system

Abstract

The system according to this embodiment aims to analyze driving data to provide tips for safe driving and to achieve insurance premium discounts. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a discount provision unit. The collection unit collects driving data. The analysis unit analyzes the driving data collected by the collection unit. The provision unit provides tips for safe driving based on the analysis results obtained by the analysis unit. The discount provision unit provides insurance premium discount data based on the tips provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system that utilizes driving data to promote safe driving and provides a discount on insurance premiums is not fully developed, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze driving data to provide hints for safe driving and realize a discount on insurance premiums.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a discount provision unit. The collection unit collects driving data. The analysis unit analyzes the driving data collected by the collection unit. The provision unit provides tips for safe driving based on the analysis results obtained by the analysis unit. The discount provision unit provides insurance premium discount data based on the tips provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze driving data to provide tips for safe driving and enable insurance premium discounts. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 3, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The drive assistant system according to an embodiment of the present invention is a system that analyzes driving data to provide hints for safe driving and supports improvement of driving skills and fuel efficiency. This drive assistant system collects driving data, and a generating AI analyzes that data to provide hints for safe driving. This allows the driver to improve their driving skills. The generating AI also provides advice on improving fuel efficiency. Furthermore, the generating AI can also provide data that can lead to discounts on insurance premiums. For example, the drive assistant system collects detailed data such as vehicle speed, acceleration, frequency of brake use, and fuel efficiency. For example, if the driver frequently uses sudden braking, that data is collected. Next, the generating AI analyzes the collected driving data. The generating AI analyzes the driving pattern and provides hints for safe driving. For example, if sudden braking is used frequently, the generating AI advises the driver to avoid sudden braking. The generating AI also provides advice on improving fuel efficiency. For example, by recommending driving at a constant speed, fuel efficiency can be improved. Furthermore, the generating AI can also provide data that can lead to discounts on insurance premiums. The generating AI analyzes the driver's driving data and evaluates the driver's safe driving performance. Based on this evaluation, it can propose discounts on insurance premiums to insurance companies. For example, drivers who rarely use sudden braking and tend to drive at a constant speed are evaluated as having a high record of safe driving and can receive insurance premium discounts. This system allows drivers to improve their driving skills and fuel efficiency. They also enjoy economic benefits, such as discounts on insurance premiums based on their safe driving record. For example, it is expected to reduce the annual accident rate by 20% and improve average fuel efficiency by 15%. The drive assistance system is suitable for drivers of all age groups and is particularly beneficial for drivers who frequently drive long distances. It also leads to increased customer satisfaction and the creation of new business opportunities for auto insurance companies, vehicle maintenance companies, and auto dealerships.This allows the drive assistance system to collect and analyze driving data, provide tips for safe driving, and offer data on insurance premium discounts.

[0029] The drive assistant system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a discount provision unit. The data collection unit collects driving data. The data collection unit collects data such as vehicle speed, acceleration, brake usage frequency, and fuel efficiency. The data collection unit measures vehicle speed using GPS data or a speed sensor, for example. The data collection unit measures acceleration using an acceleration sensor, for example. The data collection unit measures brake usage frequency using the number of brake pedal operations or in units of time, for example. The data collection unit measures fuel efficiency using fuel consumption or fuel usage per distance traveled, for example. The analysis unit analyzes the driving data collected by the data collection unit. The analysis unit analyzes the driving data using data analysis methods and algorithms, for example. The analysis unit uses driving behavior classification and pattern recognition algorithms to identify driving patterns, for example. The data provision unit provides hints for safe driving based on the analysis results obtained by the analysis unit. The data provision unit provides suggestions for improving driving behavior and specific advice, for example. The service provider, for example, advises avoiding sudden braking if the driver frequently uses sudden braking. The service provider also provides advice to improve fuel efficiency by recommending driving at a constant speed. The discount service provider provides insurance premium discount data based on the tips provided by the service provider. The discount service provider also provides insurance premium discount data based on safe driving performance. The discount service provider also proposes insurance premium discounts to drivers who frequently use sudden braking and drive at a constant speed. As a result, the drive assistant system according to the embodiment can collect and analyze driving data, provide tips for safe driving, and provide insurance premium discount data.

[0030] The data collection unit collects driving data. For example, it collects data such as vehicle speed, acceleration, brake usage frequency, and fuel efficiency. Specifically, vehicle speed is measured using GPS data and speed sensors. GPS data identifies the vehicle's position by receiving signals from satellites, and speed is calculated based on this position information. Speed ​​sensors measure the rotation speed of the vehicle's wheels and calculate speed from that rotation speed. Acceleration is measured using acceleration sensors. Acceleration sensors detect the vehicle's forward, backward, left, and right movements, and calculate acceleration based on this data. Brake usage frequency is measured by the number of brake pedal operations and time. A sensor attached to the brake pedal counts the number of operations and collects this data. Fuel efficiency is measured by fuel consumption and fuel consumption per mile. Fuel consumption is calculated based on data from a sensor attached to the fuel tank that measures the amount of fuel used. Mileage is obtained from the vehicle's odometer, and fuel efficiency is calculated based on this data. This allows the data collection unit to collect a variety of data to understand the vehicle's driving conditions in detail. Furthermore, the data collection unit can transmit this data to a central database in real time and integrate with other systems and departments. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the driving data collected by the data collection department. For example, the analysis department analyzes the driving data using data analysis methods and algorithms. Specifically, it uses driving behavior classification and pattern recognition algorithms to identify driving patterns. For instance, it uses machine learning algorithms to cluster driving data and identify different driving styles. This allows for the extraction of features such as the frequency of sudden braking and acceleration, and the duration of driving at a constant speed. Furthermore, the analysis department identifies changes and areas for improvement in driving behavior by comparing it with past driving data. For example, if the frequency of sudden braking decreases compared to past data, it can be determined that safer driving has improved. The analysis department can also use anomaly detection algorithms to detect unusual driving patterns and abnormal data. This allows for the early detection of driving risks and the implementation of appropriate countermeasures. Additionally, the analysis department can build predictive models based on driving data to predict future driving behavior and risks. For example, if a particular driving pattern persists, it can be predicted that the risk of accidents may increase in the future. This allows the analysis department to quickly and accurately analyze the collected data and provide information useful for improving driving behavior and risk management.

[0032] The service department provides tips for safe driving based on the analysis results obtained by the analysis department. Specifically, it provides suggestions for improving driving behavior and offers specific advice. For example, if a driver frequently uses sudden braking, it will advise avoiding sudden braking. While sudden braking may be necessary to shorten the vehicle's braking distance, frequent use accelerates tire and brake pad wear and worsens fuel efficiency. The service department will provide specific advice on driving techniques and precautions to avoid sudden braking. For example, it will recommend maintaining an appropriate distance from the vehicle in front and slowing down early. It will also provide advice to improve fuel efficiency by recommending driving at a constant speed. Driving at a constant speed helps to keep the engine load even and reduces fuel consumption. The service department will provide advice tailored to specific speed ranges and driving conditions. For example, it will recommend cruising at a constant speed on highways and smooth acceleration and deceleration according to traffic signals and intersection conditions in urban areas. Furthermore, the service department will continuously provide information that helps improve driving behavior and raise driver awareness. For example, it will boost driver motivation by regularly providing feedback on driving data and reporting on areas for improvement and progress. This allows the service provider to offer drivers specific and practical advice, supporting the promotion of safe driving and improvement of fuel efficiency.

[0033] The discount department provides insurance premium discount data based on hints provided by the department. Specifically, it provides insurance premium discount data based on safe driving performance. For example, it proposes an insurance premium discount to drivers who frequently use sudden braking and drive at a constant speed. The discount department evaluates the driver's driving data and scores their safe driving performance. For scoring, evaluation criteria are set for each element of driving behavior (sudden braking, sudden acceleration, speeding, etc.), and points are assigned to each element. Drivers with high overall scores are eligible for insurance premium discounts. Furthermore, the discount department provides drivers with detailed score information and areas for improvement to promote even safer driving. For example, if the frequency of sudden braking decreases, the department reports the results specifically and presents the amount of the insurance premium discount. The discount department also collaborates with insurance companies and shares discount data to realize insurance premium discounts. This allows drivers to receive insurance premium discounts commensurate with their safe driving performance. In addition, the discount department continuously supports drivers in improving their driving behavior to promote safe driving in the long term. For example, driving data could be evaluated regularly, and scores could be updated or discount amounts revised. This would allow the discount provider to offer drivers an incentive for safe driving and help reduce their insurance premium burden.

[0034] The data collection unit can collect data such as vehicle speed, acceleration, brake usage frequency, and fuel consumption. For example, the data collection unit measures vehicle speed using GPS data or a speed sensor. For example, the data collection unit measures acceleration using an acceleration sensor. For example, the data collection unit measures brake usage frequency by the number of brake pedal operations or by time. For example, the data collection unit measures fuel consumption or fuel usage per mile. By collecting detailed driving data, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input vehicle speed data into a generating AI and have the generating AI perform the analysis of the speed data.

[0035] The analysis unit can analyze the collected driving data and identify driving patterns. The analysis unit analyzes the driving data using, for example, data analysis methods and algorithms. For example, the analysis unit uses driving behavior classification and pattern recognition algorithms to identify driving patterns. By identifying driving patterns, appropriate advice can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected driving data into a generating AI and have the generating AI perform the identification of driving patterns.

[0036] The service provider can provide safe driving tips based on identified driving patterns. For example, the service provider can provide suggestions for improving driving behavior and specific advice. For example, if the driver frequently uses sudden braking, the service provider can advise avoiding sudden braking. For example, the service provider can provide advice to improve fuel efficiency by recommending driving at a constant speed. In this way, safe driving is promoted by providing tips based on driving patterns. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input identified driving patterns into a generating AI and have the generating AI generate safe driving tips.

[0037] The service provider can provide advice on improving fuel efficiency. For example, the service provider can provide advice on improving fuel efficiency by recommending driving at a constant speed. For example, the service provider can provide advice on eco-driving methods and specific driving techniques. In this way, by providing advice on improving fuel efficiency, it improves fuel efficiency. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on improving fuel efficiency into a generating AI and have the generating AI perform the generation of advice.

[0038] The discount provision unit can provide insurance premium discount data based on safe driving performance. For example, the discount provision unit may offer insurance premium discounts to drivers who frequently use sudden braking and drive at a constant speed. The discount provision unit may also provide insurance premium discount data based on accident avoidance rates and driving behavior scoring. This allows for economic benefits by providing insurance premium discount data based on safe driving performance. Some or all of the above processing in the discount provision unit may be performed using AI, for example, or without AI. For example, the discount provision unit can input safe driving performance data into a generating AI and have the generating AI generate insurance premium discount data.

[0039] The data collection unit can analyze the driver's past driving history and select the optimal data collection method. For example, if the driver has frequently used sudden braking in the past, the data collection unit will focus on collecting data related to sudden braking. For example, if the driver has preferred driving at a constant speed in the past, the data collection unit will prioritize collecting data related to driving at a constant speed. For example, if the driver has driven long distances in the past, the data collection unit will collect detailed data related to long-distance driving. This enables efficient data collection by selecting the optimal data collection method based on past driving history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's past driving history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter driving data based on the driver's current driving situation and areas of interest. For example, if the driver is driving on a highway, the data collection unit will prioritize collecting highway driving data. For example, if the driver is driving in an urban area, the data collection unit will focus on collecting urban driving data. For example, if the driver is interested in fuel efficiency, the data collection unit will prioritize collecting fuel efficiency-related data. This allows for efficient collection of necessary data by filtering the data based on the current driving situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's current driving situation data into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the driver's geographical location information when collecting driving data. For example, if the driver is driving in a mountainous area, the data collection unit will prioritize the collection of driving data in the mountainous area. For example, if the driver is driving in an urban area, the data collection unit will prioritize the collection of driving data in the urban area. For example, if the driver is driving in a suburban area, the data collection unit will prioritize the collection of driving data in the suburban area. By prioritizing the collection of highly relevant data based on geographical location information, more useful data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's geographical location information data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0042] The data collection unit can analyze the driver's social media activity and collect relevant data when collecting driving data. For example, if the driver posts about driving on social media, the data collection unit can collect driving data based on the content of those posts. For example, if the driver shows interest in a particular driving technique on social media, the data collection unit can collect data related to that technique. For example, if the driver shares information about fuel efficiency on social media, the data collection unit can collect driving data based on that information. This makes it possible to collect data that is tailored to the driver's interests by collecting relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the driving data during the analysis. For example, the analysis unit performs a detailed analysis on important driving data. For example, the analysis unit performs a simplified analysis on driving data of low importance. For example, the analysis unit applies multiple analysis methods to driving data of high importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the driving data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input driving data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of driving data during analysis. For example, the analysis unit applies a speed fluctuation analysis algorithm to speed data. For example, the analysis unit applies a fuel efficiency analysis algorithm to fuel consumption data. For example, the analysis unit applies a brake pattern analysis algorithm to brake usage frequency data. By applying different analysis algorithms depending on the category of driving data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input driving data category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may analyze current driving data while referring to past driving data. For example, the analysis unit may focus on analyzing driving data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the timing of data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the timing of data collection into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the driving data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant driving data. For example, the analysis unit may postpone the analysis of less relevant driving data. For example, the analysis unit may group highly relevant data and analyze them all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the driving data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data of the driving data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The service provider can adjust the level of detail of the hints based on the importance of the driving data when providing them. For example, hints based on important driving data may include detailed explanations. Hints based on less important driving data may include brief explanations. Hints based on highly important driving data may include advice from multiple perspectives. This allows for more efficient advice by adjusting the level of detail of the hints based on the importance of the driving data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input driving data importance data into a generating AI and have the generating AI adjust the level of detail of the hints.

[0048] The service provider can apply different hint-providing algorithms depending on the category of driving data at the time of provision. For example, hints based on speed data provide advice on suppressing speed fluctuations. For example, hints based on fuel efficiency data provide advice on improving fuel efficiency. For example, hints based on brake usage frequency data provide advice on how to use brakes appropriately. By applying different hint-providing algorithms depending on the category of driving data, more accurate advice becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input driving data category data into a generating AI and have the generating AI execute the application of different hint-providing algorithms.

[0049] The service provider can determine the priority of hints based on the timing of driving data collection at the time of provision. For example, the service provider may prioritize hints based on recently collected driving data. For example, the service provider may provide hints based on current driving data while referring to past driving data. For example, the service provider may focus on providing hints based on driving data collected during a specific period. This enables efficient advice by determining the priority of hints based on the timing of driving data collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input driving data collection timing data into a generating AI and have the generating AI perform the determination of hint priority.

[0050] The service provider can adjust the order of hints based on the relevance of the driving data at the time of provision. For example, the service provider may prioritize providing hints based on highly relevant driving data. For example, the service provider may postpone providing hints based on less relevant driving data. For example, the service provider may group highly relevant data and provide hints in a batch. This allows for efficient advice by adjusting the order of hints based on the relevance of the driving data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the relevance data of the driving data into a generating AI and have the generating AI perform the adjustment of the hint order.

[0051] The discount provider can adjust the level of detail in the discount data based on the importance of the driving data when providing discount data. For example, the discount provider may include detailed explanations for discount data based on important driving data, a brief explanation for discount data based on less important driving data, and evaluations from multiple perspectives for discount data based on highly important driving data. This allows for efficient information provision by adjusting the level of detail in the discount data based on the importance of the driving data. Some or all of the above processing in the discount provider may be performed using AI, for example, or without AI. For example, the discount provider can input driving data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the discount data.

[0052] The discount provision unit can apply different discount data provision algorithms depending on the category of driving data when providing discount data. For example, discount data based on speed data may include an evaluation of the suppression of speed fluctuations. For example, discount data based on fuel efficiency data may include an evaluation of the improvement of fuel efficiency. For example, discount data based on brake usage frequency data may include an evaluation of the appropriate use of brakes. By applying different discount data provision algorithms depending on the category of driving data, more accurate information can be provided. Some or all of the above processing in the discount provision unit may be performed using AI, for example, or without AI. For example, the discount provision unit can input driving data category data into a generating AI and have the generating AI execute the application of different discount data provision algorithms.

[0053] The discount provision unit can determine the priority of discount data based on the timing of the driving data collection when providing discount data. For example, the discount provision unit may prioritize providing discount data based on recently collected driving data. For example, the discount provision unit may provide discount data based on current driving data while referring to past driving data. For example, the discount provision unit may focus on providing discount data based on driving data collected during a specific period. This enables efficient information provision by determining the priority of discount data based on the timing of the driving data collection. Some or all of the above processing in the discount provision unit may be performed using AI, for example, or without AI. For example, the discount provision unit may input driving data collection timing data into a generating AI and have the generating AI perform the determination of the priority of discount data.

[0054] The discount provision unit can adjust the order of discount data based on the relevance of the driving data when providing discount data. For example, the discount provision unit may prioritize providing discount data based on highly relevant driving data. For example, the discount provision unit may postpone providing discount data based on less relevant driving data. For example, the discount provision unit may group highly relevant data and provide the discount data in a batch. This allows for efficient information provision by adjusting the order of discount data based on the relevance of the driving data. Some or all of the above processing in the discount provision unit may be performed using AI, for example, or without AI. For example, the discount provision unit may input the relevance data of the driving data into a generating AI and have the generating AI perform the adjustment of the order of the discount data.

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

[0056] The drive assistance system can also be equipped with a real-time traffic information unit. This unit collects and provides drivers with information such as traffic congestion and accident data. This allows drivers to select the optimal route and improve driving efficiency. For example, if traffic congestion occurs, the unit can suggest alternative routes to the driver. It can also quickly provide information in the event of an accident, supporting the driver in driving safely. Furthermore, the unit can provide weather information and offer advice on driving precautions during adverse weather conditions.

[0057] The drive assistance system can also include a health management unit that monitors the driver's health. This unit can, for example, measure the driver's heart rate and blood pressure and issue warnings if abnormalities are detected. This allows for real-time monitoring of the driver's health and supports safe driving. For instance, the health management unit can issue an alert prompting the driver to take a break if their heart rate suddenly increases. It can also recommend contacting a medical institution if blood pressure is abnormally high. Furthermore, the health management unit can measure the driver's stress level and provide advice for relaxation.

[0058] The drive assistance system can also be equipped with customization features tailored to the driver's driving style. These customization features can automatically adjust system settings based on the driver's driving habits and preferences, allowing the driver to receive optimal driving support. For example, if a driver frequently uses highways, the customization features can focus on providing highway driving advice. Similarly, if a driver prioritizes eco-driving, the system can prioritize advice on improving fuel efficiency. Furthermore, the customization features can adjust the system interface to suit the driver's driving style, improving ease of use.

[0059] The drive assistance system can also include a training mode to support the driver's improvement of their driving skills. The training mode can, for example, evaluate the driver's driving technique and point out areas for improvement, thereby helping the driver acquire safer driving techniques and reduce the risk of accidents. For instance, the training mode can analyze the frequency of sudden braking and acceleration and provide specific advice for improvement. It can also provide feedback on curve speed and maintaining a safe following distance. Furthermore, the training mode can record the driver's progress and visualize the improvement in their driving skills.

[0060] The drive assistance system can also provide personalized reports based on the driver's driving history. These personalized reports can, for example, analyze the driver's driving data and evaluate monthly and weekly driving performance. This allows the driver to understand their driving tendencies and identify areas for improvement. For instance, personalized reports can display graphs showing the number of sudden braking incidents and fluctuations in fuel efficiency. They can also provide statistics on driving time and distance, allowing drivers to understand the frequency of long-distance driving. Furthermore, personalized reports can include advice on improving the driver's driving skills.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects driving data. The data collection unit collects data such as vehicle speed, acceleration, brake usage frequency, and fuel consumption. For example, the data collection unit measures vehicle speed using GPS data or a speed sensor. For example, the data collection unit measures acceleration using an acceleration sensor. For example, the data collection unit measures brake usage frequency by the number of brake pedal operations or by the amount of time. For example, the data collection unit measures fuel consumption or fuel usage per unit of distance traveled. Step 2: The analysis unit analyzes the driving data collected by the collection unit. The analysis unit analyzes the driving data using, for example, data analysis methods and algorithms. For example, the analysis unit uses driving behavior classification and pattern recognition algorithms to identify driving patterns. Step 3: The service provider provides safe driving tips based on the analysis results obtained by the analysis provider. For example, the service provider provides suggestions for improving driving behavior and specific advice. For example, if the driver frequently uses sudden braking, the service provider will advise avoiding sudden braking. For example, the service provider will provide advice to improve fuel efficiency by recommending driving at a constant speed. Step 4: The discount provider provides premium discount data based on the hints provided by the provider. For example, the discount provider provides premium discount data based on safe driving performance. For example, the discount provider proposes a premium discount to drivers who frequently use sudden braking and drive at a constant speed.

[0063] (Example of form 2) The drive assistant system according to an embodiment of the present invention is a system that analyzes driving data to provide hints for safe driving and supports improvement of driving skills and fuel efficiency. This drive assistant system collects driving data, and a generating AI analyzes that data to provide hints for safe driving. This allows the driver to improve their driving skills. The generating AI also provides advice on improving fuel efficiency. Furthermore, the generating AI can also provide data that can lead to discounts on insurance premiums. For example, the drive assistant system collects detailed data such as vehicle speed, acceleration, frequency of brake use, and fuel efficiency. For example, if the driver frequently uses sudden braking, that data is collected. Next, the generating AI analyzes the collected driving data. The generating AI analyzes the driving pattern and provides hints for safe driving. For example, if sudden braking is used frequently, the generating AI advises the driver to avoid sudden braking. The generating AI also provides advice on improving fuel efficiency. For example, by recommending driving at a constant speed, fuel efficiency can be improved. Furthermore, the generating AI can also provide data that can lead to discounts on insurance premiums. The generating AI analyzes the driver's driving data and evaluates the driver's safe driving performance. Based on this evaluation, it can propose discounts on insurance premiums to insurance companies. For example, drivers who rarely use sudden braking and tend to drive at a constant speed are evaluated as having a high record of safe driving and can receive insurance premium discounts. This system allows drivers to improve their driving skills and fuel efficiency. They also enjoy economic benefits, such as discounts on insurance premiums based on their safe driving record. For example, it is expected to reduce the annual accident rate by 20% and improve average fuel efficiency by 15%. The drive assistance system is suitable for drivers of all age groups and is particularly beneficial for drivers who frequently drive long distances. It also leads to increased customer satisfaction and the creation of new business opportunities for auto insurance companies, vehicle maintenance companies, and auto dealerships.This allows the drive assistance system to collect and analyze driving data, provide tips for safe driving, and offer data on insurance premium discounts.

[0064] The drive assistant system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a discount provision unit. The data collection unit collects driving data. The data collection unit collects data such as vehicle speed, acceleration, brake usage frequency, and fuel efficiency. The data collection unit measures vehicle speed using GPS data or a speed sensor, for example. The data collection unit measures acceleration using an acceleration sensor, for example. The data collection unit measures brake usage frequency using the number of brake pedal operations or in units of time, for example. The data collection unit measures fuel efficiency using fuel consumption or fuel usage per distance traveled, for example. The analysis unit analyzes the driving data collected by the data collection unit. The analysis unit analyzes the driving data using data analysis methods and algorithms, for example. The analysis unit uses driving behavior classification and pattern recognition algorithms to identify driving patterns, for example. The data provision unit provides hints for safe driving based on the analysis results obtained by the analysis unit. The data provision unit provides suggestions for improving driving behavior and specific advice, for example. The service provider, for example, advises avoiding sudden braking if the driver frequently uses sudden braking. The service provider also provides advice to improve fuel efficiency by recommending driving at a constant speed. The discount service provider provides insurance premium discount data based on the tips provided by the service provider. The discount service provider also provides insurance premium discount data based on safe driving performance. The discount service provider also proposes insurance premium discounts to drivers who frequently use sudden braking and drive at a constant speed. As a result, the drive assistant system according to the embodiment can collect and analyze driving data, provide tips for safe driving, and provide insurance premium discount data.

[0065] The data collection unit collects driving data. For example, it collects data such as vehicle speed, acceleration, brake usage frequency, and fuel efficiency. Specifically, vehicle speed is measured using GPS data and speed sensors. GPS data identifies the vehicle's position by receiving signals from satellites, and speed is calculated based on this position information. Speed ​​sensors measure the rotation speed of the vehicle's wheels and calculate speed from that rotation speed. Acceleration is measured using acceleration sensors. Acceleration sensors detect the vehicle's forward, backward, left, and right movements, and calculate acceleration based on this data. Brake usage frequency is measured by the number of brake pedal operations and time. A sensor attached to the brake pedal counts the number of operations and collects this data. Fuel efficiency is measured by fuel consumption and fuel consumption per mile. Fuel consumption is calculated based on data from a sensor attached to the fuel tank that measures the amount of fuel used. Mileage is obtained from the vehicle's odometer, and fuel efficiency is calculated based on this data. This allows the data collection unit to collect a variety of data to understand the vehicle's driving conditions in detail. Furthermore, the data collection unit can transmit this data to a central database in real time and integrate with other systems and departments. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis department analyzes the driving data collected by the data collection department. For example, the analysis department analyzes the driving data using data analysis methods and algorithms. Specifically, it uses driving behavior classification and pattern recognition algorithms to identify driving patterns. For instance, it uses machine learning algorithms to cluster driving data and identify different driving styles. This allows for the extraction of features such as the frequency of sudden braking and acceleration, and the duration of driving at a constant speed. Furthermore, the analysis department identifies changes and areas for improvement in driving behavior by comparing it with past driving data. For example, if the frequency of sudden braking decreases compared to past data, it can be determined that safer driving has improved. The analysis department can also use anomaly detection algorithms to detect unusual driving patterns and abnormal data. This allows for the early detection of driving risks and the implementation of appropriate countermeasures. Additionally, the analysis department can build predictive models based on driving data to predict future driving behavior and risks. For example, if a particular driving pattern persists, it can be predicted that the risk of accidents may increase in the future. This allows the analysis department to quickly and accurately analyze the collected data and provide information useful for improving driving behavior and risk management.

[0067] The service department provides tips for safe driving based on the analysis results obtained by the analysis department. Specifically, it provides suggestions for improving driving behavior and offers specific advice. For example, if a driver frequently uses sudden braking, it will advise avoiding sudden braking. While sudden braking may be necessary to shorten the vehicle's braking distance, frequent use accelerates tire and brake pad wear and worsens fuel efficiency. The service department will provide specific advice on driving techniques and precautions to avoid sudden braking. For example, it will recommend maintaining an appropriate distance from the vehicle in front and slowing down early. It will also provide advice to improve fuel efficiency by recommending driving at a constant speed. Driving at a constant speed helps to keep the engine load even and reduces fuel consumption. The service department will provide advice tailored to specific speed ranges and driving conditions. For example, it will recommend cruising at a constant speed on highways and smooth acceleration and deceleration according to traffic signals and intersection conditions in urban areas. Furthermore, the service department will continuously provide information that helps improve driving behavior and raise driver awareness. For example, it will boost driver motivation by regularly providing feedback on driving data and reporting on areas for improvement and progress. This allows the service provider to offer drivers specific and practical advice, supporting the promotion of safe driving and improvement of fuel efficiency.

[0068] The discount department provides insurance premium discount data based on hints provided by the department. Specifically, it provides insurance premium discount data based on safe driving performance. For example, it proposes an insurance premium discount to drivers who frequently use sudden braking and drive at a constant speed. The discount department evaluates the driver's driving data and scores their safe driving performance. For scoring, evaluation criteria are set for each element of driving behavior (sudden braking, sudden acceleration, speeding, etc.), and points are assigned to each element. Drivers with high overall scores are eligible for insurance premium discounts. Furthermore, the discount department provides drivers with detailed score information and areas for improvement to promote even safer driving. For example, if the frequency of sudden braking decreases, the department reports the results specifically and presents the amount of the insurance premium discount. The discount department also collaborates with insurance companies and shares discount data to realize insurance premium discounts. This allows drivers to receive insurance premium discounts commensurate with their safe driving performance. In addition, the discount department continuously supports drivers in improving their driving behavior to promote safe driving in the long term. For example, driving data could be evaluated regularly, and scores could be updated or discount amounts revised. This would allow the discount provider to offer drivers an incentive for safe driving and help reduce their insurance premium burden.

[0069] The data collection unit can collect data such as vehicle speed, acceleration, brake usage frequency, and fuel consumption. For example, the data collection unit measures vehicle speed using GPS data or a speed sensor. For example, the data collection unit measures acceleration using an acceleration sensor. For example, the data collection unit measures brake usage frequency by the number of brake pedal operations or by time. For example, the data collection unit measures fuel consumption or fuel usage per mile. By collecting detailed driving data, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input vehicle speed data into a generating AI and have the generating AI perform the analysis of the speed data.

[0070] The analysis unit can analyze the collected driving data and identify driving patterns. The analysis unit analyzes the driving data using, for example, data analysis methods and algorithms. For example, the analysis unit uses driving behavior classification and pattern recognition algorithms to identify driving patterns. By identifying driving patterns, appropriate advice can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected driving data into a generating AI and have the generating AI perform the identification of driving patterns.

[0071] The service provider can provide safe driving tips based on identified driving patterns. For example, the service provider can provide suggestions for improving driving behavior and specific advice. For example, if the driver frequently uses sudden braking, the service provider can advise avoiding sudden braking. For example, the service provider can provide advice to improve fuel efficiency by recommending driving at a constant speed. In this way, safe driving is promoted by providing tips based on driving patterns. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input identified driving patterns into a generating AI and have the generating AI generate safe driving tips.

[0072] The service provider can provide advice on improving fuel efficiency. For example, the service provider can provide advice on improving fuel efficiency by recommending driving at a constant speed. For example, the service provider can provide advice on eco-driving methods and specific driving techniques. In this way, by providing advice on improving fuel efficiency, it improves fuel efficiency. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on improving fuel efficiency into a generating AI and have the generating AI perform the generation of advice.

[0073] The discount provision unit can provide insurance premium discount data based on safe driving performance. For example, the discount provision unit may offer insurance premium discounts to drivers who frequently use sudden braking and drive at a constant speed. The discount provision unit may also provide insurance premium discount data based on accident avoidance rates and driving behavior scoring. This allows for economic benefits by providing insurance premium discount data based on safe driving performance. Some or all of the above processing in the discount provision unit may be performed using AI, for example, or without AI. For example, the discount provision unit can input safe driving performance data into a generating AI and have the generating AI generate insurance premium discount data.

[0074] The data collection unit can estimate the driver's emotions and adjust the timing of driving data collection based on the estimated emotions. For example, if the driver is stressed, the data collection unit increases the frequency of driving data collection and collects more detailed data. For example, if the driver is relaxed, the data collection unit decreases the frequency of driving data collection and collects only the minimum necessary data. For example, if the driver is tired, the data collection unit adjusts the timing of driving data collection and also collects data during breaks. This allows for more appropriate data collection by adjusting the collection timing according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the driver's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0075] The data collection unit can analyze the driver's past driving history and select the optimal data collection method. For example, if the driver has frequently used sudden braking in the past, the data collection unit will focus on collecting data related to sudden braking. For example, if the driver has preferred driving at a constant speed in the past, the data collection unit will prioritize collecting data related to driving at a constant speed. For example, if the driver has driven long distances in the past, the data collection unit will collect detailed data related to long-distance driving. This enables efficient data collection by selecting the optimal data collection method based on past driving history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's past driving history data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter driving data based on the driver's current driving situation and areas of interest. For example, if the driver is driving on a highway, the data collection unit will prioritize collecting highway driving data. For example, if the driver is driving in an urban area, the data collection unit will focus on collecting urban driving data. For example, if the driver is interested in fuel efficiency, the data collection unit will prioritize collecting fuel efficiency-related data. This allows for efficient collection of necessary data by filtering the data based on the current driving situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's current driving situation data into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the driver's emotions and determine the priority of driving data to collect based on the estimated driver's emotions. For example, if the driver is tense, the data collection unit will prioritize collecting driving data from tense periods. For example, if the driver is relaxed, the data collection unit will prioritize collecting driving data from relaxed periods. For example, if the driver is in a hurry, the data collection unit will prioritize collecting driving data from hurried periods. In this way, important data can be collected preferentially by determining the priority of data according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the driver's geographical location information when collecting driving data. For example, if the driver is driving in a mountainous area, the data collection unit will prioritize the collection of driving data in the mountainous area. For example, if the driver is driving in an urban area, the data collection unit will prioritize the collection of driving data in the urban area. For example, if the driver is driving in a suburban area, the data collection unit will prioritize the collection of driving data in the suburban area. By prioritizing the collection of highly relevant data based on geographical location information, more useful data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's geographical location information data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0079] The data collection unit can analyze the driver's social media activity and collect relevant data when collecting driving data. For example, if the driver posts about driving on social media, the data collection unit can collect driving data based on the content of those posts. For example, if the driver shows interest in a particular driving technique on social media, the data collection unit can collect data related to that technique. For example, if the driver shares information about fuel efficiency on social media, the data collection unit can collect driving data based on that information. This makes it possible to collect data that is tailored to the driver's interests by collecting relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0080] The analysis unit can estimate the driver's emotions and adjust the method of analyzing driving data based on the estimated emotions. For example, if the driver is stressed, the analysis unit will analyze the driving data in detail during stressful periods. For example, if the driver is relaxed, the analysis unit will analyze the driving data during relaxed periods in a simplified manner. For example, if the driver is tired, the analysis unit will focus on analyzing the driving data during fatigued periods. This allows for more appropriate analysis by adjusting the analysis method according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the driver's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the driving data during the analysis. For example, the analysis unit performs a detailed analysis on important driving data. For example, the analysis unit performs a simplified analysis on driving data of low importance. For example, the analysis unit applies multiple analysis methods to driving data of high importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the driving data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input driving data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of driving data during analysis. For example, the analysis unit applies a speed fluctuation analysis algorithm to speed data. For example, the analysis unit applies a fuel efficiency analysis algorithm to fuel consumption data. For example, the analysis unit applies a brake pattern analysis algorithm to brake usage frequency data. By applying different analysis algorithms depending on the category of driving data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input driving data category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated emotions of the driver. For example, if the driver is tense, the analysis unit provides a simple and highly visible display method. For example, if the driver is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the driver is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method according to the driver's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the driver's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0084] The analysis unit can determine the priority of analysis based on the timing of data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may analyze current driving data while referring to past driving data. For example, the analysis unit may focus on analyzing driving data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the timing of data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the timing of data collection into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the driving data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant driving data. For example, the analysis unit may postpone the analysis of less relevant driving data. For example, the analysis unit may group highly relevant data and analyze them all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the driving data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data of the driving data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The service provider can estimate the driver's emotions and adjust the way safe driving tips are presented based on the estimated emotions. For example, if the driver is tense, the service provider provides simple and easily recognizable tips. For example, if the driver is relaxed, the service provider provides tips containing detailed information. For example, if the driver is in a hurry, the service provider provides concise tips. By adjusting the way tips are presented according to the driver's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input driver emotion data into a generative AI and have the generative AI adjust the way tips are presented.

[0087] The service provider can adjust the level of detail of the hints based on the importance of the driving data when providing them. For example, hints based on important driving data may include detailed explanations. Hints based on less important driving data may include brief explanations. Hints based on highly important driving data may include advice from multiple perspectives. This allows for more efficient advice by adjusting the level of detail of the hints based on the importance of the driving data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input driving data importance data into a generating AI and have the generating AI adjust the level of detail of the hints.

[0088] The service provider can apply different hint-providing algorithms depending on the category of driving data at the time of provision. For example, hints based on speed data provide advice on suppressing speed fluctuations. For example, hints based on fuel efficiency data provide advice on improving fuel efficiency. For example, hints based on brake usage frequency data provide advice on how to use brakes appropriately. By applying different hint-providing algorithms depending on the category of driving data, more accurate advice becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input driving data category data into a generating AI and have the generating AI execute the application of different hint-providing algorithms.

[0089] The service provider can estimate the driver's emotions and adjust the length of hints based on the estimated emotions. For example, if the driver is tense, the service provider will provide short, concise hints. If the driver is relaxed, the service provider will provide longer hints with more detailed explanations. If the driver is in a hurry, the service provider will provide quick and concise hints. By adjusting the length of hints according to the driver's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input driver emotion data into a generative AI and have the generative AI adjust the length of hints.

[0090] The service provider can determine the priority of hints based on the timing of driving data collection at the time of provision. For example, the service provider may prioritize hints based on recently collected driving data. For example, the service provider may provide hints based on current driving data while referring to past driving data. For example, the service provider may focus on providing hints based on driving data collected during a specific period. This enables efficient advice by determining the priority of hints based on the timing of driving data collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input driving data collection timing data into a generating AI and have the generating AI perform the determination of hint priority.

[0091] The service provider can adjust the order of hints based on the relevance of the driving data at the time of provision. For example, the service provider may prioritize providing hints based on highly relevant driving data. For example, the service provider may postpone providing hints based on less relevant driving data. For example, the service provider may group highly relevant data and provide hints in a batch. This allows for efficient advice by adjusting the order of hints based on the relevance of the driving data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the relevance data of the driving data into a generating AI and have the generating AI perform the adjustment of the hint order.

[0092] The discount provision unit can estimate the driver's emotions and adjust the method of providing insurance premium discount data based on the estimated emotions. For example, if the driver is tense, the discount provision unit provides simple and easy-to-read discount data. For example, if the driver is relaxed, the discount provision unit provides discount data that includes detailed information. For example, if the driver is in a hurry, the discount provision unit provides concise discount data. This allows for more appropriate information to be provided by adjusting the method of providing discount data according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the discount provision unit may be performed using AI or not using AI. For example, the discount provision unit can input driver emotion data into a generative AI and have the generative AI adjust the method of providing discount data.

[0093] The discount provider can adjust the level of detail in the discount data based on the importance of the driving data when providing discount data. For example, the discount provider may include detailed explanations for discount data based on important driving data, a brief explanation for discount data based on less important driving data, and evaluations from multiple perspectives for discount data based on highly important driving data. This allows for efficient information provision by adjusting the level of detail in the discount data based on the importance of the driving data. Some or all of the above processing in the discount provider may be performed using AI, for example, or without AI. For example, the discount provider can input driving data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the discount data.

[0094] The discount provision unit can apply different discount data provision algorithms depending on the category of driving data when providing discount data. For example, discount data based on speed data may include an evaluation of the suppression of speed fluctuations. For example, discount data based on fuel efficiency data may include an evaluation of the improvement of fuel efficiency. For example, discount data based on brake usage frequency data may include an evaluation of the appropriate use of brakes. By applying different discount data provision algorithms depending on the category of driving data, more accurate information can be provided. Some or all of the above processing in the discount provision unit may be performed using AI, for example, or without AI. For example, the discount provision unit can input driving data category data into a generating AI and have the generating AI execute the application of different discount data provision algorithms.

[0095] The discount provider can estimate the driver's emotions and prioritize discount data based on the estimated emotions. For example, if the driver is tense, the discount provider will prioritize discount data based on driving data during tense periods. For example, if the driver is relaxed, the discount provider will prioritize discount data based on driving data during relaxed periods. For example, if the driver is in a hurry, the discount provider will prioritize discount data based on driving data during hurried periods. This allows for the provision of more appropriate information by prioritizing discount data according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, 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 processing in the discount provider may be performed using AI, for example, or without AI. For example, the discount provider can input the driver's emotion data into a generative AI and have the generative AI determine the priority of discount data.

[0096] The discount provision unit can determine the priority of discount data based on the timing of the driving data collection when providing discount data. For example, the discount provision unit may prioritize providing discount data based on recently collected driving data. For example, the discount provision unit may provide discount data based on current driving data while referring to past driving data. For example, the discount provision unit may focus on providing discount data based on driving data collected during a specific period. This enables efficient information provision by determining the priority of discount data based on the timing of the driving data collection. Some or all of the above processing in the discount provision unit may be performed using AI, for example, or without AI. For example, the discount provision unit may input driving data collection timing data into a generating AI and have the generating AI perform the determination of the priority of discount data.

[0097] The discount provision unit can adjust the order of discount data based on the relevance of the driving data when providing discount data. For example, the discount provision unit may prioritize providing discount data based on highly relevant driving data. For example, the discount provision unit may postpone providing discount data based on less relevant driving data. For example, the discount provision unit may group highly relevant data and provide the discount data in a batch. This allows for efficient information provision by adjusting the order of discount data based on the relevance of the driving data. Some or all of the above processing in the discount provision unit may be performed using AI, for example, or without AI. For example, the discount provision unit may input the relevance data of the driving data into a generating AI and have the generating AI perform the adjustment of the order of the discount data.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The drive assistance system can also be equipped with a real-time traffic information unit. This unit collects and provides drivers with information such as traffic congestion and accident data. This allows drivers to select the optimal route and improve driving efficiency. For example, if traffic congestion occurs, the unit can suggest alternative routes to the driver. It can also quickly provide information in the event of an accident, supporting the driver in driving safely. Furthermore, the unit can provide weather information and offer advice on driving precautions during adverse weather conditions.

[0100] The drive assistance system can also include a health management unit that monitors the driver's health. This unit can, for example, measure the driver's heart rate and blood pressure and issue warnings if abnormalities are detected. This allows for real-time monitoring of the driver's health and supports safe driving. For instance, the health management unit can issue an alert prompting the driver to take a break if their heart rate suddenly increases. It can also recommend contacting a medical institution if blood pressure is abnormally high. Furthermore, the health management unit can measure the driver's stress level and provide advice for relaxation.

[0101] The drive assistance system can also be equipped with customization features tailored to the driver's driving style. These customization features can automatically adjust system settings based on the driver's driving habits and preferences, allowing the driver to receive optimal driving support. For example, if a driver frequently uses highways, the customization features can focus on providing highway driving advice. Similarly, if a driver prioritizes eco-driving, the system can prioritize advice on improving fuel efficiency. Furthermore, the customization features can adjust the system interface to suit the driver's driving style, improving ease of use.

[0102] The drive assistance system can also include a training mode to support the driver's improvement of their driving skills. The training mode can, for example, evaluate the driver's driving technique and point out areas for improvement, thereby helping the driver acquire safer driving techniques and reduce the risk of accidents. For instance, the training mode can analyze the frequency of sudden braking and acceleration and provide specific advice for improvement. It can also provide feedback on curve speed and maintaining a safe following distance. Furthermore, the training mode can record the driver's progress and visualize the improvement in their driving skills.

[0103] The drive assistance system can also provide personalized reports based on the driver's driving history. These personalized reports can, for example, analyze the driver's driving data and evaluate monthly and weekly driving performance. This allows the driver to understand their driving tendencies and identify areas for improvement. For instance, personalized reports can display graphs showing the number of sudden braking incidents and fluctuations in fuel efficiency. They can also provide statistics on driving time and distance, allowing drivers to understand the frequency of long-distance driving. Furthermore, personalized reports can include advice on improving the driver's driving skills.

[0104] A driving assistance system can estimate the driver's emotions and adjust the music and entertainment provided while driving based on those estimated emotions. For example, if the driver is stressed, relaxing music can be played. If the driver is tired, uplifting music can be played. Furthermore, if the driver is relaxed, their preferred music can be played. This provides entertainment tailored to the driver's emotions, creating a comfortable driving environment. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The driving assistance system can estimate the driver's emotions and adjust the lighting while driving based on those emotions. For example, if the driver is tense, it can provide calming lighting. If the driver is relaxed, it can provide bright lighting. Furthermore, if the driver is tired, it can provide eye-friendly lighting. In this way, a comfortable driving environment can be achieved by providing lighting that matches the driver's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The driving assistance system can estimate the driver's emotions and adjust the temperature during driving based on those emotions. For example, if the driver is stressed, the temperature can be adjusted to a comfortable level. If the driver is relaxed, the temperature can be adjusted to their preference. Furthermore, if the driver is tired, the temperature can be adjusted to help them wake up. This provides a comfortable driving environment by offering temperature adjustments tailored to the driver's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The drive assistance system can estimate the driver's emotions and adjust the navigation voice during driving based on the estimated emotions. For example, if the driver is tense, the navigation can be delivered in a calm voice. If the driver is relaxed, the navigation can be delivered in a cheerful voice. Furthermore, if the driver is tired, the navigation can be delivered in an encouraging voice. This provides a comfortable driving environment by offering navigation voice that responds to the driver's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] A driving assistance system can estimate the driver's emotions and adjust the seat while driving based on those emotions. For example, if the driver is stressed, the seat can be adjusted to help them relax. If the driver is relaxed, the seat can be adjusted to maintain a comfortable posture. Furthermore, if the driver is tired, the seat can be adjusted to encourage them to take a break. This provides a comfortable driving environment by offering seat adjustments that respond to the driver's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects driving data. The data collection unit collects data such as vehicle speed, acceleration, brake usage frequency, and fuel consumption. For example, the data collection unit measures vehicle speed using GPS data or a speed sensor. For example, the data collection unit measures acceleration using an acceleration sensor. For example, the data collection unit measures brake usage frequency by the number of brake pedal operations or by the amount of time. For example, the data collection unit measures fuel consumption or fuel usage per unit of distance traveled. Step 2: The analysis unit analyzes the driving data collected by the collection unit. The analysis unit analyzes the driving data using, for example, data analysis methods and algorithms. For example, the analysis unit uses driving behavior classification and pattern recognition algorithms to identify driving patterns. Step 3: The service provider provides safe driving tips based on the analysis results obtained by the analysis provider. For example, the service provider provides suggestions for improving driving behavior and specific advice. For example, if the driver frequently uses sudden braking, the service provider will advise avoiding sudden braking. For example, the service provider will provide advice to improve fuel efficiency by recommending driving at a constant speed. Step 4: The discount provider provides premium discount data based on the hints provided by the provider. For example, the discount provider provides premium discount data based on safe driving performance. For example, the discount provider proposes a premium discount to drivers who frequently use sudden braking and drive at a constant speed.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and discount provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data such as vehicle speed, acceleration, brake usage frequency, and fuel efficiency using the camera 42 and sensors of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving data. The provision unit is implemented in the control unit 46A of the smart device 14 and provides tips for safe driving based on the analysis results. The discount provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides insurance premium discount data based on the tips provided by the provision unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and discount provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data such as vehicle speed, acceleration, brake usage frequency, and fuel consumption using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected driving data. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides tips for safe driving based on the analysis results. The discount provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides insurance premium discount data based on the tips provided by the provision unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and discount provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data such as vehicle speed, acceleration, brake usage frequency, and fuel consumption using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected driving data. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides tips for safe driving based on the analysis results. The discount provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides insurance premium discount data based on the tips provided by the provision unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and discount provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data such as vehicle speed, acceleration, brake usage frequency, and fuel consumption using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected driving data. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides tips for safe driving based on the analysis results. The discount provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides insurance premium discount data based on the tips provided by the provision unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects driving data, An analysis unit analyzes the driving data collected by the aforementioned collection unit, A providing unit that provides hints for safe driving based on the analysis results obtained by the aforementioned analysis unit, A discount provision unit provides discount data for insurance premiums based on hints provided by the provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as vehicle speed, acceleration, brake usage frequency, and fuel efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the collected driving data to identify driving patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides safe driving tips based on identified driving patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide advice on improving fuel efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned discount offering section is, We provide insurance premium discount data based on safe driving performance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the driver's emotions and adjusts the timing of driving data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the driver's past driving history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting driving data, filtering is performed based on the driver's current driving status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the driver's emotions and prioritizes the driving data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting driving data, the system prioritizes collecting highly relevant data by considering the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting driving data, analyze the driver's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the driver's emotions and adjust the analysis method of driving data based on the estimated driver emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the driver's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the driving data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the driver's emotions and adjusts the way safe driving tips are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the hints, adjust the level of detail based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing data, different hint-providing algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the driver's emotions and adjusts the length of the hints based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing hints, we prioritize them based on when the driving data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the tips, the order will be adjusted based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned discount offering section is, We estimate the driver's emotions and adjust how we provide insurance premium discount data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned discount offering section is, When providing discount data, the level of detail in the discount data will be adjusted based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned discount offering section is, When providing discount data, different discount data provision algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned discount offering section is, The system estimates the driver's emotions and prioritizes discount data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned discount offering section is, When providing discount data, the priority of the discount data is determined based on when the driving data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned discount offering section is, When providing discount data, the order of the discount data will be adjusted based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects driving data, An analysis unit analyzes the driving data collected by the aforementioned collection unit, A providing unit that provides hints for safe driving based on the analysis results obtained by the aforementioned analysis unit, A discount provision unit provides discount data for insurance premiums based on hints provided by the provision unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as vehicle speed, acceleration, brake usage frequency, and fuel efficiency. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the collected driving data to identify driving patterns. The system according to feature 1.

4. The aforementioned supply unit is, Provides safe driving tips based on identified driving patterns. The system according to feature 1.

5. The aforementioned supply unit is, We provide advice on improving fuel efficiency. The system according to feature 1.

6. The aforementioned discount offering section is, We provide insurance premium discount data based on safe driving performance. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the driver's emotions and adjusts the timing of driving data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the driver's past driving history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting driving data, filtering is performed based on the driver's current driving status and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the driver's emotions and prioritizes the driving data to collect based on the estimated emotions. The system according to feature 1.

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