below a certain standard. For example, if the score is low, the sharing unit notifies the family members and suggests that the driver return his / her license. The sharing unit can also provide the police with information that can be used as reference information when renewing a driver's license. The notification unit notifies the information to a specific recipient via the sharing unit. For example, the notification unit notifies the family members and suggests that the driver return his / her license. For example, if the score is low, the notification unit can notify the family members and suggest that the driver return his / her license. The notification unit can also provide information to the police and use it as reference information when renewing a driver's license.As a result, the driving evaluation system of the embodiment can check and score the driving of elderly drivers in real time, and can be used as reference information for calculating insurance premiums, proposing license surrender, and license renewal.

[0076] The collection unit can specifically record the vehicle behavior and driver behavior during driving. The collection unit, for example, records the vehicle behavior during driving. For example, the collection unit can record data such as speed, acceleration, and braking operation. The collection unit also records the driver's behavior. For example, the collection unit can record data such as steering operation, eye movement, and fatigue state. This allows for detailed recording of the vehicle behavior and driver behavior during driving, thereby obtaining basic data for evaluating driving safety. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data acquired from a camera or sensor into AI, which then analyzes and records the data.

[0077] The analysis unit can analyze data such as sudden braking, sudden acceleration, and lane departure to quantify driving safety. The analysis unit, for example, analyzes data on sudden braking. For example, the analysis unit can analyze the number of sudden braking events and deceleration to evaluate driving safety. The analysis unit can also analyze data on sudden acceleration. For example, the analysis unit can analyze the number of sudden acceleration events and acceleration to evaluate driving safety. The analysis unit can also analyze data on lane departure. For example, the analysis unit can analyze the number of lane departure events and the distance of departure to evaluate driving safety. In this way, by analyzing data such as sudden braking, sudden acceleration, and lane departure and quantifying driving safety, driving safety can be objectively evaluated. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired from the collection unit into AI, which can analyze the data and quantify driving safety.

[0078] The scoring unit can evaluate driving safety and perform scoring. The scoring unit can evaluate driving safety and perform scoring, for example. For example, the scoring unit can perform scoring based on the number of sudden brakings and the frequency of sudden accelerations. The scoring unit can also perform scoring based on the number of lane departures. In this way, by evaluating driving safety and performing scoring, driving safety can be quantified and used for calculating insurance premiums. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data and performs scoring.

[0079] The calculation unit can set the insurance premium based on the scoring result. The calculation unit, for example, sets the insurance premium based on the scoring result. For example, the calculation unit can set the insurance premium low if the score is high and high if the score is low. By setting the insurance premium based on the scoring result, it becomes possible to set the insurance premium according to the driving safety. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data obtained from the scoring unit into AI, which analyzes the data and sets the insurance premium.

[0080] The sharing unit can share information with specific recipients when the score falls below a certain standard. For example, the sharing unit shares information with family members or the police when the score falls below a certain standard. For example, if the score is low, the sharing unit notifies family members and suggests that the driver surrender their license. The sharing unit can also provide information to the police, which can use it as reference information when renewing a driver's license. In this way, by sharing information with family members and the police when the score falls below a certain standard, safe driving among elderly drivers can be promoted and the risk of accidents can be reduced. Some or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input data obtained from the scoring unit into AI, which analyzes the data and shares the information.

[0081] The notification unit can notify the family and suggest that the driver return his / her license. The notification unit can, for example, notify the family and suggest that the driver return his / her license. For example, if the score is low, the notification unit can notify the family and suggest that the driver return his / her license. By notifying the family and suggesting that the driver return his / her license, safe driving by elderly drivers can be promoted and the risk of accidents can be reduced. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input data acquired from the sharing unit into AI, which can analyze the data and make a notification.

[0082] The collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated driver's emotions. For example, the collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated driver's emotions. For example, if the driver is stressed, the collection unit can increase the frequency of data collection and collect detailed data. Furthermore, if the driver is relaxed, the collection unit can reduce the frequency of data collection and collect the minimum amount of data necessary. Furthermore, if the driver is tired, the collection unit can adjust the timing of data collection and collect data to ensure driving safety. This enables more appropriate data collection by adjusting the timing of data collection based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI 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 facial expression data into the generation AI, which can then infer the driver's emotions and adjust the timing of data collection based on the results.

[0083] The collection unit records the vehicle behavior and driver behavior during driving in detail, and can detect abnormal behavior in real time. The collection unit, for example, records the vehicle behavior during driving in detail. For example, the collection unit may record the frequency of sudden braking and sudden acceleration in real time to detect abnormal behavior. The collection unit may also record the number of lane departures in real time to detect abnormal behavior. The collection unit may also record the driver's gaze and hand movements in real time to detect abnormal behavior. This allows for detailed recording of the vehicle behavior and driver behavior during driving and detection of abnormal behavior in real time, thereby improving driving safety. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data acquired from a camera or sensor into AI, which then analyzes the data to detect abnormal behavior in real time.

[0084] The collection unit can collect data taking into account the driving environment based on the collected data. The collection unit, for example, collects data taking into account the driving environment based on the collected data. For example, the collection unit collects data taking into account slippery road conditions when it is raining. Furthermore, the collection unit can collect data taking into account congestion when there is heavy traffic. Furthermore, the collection unit can collect data taking into account poor visibility when driving at night. This allows for more accurate evaluation of driving safety by collecting data taking into account the driving environment. 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 driving environment data into AI, which analyzes the data and collects data taking into account the driving environment.

[0085] The collection unit can refer to the driver's past driving history and focus on collecting specific driving patterns. The collection unit, for example, refers to the driver's past driving history and focus on collecting specific driving patterns. For example, the collection unit can focus on collecting driving patterns of drivers who have frequently braked suddenly in the past. The collection unit can also focus on collecting driving patterns of drivers who have frequently deviated from their lanes in the past. The collection unit can also focus on collecting driving patterns of drivers who have frequently exceeded the speed limit in the past. In this way, by referring to the driver's past driving history and focusing on collecting specific driving patterns, driving safety can be improved. 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 past driving history data into AI, which analyzes the data and focuses on collecting specific driving patterns.

[0086] 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. The collection unit, for example, estimates the driver's emotions and determines 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 data on lane departure. Furthermore, if the driver is tired, the collection unit can prioritize collecting data on eye movements and hand movements. Thus, by prioritizing the data to be collected based on the driver's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the driver's facial expression data into the generation AI, which can then infer the emotion and determine the priority of the data to be collected based on the results.

[0087] The collection unit can monitor the driver's health state based on the collected data. The collection unit monitors the driver's health state based on, for example, the collected data. For example, the collection unit can monitor the driver's heart rate and respiratory rate to evaluate the driver's fatigue state. The collection unit can also monitor the driver's facial expressions and tone of voice to evaluate the driver's stress state. The collection unit can also monitor the driver's driving behavior to comprehensively evaluate the driver's health state. In this way, driving safety can be improved by monitoring the driver's health state. 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 the driver's biometric data into AI, which can analyze the data to monitor the driver's health state.

[0088] The collection unit can evaluate the vehicle's maintenance status based on the collected data and suggest necessary maintenance. The collection unit can, for example, evaluate the vehicle's maintenance status based on the collected data and suggest necessary maintenance. For example, the collection unit can monitor the frequency of brake use and suggest when to replace brake pads. The collection unit can also monitor the engine's operating time and suggest when to change oil. The collection unit can also monitor the tire wear state and suggest when to replace tires. In this way, the vehicle's maintenance status can be evaluated and necessary maintenance can be suggested, thereby improving vehicle safety. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI, for example. For example, the collection unit can input vehicle maintenance data into AI, which can analyze the data and suggest necessary maintenance.

[0089] The collection unit can provide driving advice according to the driver's driving style based on the collected data. The collection unit, for example, provides driving advice according to the driver's driving style based on the collected data. For example, the collection unit can provide advice to a driver who frequently brakes suddenly on how to improve brake usage. The collection unit can also advise a driver who frequently deviates from their lane on how to stay in their lane. The collection unit can also advise a driver who frequently speeds to drive at an appropriate speed. This allows driving safety to be improved by providing driving advice according to the driver's driving style. 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 driving data into AI, which analyzes the data and provides driving advice.

[0090] The analysis unit can estimate the driver's emotions and adjust the analysis algorithm based on the estimated driver's emotions. For example, the analysis unit estimates the driver's emotions and adjusts the analysis algorithm based on the estimated driver's emotions. For example, if the driver is stressed, the analysis unit uses an analysis algorithm that emphasizes stress factors. Also, if the driver is relaxed, the analysis unit can use a normal analysis algorithm. Also, if the driver is tired, the analysis unit can use an analysis algorithm that emphasizes fatigue factors. This enables more accurate analysis by adjusting the analysis algorithm based on the driver's emotions. 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, AI, or without AI. For example, the analysis unit can input the driver's facial expression data into a generation AI, which then estimates the driver's emotions and adjusts the analysis algorithm based on the results.

[0091] The analysis unit can identify risk factors for driving based on the analysis results and perform risk assessment. The analysis unit can, for example, identify risk factors for driving based on the analysis results and perform risk assessment. For example, the analysis unit can identify a high frequency of sudden braking as a risk factor and perform risk assessment. The analysis unit can also identify a high frequency of sudden acceleration as a risk factor and perform risk assessment. The analysis unit can also identify a high number of lane departures as a risk factor and perform risk assessment. In this way, by identifying risk factors for driving and performing risk assessment, driving safety can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data acquired from the collection unit into AI, which can analyze the data to identify risk factors and perform risk assessment.

[0092] The analysis unit can identify driving improvements based on the analysis results and propose specific improvement measures. The analysis unit can, for example, identify driving improvements based on the analysis results and propose specific improvement measures. For example, if there are frequent sudden braking, the analysis unit can propose specific measures to improve braking usage. Furthermore, if there are frequent sudden accelerations, the analysis unit can also propose specific measures to improve acceleration methods. Furthermore, if there are frequent lane departures, the analysis unit can also propose specific measures to improve lane-keeping methods. In this way, by identifying driving improvements and proposing specific improvement measures, driving safety can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired from the collection unit into AI, which can analyze the data to identify driving improvements and propose specific improvement measures.

[0093] The analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated driver's emotions. For example, the analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated driver's emotions. For example, if the driver is stressed, the analysis unit can provide a simple, highly visible display method. If the driver is relaxed, the analysis unit can provide a display method that includes detailed information. If the driver is tired, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results based on the driver's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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, an AI, or without an AI. For example, the analysis unit can input the driver's facial expression data into a generation AI, which can estimate the driver's emotions and adjust the display method of the analysis results based on the estimation result.

[0094] The analysis unit can evaluate the driver's driving skills based on the analysis data and suggest a training program to improve those skills. For example, the analysis unit can evaluate the driver's driving skills based on the analysis data and suggest a training program to improve those skills. For example, the analysis unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The analysis unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The analysis unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lane. By evaluating the driver's driving skills and suggesting a training program to improve those skills, driving safety can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data acquired from the collection unit into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve the skills.

[0095] The analysis unit can evaluate the influence of the driving environment based on the analysis data and provide driving advice appropriate for the environment. For example, the analysis unit can evaluate the influence of the driving environment based on the analysis data and provide driving advice appropriate for the environment. For example, the analysis unit can analyze driving data in rainy weather and provide driving advice for slippery roads. The analysis unit can also analyze driving data in heavy traffic and provide driving advice for congested situations. The analysis unit can also analyze data from nighttime driving and provide driving advice for poor visibility. This allows for the evaluation of the influence of the driving environment and the provision of driving advice appropriate for the environment, thereby improving driving safety. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input driving environment data to AI, which analyzes the data to evaluate the influence of the driving environment and provide driving advice appropriate for the environment.

[0096] The analysis unit can propose an insurance plan according to the driver's driving style based on the analysis data. The analysis unit, for example, proposes an insurance plan according to the driver's driving style based on the analysis data. For example, the analysis unit can propose an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The analysis unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The analysis unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired from the collection unit into AI, which analyzes the data and proposes an insurance plan according to the driving style.

[0097] The scoring unit can estimate the driver's emotions and adjust the scoring criteria based on the estimated driver's emotions. For example, the scoring unit can estimate the driver's emotions and adjust the scoring criteria based on the estimated driver's emotions. For example, if the driver is stressed, the scoring unit can use scoring criteria that emphasize stress factors. If the driver is relaxed, the scoring unit can use normal scoring criteria. If the driver is tired, the scoring unit can use scoring criteria that emphasize fatigue factors. This allows for more accurate scoring by adjusting the scoring criteria based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the scoring unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the scoring unit can input the driver's facial expression data into a generative AI, which can estimate the driver's emotions and adjust the scoring criteria based on the results.

[0098] The scoring unit can evaluate the risk level of driving based on the scoring result. The scoring unit evaluates the risk level of driving based on, for example, the scoring result. For example, the scoring unit evaluates the risk level as high if the frequency of sudden braking is high. The scoring unit can also evaluate the risk level as high if the frequency of sudden acceleration is high. The scoring unit can also evaluate the risk level as high if the number of lane departures is high. In this way, by evaluating the risk level of driving, driving safety can be improved. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data and evaluates the risk level of driving.

[0099] The scoring unit can identify driving improvement points based on the scoring results and propose specific improvement measures. The scoring unit can identify driving improvement points based on the scoring results, for example, and propose specific improvement measures. For example, if there are frequent sudden braking, the scoring unit can propose specific measures to improve braking usage. Furthermore, if there are frequent sudden accelerations, the scoring unit can also propose specific measures to improve acceleration methods. Furthermore, if there are frequent lane departures, the scoring unit can also propose specific measures to improve lane keeping methods. In this way, by identifying driving improvement points and proposing specific improvement measures, driving safety can be improved. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data to identify driving improvement points and propose specific improvement measures.

[0100] The scoring unit can estimate the driver's emotions and adjust the display method of the scoring results based on the estimated driver's emotions. For example, the scoring unit can estimate the driver's emotions and adjust the display method of the scoring results based on the estimated driver's emotions. For example, if the driver is stressed, the scoring unit can provide a simple, highly visible display method. Furthermore, if the driver is relaxed, the scoring unit can provide a display method that includes detailed information. Furthermore, if the driver is tired, the scoring unit can provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the scoring results based on the driver's emotions. The 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 these examples. Some or all of the above-described processing in the scoring unit can be performed, for example, using AI, or without AI. For example, the scoring unit can input the driver's facial expression data into the generation AI, which then estimates the emotion and adjusts the way the scoring results are displayed based on that.

[0101] The scoring unit can evaluate the driver's driving skills based on the scoring results and suggest a training program to improve the skills. For example, the scoring unit can evaluate the driver's driving skills based on the scoring results and suggest a training program to improve the skills. For example, the scoring unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The scoring unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The scoring unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lanes. In this way, by evaluating the driver's driving skills and suggesting a training program to improve the skills, driving safety can be improved. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve the skills.

[0102] The scoring unit can evaluate the influence of the driving environment based on the scoring results and provide driving advice appropriate to the environment. For example, the scoring unit can evaluate the influence of the driving environment based on the scoring results and provide driving advice appropriate to the environment. For example, the scoring unit can analyze driving data in rainy weather and provide driving advice for slippery roads. The scoring unit can also analyze driving data in heavy traffic and provide driving advice for congested situations. The scoring unit can also analyze data from nighttime driving and provide driving advice for poor visibility. This allows for the evaluation of the influence of the driving environment and the provision of driving advice appropriate to the environment, thereby improving driving safety. Some or all of the above-described processing in the scoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the scoring unit can input driving environment data into AI, which analyzes the data to evaluate the influence of the driving environment and provide driving advice appropriate to the environment.

[0103] The scoring unit can propose an insurance plan according to the driver's driving style based on the scoring result. The scoring unit, for example, proposes an insurance plan according to the driver's driving style based on the scoring result. For example, the scoring unit can propose an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The scoring unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The scoring unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input data acquired from the analysis unit into AI, which analyzes the data and proposes an insurance plan according to the driving style.

[0104] The calculation unit can estimate the driver's emotions and adjust the insurance premium calculation standard based on the estimated driver's emotions. For example, the calculation unit can estimate the driver's emotions and adjust the insurance premium calculation standard based on the estimated driver's emotions. For example, if the driver is stressed, the calculation unit can use an insurance premium calculation standard that emphasizes stress factors. Furthermore, if the driver is relaxed, the calculation unit can use a normal insurance premium calculation standard. Furthermore, if the driver is tired, the calculation unit can use an insurance premium calculation standard that emphasizes fatigue factors. This allows for more appropriate insurance premium setting by adjusting the insurance premium calculation standard based on the driver's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input the driver's facial expression data into the generation AI, which can then estimate the driver's emotions and adjust the insurance premium calculation criteria based on the results.

[0105] The calculation unit can take into account the driver's past driving history when calculating the insurance premium. The calculation unit, for example, takes into account the driver's past driving history when calculating the insurance premium. For example, the calculation unit sets a high insurance premium for a driver who has had many accidents in the past. The calculation unit can also set a low insurance premium for a driver who has had many safe driving in the past. The calculation unit can also set a medium insurance premium for a driver with a medium driving history in the past. This makes it possible to set a more appropriate insurance premium by taking into account the driver's past driving history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the driver's past driving history data into AI, which analyzes the data and calculates the insurance premium.

[0106] The calculation unit can take the driving environment into consideration when calculating the insurance premium. The calculation unit, for example, takes the driving environment into consideration when calculating the insurance premium. For example, the calculation unit sets the insurance premium taking into consideration driving data in rainy weather. The calculation unit can also set the insurance premium taking into consideration driving data when traffic is heavy. The calculation unit can also set the insurance premium taking into consideration data when driving at night. This makes it possible to set a more appropriate insurance premium by taking the driving environment into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input driving environment data into AI, which analyzes the data and calculates the insurance premium.

[0107] The calculation unit can take the driver's health condition into consideration when calculating the insurance premium. The calculation unit, for example, takes the driver's health condition into consideration when calculating the insurance premium. For example, the calculation unit can monitor the driver's heart rate and respiratory rate, evaluate the driver's fatigue state, and set the insurance premium. The calculation unit can also monitor the driver's facial expression and tone of voice, evaluate the driver's stress state, and set the insurance premium. The calculation unit can also monitor the driver's driving behavior, comprehensively evaluate the driver's health condition, and set the insurance premium. This makes it possible to set a more appropriate insurance premium by taking the driver's health condition into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the driver's biometric data into AI, which can analyze the data and calculate the insurance premium.

[0108] The calculation unit can evaluate the driver's driving skills when calculating insurance premiums and suggest a training program to improve those skills. For example, the calculation unit can evaluate the driver's driving skills and suggest a training program to improve those skills when calculating insurance premiums. For example, the calculation unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The calculation unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The calculation unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lane. In this way, by evaluating the driver's driving skills and suggesting a training program to improve those skills, driving safety can be improved. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input the driver's driving data into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve those skills.

[0109] The calculation unit can propose an insurance plan according to the driver's driving style when calculating the insurance premium. For example, the calculation unit proposes an insurance plan according to the driver's driving style when calculating the insurance premium. For example, the calculation unit proposes an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The calculation unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The calculation unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the driver's driving data into AI, which analyzes the data and proposes an insurance plan according to the driving style.

[0110] The sharing unit can estimate the driver's emotions and adjust the timing of information sharing based on the estimated driver's emotions. The sharing unit, for example, estimates the driver's emotions and adjusts the timing of information sharing based on the estimated driver's emotions. For example, the sharing unit delays the timing of information sharing when the driver is stressed. The sharing unit can also advance the timing of information sharing when the driver is relaxed. The sharing unit can also adjust the timing of information sharing when the driver is tired. By adjusting the timing of information sharing based on the driver's emotions, information can be shared at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, 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 these examples. Some or all of the above-mentioned processing in the sharing unit may be performed using an AI, for example, or without an AI. For example, the sharing unit can input the driver's facial expression data into the generation AI, which can estimate the driver's emotions and adjust the timing of information sharing based on the result.

[0111] The sharing unit can take into account the driver's past driving history when sharing information. For example, the sharing unit takes into account the driver's past driving history when sharing information. For example, the sharing unit shares information about drivers who have had many accidents in the past with family members and the police. The sharing unit can also share information about drivers who have had many safe driving experiences in the past with family members and the police. The sharing unit can also share information about drivers with average driving histories with family members and the police. This enables more appropriate information sharing by taking into account the driver's past driving history. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the driver's past driving history data into AI, which analyzes the data and shares the information.

[0112] The sharing unit can take the driving environment into consideration when sharing information. The sharing unit, for example, takes the driving environment into consideration when sharing information. For example, the sharing unit shares information taking into consideration driving data in rainy weather. The sharing unit can also share information taking into consideration driving data when traffic is heavy. The sharing unit can also share information taking into consideration data when driving at night. This enables more appropriate information sharing by taking the driving environment into consideration. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input driving environment data into AI, which analyzes the data and shares the information.

[0113] The sharing unit can take the driver's health state into consideration when sharing information. The sharing unit, for example, takes the driver's health state into consideration when sharing information. For example, the sharing unit can monitor the driver's heart rate and respiratory rate, evaluate the driver's fatigue state, and share the information. The sharing unit can also monitor the driver's facial expressions and tone of voice, evaluate the driver's stress state, and share the information. The sharing unit can also monitor the driver's driving behavior, comprehensively evaluate the driver's health state, and share the information. This enables more appropriate information sharing by taking the driver's health state into consideration. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the driver's biometric data into AI, which can analyze the data and share the information.

[0114] The sharing unit can evaluate the driver's driving skills when sharing information and suggest a training program to improve those skills. For example, the sharing unit can evaluate the driver's driving skills when sharing information and suggest a training program to improve those skills. For example, the sharing unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The sharing unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The sharing unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lane. In this way, by evaluating the driver's driving skills and suggesting a training program to improve those skills, driving safety can be improved. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the driver's driving data into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve the skills.

[0115] The shared unit can propose an insurance plan according to the driver's driving style when sharing information. For example, the shared unit proposes an insurance plan according to the driver's driving style when sharing information. For example, the shared unit proposes an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The shared unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The shared unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the shared unit may be performed using, for example, AI, or may be performed without using AI. For example, the shared unit can input the driver's driving data into AI, which analyzes the data and proposes an insurance plan according to the driving style.

[0116] The notification unit can estimate the driver's emotion and adjust the timing of the notification based on the estimated driver's emotion. The notification unit, for example, estimates the driver's emotion and adjusts the timing of the notification based on the estimated driver's emotion. For example, the notification unit delays the timing of the notification when the driver is stressed. The notification unit can also advance the timing of the notification when the driver is relaxed. The notification unit can also adjust the timing of the notification when the driver is tired. By adjusting the timing of the notification based on the driver's emotion, the notification can be provided at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, 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 notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input the driver's facial expression data into the generation AI, which then estimates the emotion and adjusts the timing of the notification based on the result.

[0117] The notification unit can take into account the driver's past driving history when making a notification. The notification unit, for example, takes into account the driver's past driving history when making a notification. For example, the notification unit notifies family members and the police of information about drivers who have had many accidents in the past. The notification unit can also notify family members and the police of information about drivers who have had many safe driving experiences in the past. The notification unit can also notify family members and the police of information about drivers with average driving histories in the past. This enables more appropriate notification by taking into account the driver's past driving history. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the driver's past driving history data into AI, which analyzes the data and makes a notification.

[0118] The notification unit can take the driver's health state into consideration when issuing a notification. The notification unit can, for example, take the driver's health state into consideration when issuing a notification. For example, the notification unit can monitor the driver's heart rate and respiratory rate, evaluate the driver's fatigue state, and issue a notification. The notification unit can also monitor the driver's facial expression and tone of voice, evaluate the driver's stress state, and issue a notification. The notification unit can also monitor the driver's driving behavior, comprehensively evaluate the driver's health state, and issue a notification. This allows for more appropriate notification by taking the driver's health state into consideration. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the driver's biometric data into AI, which can analyze the data and issue a notification.

[0119] The notification unit can evaluate the driver's driving skills at the time of notification and suggest a training program to improve the skills. For example, the notification unit can evaluate the driver's driving skills at the time of notification and suggest a training program to improve the skills. For example, the notification unit can suggest a training program to improve braking usage for a driver who frequently brakes suddenly. The notification unit can also suggest a training program to improve acceleration for a driver who frequently accelerates suddenly. The notification unit can also suggest a training program to improve lane keeping for a driver who frequently deviates from their lane. In this way, by evaluating the driver's driving skills and suggesting a training program to improve the skills, driving safety can be improved. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the driver's driving data into AI, which analyzes the data to evaluate the driving skills and suggest a training program to improve the skills.

[0120] The notification unit can propose an insurance plan according to the driver's driving style at the time of notification. The notification unit, for example, proposes an insurance plan according to the driver's driving style at the time of notification. For example, the notification unit can propose an insurance plan according to a high-risk driving style to a driver who frequently brakes suddenly. The notification unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently accelerates suddenly. The notification unit can also propose an insurance plan according to a high-risk driving style to a driver who frequently deviates from their lane. In this way, by proposing an insurance plan according to the driver's driving style, driving safety can be improved. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the driver's driving data into AI, which can analyze the data and propose an insurance plan according to the driving style. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, scoring unit, calculation unit, sharing unit, and notification 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 uses the camera and sensors of the smart device 14 to record detailed vehicle behavior and driver actions while driving and transmits the recorded data to the data processing device 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and evaluates driving safety. The scoring unit performs scoring based on the evaluation results obtained by the analysis unit. The calculation unit calculates insurance premiums based on the score obtained by the scoring unit. The sharing unit shares information with family members or the police if the score falls below a certain standard. The notification unit notifies specific recipients of the information via the sharing unit. The collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated driver's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, scoring unit, calculation unit, sharing unit, and notification 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 uses the camera and sensors of the smart glasses 214 to record detailed vehicle behavior and driver actions while driving and transmits the records to the data processing device 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and evaluates driving safety. The scoring unit performs scoring based on the evaluation results obtained by the analysis unit. The calculation unit calculates insurance premiums based on the score obtained by the scoring unit. The sharing unit shares information with family members or the police if the score falls below a certain standard. The notification unit notifies specific recipients of the information via the sharing unit. The collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated driver's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, scoring unit, calculation unit, sharing unit, and notification unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit uses the camera and sensors of the headset-type terminal 314 to record detailed vehicle behavior and driver actions while driving and transmits the records to the data processing device 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and evaluates driving safety. The scoring unit performs scoring based on the evaluation results obtained by the analysis unit. The calculation unit calculates insurance premiums based on the score obtained by the scoring unit. The sharing unit shares information with family members or the police if the score falls below a certain standard. The notification unit notifies specific recipients of the information via the sharing unit. The collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated driver's emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, scoring unit, calculation unit, sharing unit, and notification 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 uses the robot 414's camera and sensors to record detailed vehicle behavior and driver actions while driving and transmits the data to the data processing device 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and evaluates driving safety. The scoring unit performs scoring based on the evaluation results obtained by the analysis unit. The calculation unit calculates insurance premiums based on the score obtained by the scoring unit. The sharing unit shares information with family members or the police if the score falls below a certain standard. The notification unit notifies specific recipients of the information via the sharing unit. The collection unit can estimate the driver's emotions and adjust the timing of data collection based on the estimated driver's emotions.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The analysis unit can adjust the music and navigation voices while driving based on the driver's driving style. For example, if the driver is feeling stressed, the analysis unit can play relaxing music. If the driver is relaxed, the analysis unit can also play regular music and navigation voices. If the driver is tired, the analysis unit can also play voices that call attention to the driver's actions. This makes it possible to improve driving safety by adjusting the music and navigation voices based on the driver's emotions.

[0123] The collection unit can monitor the driver's posture while driving and encourage them to improve their posture. For example, if the driver has been driving in the same posture for a long time, the collection unit can encourage them to change their posture. In addition, if the driver is driving in an unnatural posture, the collection unit can also advise the driver to adopt a correct posture. In addition, if the driver is tired, the collection unit can encourage the driver to take a break. In this way, driving safety can be improved by monitoring the driver's posture and maintaining appropriate posture.

[0124] The analysis unit can analyze the driver's eye movements while driving and evaluate the degree of eye concentration. For example, the analysis unit can compare the amount of time the driver spends looking ahead with the amount of time they spend looking to the side to evaluate the degree of eye concentration. The analysis unit can also evaluate the possibility of distraction if the driver frequently moves their eyes. The analysis unit can also evaluate the degree of eye concentration as high if the driver keeps their eyes fixed. In this way, by analyzing the driver's eye movements and evaluating the degree of eye concentration, driving safety can be improved.

[0125] The scoring unit can monitor the driver's heart rate while driving and perform scoring based on heart rate fluctuations. For example, the scoring unit evaluates heart rate fluctuations during sudden braking or sudden acceleration and scores driving safety. The scoring unit can also evaluate heart rate fluctuations during long-term driving and score the degree of fatigue. The scoring unit can also evaluate heart rate stability and score the degree of relaxation. In this way, scoring based on the driver's heart rate fluctuations can improve driving safety.

[0126] The calculation unit can calculate insurance premiums by taking into account the driver's fuel efficiency data while driving. For example, the calculation unit can set lower insurance premiums for drivers with good fuel efficiency. The calculation unit can also set higher insurance premiums for drivers with poor fuel efficiency. The calculation unit can also provide advice on eco-driving based on the fuel efficiency data. In this way, calculating insurance premiums by taking into account the driver's fuel efficiency data can encourage eco-driving and contribute to environmental protection.

[0127] The sharing unit can estimate the driver's emotions while driving and share information with family members or the police based on the estimated emotions. For example, if the driver is feeling stressed, the sharing unit can notify family members and ask for support. If the driver is relaxed, the sharing unit can also share information normally. If the driver is tired, the sharing unit can also notify the police and encourage them to take a break. This makes it possible to improve driving safety by sharing information based on the driver's emotions.

[0128] The notification unit can monitor the driver's behavior while driving in real time and issue a notification if abnormal behavior is detected. For example, if the driver frequently brakes suddenly, the notification unit can notify family members. The notification unit can also notify the police if the driver repeatedly deviates from their lane. The notification unit can also notify the driver to take a break if the driver continues driving for a long period of time. In this way, by detecting abnormal driver behavior in real time and issuing appropriate notifications, driving safety can be improved.

[0129] The collection unit can estimate the driver's emotions while driving and provide driving advice based on the estimated emotions. For example, if the driver feels stressed, the collection unit can provide advice to relax. If the driver is relaxed, the collection unit can also provide normal driving advice. If the driver is tired, the collection unit can also advise the driver to take a break. In this way, by providing driving advice based on the driver's emotions, driving safety can be improved.

[0130] The analysis unit can analyze the driver's behavior while driving and identify risk factors for driving. For example, the analysis unit can identify a high frequency of sudden braking as a risk factor. The analysis unit can also identify a high number of lane departures as a risk factor. The analysis unit can also identify a high frequency of speeding as a risk factor. In this way, by analyzing the driver's behavior while driving and identifying risk factors, it is possible to improve driving safety.

[0131] The scoring unit can estimate the driver's emotions while driving and adjust the method for displaying the scoring results based on the estimated emotions. For example, if the driver is feeling stressed, the scoring unit can provide a simple, highly visible display method. If the driver is relaxed, the scoring unit can also provide a display method that includes detailed information. If the driver is tired, the scoring unit can also provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the method for displaying the scoring results based on the driver's emotions.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection unit collects data from cameras and sensors. For example, it records the vehicle's behavior and the driver's actions in detail while driving, collecting data such as sudden braking, sudden acceleration, lane departure, and the driver's gaze and hand movements. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates driving safety. For example, it analyzes the number of sudden braking, frequency of sudden acceleration, and number of lane departures to quantify driving safety. Step 3: The scoring unit performs scoring based on the evaluation results obtained by the analysis unit, for example, based on the number of sudden braking incidents, the frequency of sudden acceleration, and the number of lane departures. Step 4: The calculation unit calculates the insurance premium based on the score obtained by the scoring unit. For example, if the score is high, the insurance premium is set low, and if the score is low, the insurance premium is set high. Step 5: The sharing department shares information if the score falls below a certain standard. For example, if the score is low, the information is provided to family members or the police, and used as reference information when proposing license surrender or license renewal. Step 6: The notification unit notifies the specified recipients of the information via the sharing unit. For example, the notification unit notifies family members and suggests that the driver surrender his / her license. The information is also provided to the police, who use it as reference information when renewing the driver's license.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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 data from cameras and sensors; an analysis unit that analyzes the data collected by the collection unit and evaluates driving safety; a scoring unit that performs scoring based on the evaluation results obtained by the analysis unit; a calculation unit that calculates insurance premiums based on the scores obtained by the scoring unit; a sharing unit that shares information when the score falls below a certain standard; a notification unit that notifies a specific recipient of information by the sharing unit; A system characterized by:

2. The collecting unit Record specific vehicle behavior and driver actions while driving 2. The system of claim 1.

3. The analysis unit Analyzing data on sudden braking, sudden acceleration, lane departure, etc. to quantify driving safety 2. The system of claim 1.

4. The scoring unit Evaluate and score driving safety 2. The system of claim 1.

5. The calculation unit Set insurance premiums based on the scoring results 2. The system of claim 1.

6. The common part is Share information with specific recipients if the score falls below a certain threshold 2. The system of claim 1.

7. The notification unit Notify the family and propose surrendering the license 2. The system of claim 1.

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

2. The system of claim 1.

Images