system

Abstract

The system according to this embodiment aims to collect vehicle data in real time, analyze driving behavior, and provide feedback. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a feedback unit, and a provision unit. The data collection unit collects data from sensors mounted on the vehicle. The analysis unit analyzes the data collected by the data collection unit and analyzes driving behavior. The feedback unit provides real-time feedback based on the analysis results obtained by the analysis unit. The provision unit provides each user with Wi-Fi for installation in their vehicle.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, vehicle data has not been sufficiently collected in real time and driving behavior has not been sufficiently analyzed to provide feedback, leaving room for improvement.

[0005] The system according to the embodiment aims to collect vehicle data in real time, analyze driving behavior, and provide feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a feedback unit, and a data provision unit. The data collection unit collects data from sensors mounted on the vehicle. The analysis unit analyzes the data collected by the data collection unit and analyzes driving behavior. The feedback unit provides real-time feedback based on the analysis results obtained by the analysis unit. The data provision unit provides each user with Wi-Fi for installation in their vehicle. [Effects of the Invention]

[0007] The system according to this embodiment can collect vehicle data in real time, analyze driving behavior, and provide feedback. [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 multiple 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 30, 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 reception 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 reception 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 system according to an embodiment of the present invention is a system that utilizes IoT and generative AI to collect vehicle data in real time and provide individually optimized insurance, driving feedback, and travel recommendations through analysis of driving behavior. This system collects data in real time from sensors mounted on the vehicle, and the generative AI analyzes it to analyze driving behavior, thereby providing real-time feedback and recommendations tailored to individual driving patterns. In addition, each user is provided with Wi-Fi for installation in their car at a low cost, and data is acquired in a highly confidential state. This allows users to enjoy an internet environment in their car and enjoy video streaming services, etc. For example, data is collected in real time from sensors mounted on the vehicle. At this time, detailed data such as the vehicle's speed, position, acceleration, and brake usage is collected. For example, if the vehicle brakes suddenly, that data is collected. This provides detailed data on driving behavior. Next, the generative AI analyzes the collected data. Based on the collected data, the generative AI analyzes driving behavior and identifies individual driving patterns. For example, the frequency of sudden braking and the tendency to exceed the speed limit are analyzed. This reveals the characteristics of driving behavior. Based on the analysis results, real-time feedback and recommendations tailored to individual driving patterns are provided. For example, advice to encourage safe driving and suggestions for driving methods to improve fuel efficiency are offered. This allows drivers to improve their driving behavior. Furthermore, each user is provided with Wi-Fi for installation in their car at a low cost, and data is acquired in a highly confidential manner. This allows users to enjoy an internet environment in their car and enjoy video streaming services, etc. For example, they can watch movies during long drives. This system provides individually optimized insurance, driving feedback, and travel recommendations, improving the quality of life for users. In addition, the collected data can be networked to enable industry-wide trend analysis. This is expected to lead to the development of new insurance products and contributions to the tourism industry. As a result, the system can collect vehicle data in real time and provide individually optimized insurance, driving feedback, and travel recommendations through the analysis of driving behavior.

[0029] The system according to the embodiment comprises a data collection unit, an analysis unit, a feedback unit, and a data provision unit. The data collection unit collects data from sensors mounted on the vehicle. The data collection unit collects data such as the vehicle's speed, position, acceleration, and brake usage. For example, the data collection unit can collect data when the vehicle applies sudden brakes. The data collection unit can also monitor the vehicle's speed in real time and collect data on speeding. For example, the data collection unit can collect vehicle position data using GPS. The analysis unit analyzes the data collected by the data collection unit and analyzes driving behavior. For example, the analysis unit analyzes driving behavior based on the collected data and identifies individual driving patterns. For example, the analysis unit can analyze the frequency of sudden braking and the tendency for speeding. The analysis unit can also clarify the characteristics of driving behavior based on the collected data. For example, the analysis unit can identify the characteristics of driving behavior and provide advice to promote safe driving. The feedback unit provides real-time feedback based on the analysis results obtained by the analysis unit. For example, the feedback unit provides advice to promote safe driving. The feedback unit can, for example, suggest driving methods to improve fuel efficiency. The feedback unit can also provide the driver with real-time suggestions for improving their driving behavior. The feedback unit can, for example, provide the driver with visual aids indicating areas for improvement in their driving behavior. The service provider provides each user with Wi-Fi for installation in their vehicle. The service provider can, for example, provide Wi-Fi for installation in each user at a low cost. The service provider can, for example, acquire data while maintaining high confidentiality. Furthermore, the service provider enables users to enjoy an internet environment in their vehicles and access video streaming services, etc. The service provider can, for example, enable users to watch movies during long drives. As a result, the system according to this embodiment can collect vehicle data in real time and provide individually optimized insurance, driving feedback, and travel recommendations through analysis of driving behavior.

[0030] The data collection unit collects data from sensors mounted on the vehicle. Specifically, it collects data such as vehicle speed, position, acceleration, and brake usage. Vehicle speed is monitored in real time using a speed sensor, and if speeding occurs, the data is urgently collected. Position data is accurately acquired using GPS, and the vehicle's current position and travel path are recorded. Acceleration data is obtained using an acceleration sensor to understand the vehicle's acceleration and deceleration in detail. Brake usage is monitored using a brake sensor to collect data such as sudden braking and braking frequency. This allows the data collection unit to comprehensively understand the vehicle's driving conditions and collect data in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and feedback units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit to analyze driving behavior. Specifically, it analyzes driving behavior in detail based on the collected data to identify individual driving patterns. For example, it analyzes the frequency of sudden braking and the tendency to exceed the speed limit to reveal the driver's driving style and risk factors. The analysis unit uses AI to process data in real time and identify characteristics of driving behavior. The AI ​​uses machine learning algorithms to learn patterns of driving behavior from the collected data and detect abnormal or high-risk driving behavior. For example, it provides advice to drivers who frequently brake suddenly to improve their braking timing. It also provides advice to drivers who tend to exceed the speed limit to help them maintain an appropriate speed. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term driving behavior trends and predict future risks. As a result, the analysis unit can provide individually optimized advice to drivers through detailed analysis of driving behavior, promoting safe driving.

[0032] The feedback unit provides real-time feedback based on the analysis results obtained by the analysis unit. Specifically, it provides drivers with advice to promote safe driving and suggestions for improving their driving methods. For example, drivers who frequently brake suddenly will receive specific advice on how to improve their braking timing. The feedback unit can also provide drivers with real-time visual aids that show areas for improvement in their driving behavior. This makes it easier for drivers to visually understand areas for improvement. The feedback unit can also use a voice assistant to notify drivers of areas for improvement in their driving behavior. For example, if speeding occurs, the voice assistant will prompt the driver to slow down. Furthermore, the feedback unit can continuously monitor the driver's driving behavior and provide positive feedback when improvements are observed. This makes it easier for drivers to maintain motivation to improve their driving behavior. The feedback unit can provide drivers with individually optimized feedback, thereby promoting safe driving.

[0033] The service provider will provide each user with Wi-Fi for installation in their vehicle. Specifically, it will provide each user with Wi-Fi for installation in their vehicle at a low cost, and will be able to acquire data while enhancing data confidentiality. The service provider will enable users to enjoy an internet environment in their vehicles and enjoy video streaming services, etc. For example, they will be able to watch movies during long drives. The service provider will provide a high-speed and stable communication environment to ensure a stable internet connection in the vehicle. This will allow users to comfortably use the internet in their vehicles. In addition, the service provider can improve the overall system performance by providing the communication infrastructure necessary for collecting and analyzing vehicle data. For example, it will establish a communication environment that allows the collection and analysis units to send and receive data in real time. Furthermore, the service provider will implement data encryption and security measures to protect user privacy. This will allow users to use the internet in their vehicles with peace of mind. The service provider can provide users with a comfortable internet environment and improve the overall system performance.

[0034] The data collection unit can collect data such as vehicle speed, position, acceleration, and brake usage. For example, the data collection unit can monitor vehicle speed in real time and collect data on speeding. For example, the data collection unit can collect vehicle position data using GPS. The data collection unit can also collect data when the vehicle applies sudden brakes. For example, the data collection unit can collect vehicle acceleration data to obtain data on sudden acceleration and sudden deceleration. This allows for detailed analysis of driving behavior by collecting detailed vehicle data. 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 process data on speeding.

[0035] The analysis unit can analyze driving behavior based on collected data and identify individual driving patterns. For example, the analysis unit can analyze driving behavior based on collected data and identify the frequency of sudden braking and the tendency to exceed the speed limit. For example, the analysis unit can reveal the characteristics of driving behavior. For example, the analysis unit can identify the characteristics of driving behavior and provide advice to promote safe driving. The analysis unit can also indicate areas for improvement in driving behavior based on collected data. For example, the analysis unit can provide a visual aid that shows areas for improvement in driving behavior. This allows for the provision of individually optimized feedback by revealing the characteristics of driving behavior. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input collected data into a generative AI and have the generative AI perform the analysis of driving behavior.

[0036] The feedback unit can provide advice to encourage safe driving and suggest driving methods to improve fuel efficiency. For example, the feedback unit can provide advice to encourage safe driving. For example, the feedback unit can suggest driving methods to improve fuel efficiency. The feedback unit can also show the driver areas for improvement in their driving behavior in real time. For example, the feedback unit can provide the driver with visual aids showing areas for improvement in their driving behavior. This allows the driver to improve their own driving behavior. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the analysis results obtained by the analysis unit into a generating AI and have the generating AI perform real-time feedback.

[0037] The service provider can provide each user with Wi-Fi for installation in their car at a low cost and acquire data in a highly confidential manner. The service provider can, for example, provide each user with Wi-Fi for installation in their car at a low cost. The service provider can, for example, acquire data in a highly confidential manner. The service provider can also enable users to enjoy an internet environment in their car and enjoy video streaming services, etc. The service provider can, for example, enable users to watch movies during long drives. This allows users to enjoy an internet environment in their car and enjoy video streaming services, etc. 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 the Wi-Fi settings to be provided to each user into a generating AI and have the generating AI perform the Wi-Fi provision.

[0038] The service provider can enable users to enjoy an internet environment in their car and access video streaming services, etc. For example, the service provider can enable users to watch movies during long drives. The service provider can enable users to enjoy an internet environment in their car and access video streaming services, etc. Furthermore, the service provider can enable users to enjoy an internet environment in their car and access music streaming. The service provider can also enable users to enjoy an internet environment in their car and access online games. This allows users to watch movies during long drives. 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 the settings for the internet environment to be provided to each user into a generating AI and have the generating AI provide the internet environment.

[0039] The data collection unit can dynamically change the frequency of data collection according to the vehicle's operating conditions. For example, when driving on a highway, the data collection unit can increase the frequency of data collection to obtain detailed driving data. For example, when driving in an urban area, the data collection unit can decrease the frequency of data collection to reduce the burden on the driver. Furthermore, during traffic congestion, the data collection unit can increase the frequency of data collection to record changes in driving behavior in detail. In this way, detailed driving data can be obtained by changing the frequency of data collection according to the operating conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input vehicle operating condition data into a generating AI and have the generating AI change the frequency of data collection.

[0040] The data collection unit can collect vehicle maintenance information and analyze its relevance to driving behavior. For example, the data collection unit can record the timing of vehicle oil changes and analyze changes in driving behavior. For example, the data collection unit can record the wear status of tires and analyze its relevance to driving behavior. The data collection unit can also record the timing of brake pad replacement and analyze changes in driving behavior. In this way, changes in driving behavior can be analyzed by collecting maintenance information. 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 maintenance information into a generating AI and have the generating AI perform an analysis of its relevance to driving behavior.

[0041] The data collection unit can collect external environmental data of the vehicle (weather, traffic conditions, etc.) and analyze its relationship to driving behavior. For example, the data collection unit can record driving behavior in rainy weather and analyze its relationship to the weather. For example, the data collection unit can record driving behavior during traffic congestion and analyze its relationship to traffic conditions. The data collection unit can also record driving behavior at night and analyze its relationship to the time of day. In this way, by collecting external environmental data, the relationship to driving behavior can be analyzed. 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 vehicle's external environmental data into a generating AI and have the generating AI perform an analysis of its relationship to driving behavior.

[0042] The data collection unit can collect data on the use of the vehicle's entertainment system and analyze its relationship to driving behavior. For example, the data collection unit can record driving behavior while music is playing and analyze its relationship to the entertainment system. For example, the data collection unit can record driving behavior while watching videos and analyze its relationship to the entertainment system. The data collection unit can also record driving behavior while playing games and analyze its relationship to the entertainment system. In this way, by collecting data on the use of the entertainment system, its relationship to driving behavior can be analyzed. 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 data on the use of the vehicle's entertainment system into a generating AI and have the generating AI perform an analysis of its relationship to driving behavior.

[0043] The analysis unit can identify abnormal driving patterns by comparing them with past driving data during the analysis of driving behavior. For example, the analysis unit can identify an anomaly if the frequency of sudden braking has increased compared to past driving data. For example, the analysis unit can identify an anomaly if there is a tendency for excessive speed compared to past driving data. The analysis unit can also identify an anomaly if the frequency of sudden acceleration has increased compared to past driving data. In this way, abnormal driving patterns can be identified by comparing them with past driving data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past driving data into a generating AI and have the generating AI perform the identification of abnormal driving patterns.

[0044] The analysis unit can improve the accuracy of its analysis of driving behavior by considering vehicle maintenance information. For example, the analysis unit can analyze changes in driving behavior by considering the timing of vehicle oil changes. For example, the analysis unit can analyze changes in driving behavior by considering the wear status of tires. Furthermore, the analysis unit can analyze changes in driving behavior by considering the timing of brake pad replacement. This improves the accuracy of the analysis by considering maintenance information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input vehicle maintenance information into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0045] The analysis unit can improve the accuracy of its analysis by considering external environmental data (weather, traffic conditions, etc.) when analyzing driving behavior. For example, the analysis unit can analyze driving behavior in rainy weather and consider its relationship to the weather. For example, the analysis unit can analyze driving behavior during traffic congestion and consider its relationship to traffic conditions. Furthermore, the analysis unit can analyze driving behavior at night and consider its relationship to the time of day. By considering external environmental data, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input external environmental data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0046] The analysis unit can improve the accuracy of its analysis of driving behavior by considering the usage status of the vehicle's entertainment system. For example, the analysis unit can analyze driving behavior while music is playing and consider its relationship to the entertainment system. For example, the analysis unit can analyze driving behavior while watching videos and consider its relationship to the entertainment system. Furthermore, the analysis unit can analyze driving behavior while playing games and consider its relationship to the entertainment system. This improves the accuracy of the analysis by considering the usage status of the entertainment system. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input entertainment system usage data into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0047] The feedback unit can provide visual aids to specifically indicate areas for improvement in driving behavior during feedback. For example, the feedback unit can provide graphs or charts showing areas for improvement in driving behavior. For example, the feedback unit can provide videos showing areas for improvement in driving behavior. The feedback unit can also provide animations showing areas for improvement in driving behavior. This makes it easier for drivers to understand areas for improvement by specifically showing them. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the creation of visual aids showing areas for improvement in driving behavior into a generating AI and have the generating AI provide the visual aids.

[0048] The feedback unit can provide optimal advice by referring to past feedback history during the feedback process. For example, the feedback unit can provide optimal advice based on past feedback history. For example, the feedback unit can indicate areas for improvement in driving behavior based on past feedback history. The feedback unit can also indicate changes in driving behavior based on past feedback history. This allows the feedback unit to provide optimal advice by referring to past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past feedback history into a generating AI and have the generating AI perform the task of providing optimal advice.

[0049] The feedback unit can provide advice by taking external environmental data (weather, traffic conditions, etc.) into consideration when providing feedback. For example, the feedback unit can provide advice on driving behavior in rainy weather. For example, the feedback unit can provide advice on driving behavior in traffic congestion. The feedback unit can also provide advice on driving behavior at night. By taking external environmental data into consideration, appropriate advice can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input external environmental data into a generating AI and have the generating AI perform the task of providing advice.

[0050] The feedback unit can provide advice when providing feedback, taking into account the usage of the vehicle's entertainment system. For example, the feedback unit can provide advice regarding driving behavior while music is playing. For example, the feedback unit can provide advice regarding driving behavior while watching videos. The feedback unit can also provide advice regarding driving behavior while playing games. This allows for the provision of appropriate advice by taking into account the usage of the entertainment system. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input entertainment system usage data into a generating AI and have the generating AI perform the task of providing advice.

[0051] The service provider can monitor the user's data usage when providing Wi-Fi and propose the optimal data plan. For example, if the user is likely to exceed their data usage limit, the service provider can propose the optimal data plan. For example, if the user wants to conserve data usage, the service provider can propose a low-data-usage plan. Furthermore, if the user uses a large amount of data, the service provider can propose an unlimited data plan. In this way, the service provider can propose the optimal data plan by monitoring the user's data usage. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's data usage into a generating AI and have the generating AI propose the optimal data plan.

[0052] The service provider can dynamically change the priority of data usage according to the vehicle's operating status when providing Wi-Fi. For example, when driving on a highway, the service provider can set a high priority for data usage to provide a stable internet connection. For example, when driving in an urban area, the service provider can set a low priority for data usage to reduce the burden on the driver. In addition, during traffic congestion, the service provider can set a high priority for data usage to provide entertainment services. In this way, a stable internet connection can be provided by changing the priority of data usage according to the operating status. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input vehicle operating status data into a generating AI and have the generating AI execute the change in data usage priority.

[0053] The service provider can propose an optimal data plan when providing Wi-Fi, taking into account external environmental data (weather, traffic conditions, etc.). For example, during rainy weather, the service provider can propose a data plan that provides a stable internet connection. During traffic congestion, for example, the service provider can propose a data plan that provides entertainment services. Furthermore, the service provider can propose a low-data usage plan at night. In this way, the service provider can propose an optimal data plan by taking external environmental data into consideration. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input external environmental data into a generating AI and have the generating AI propose an optimal data plan.

[0054] The service provider can propose an optimal data plan when providing Wi-Fi, taking into account the usage of the vehicle's entertainment system. For example, the service provider can propose a low-data usage plan when music is being played. For example, the service provider can propose a data plan that provides a stable internet connection when watching videos. The service provider can also propose a high-speed data plan when playing games. In this way, the service provider can propose an optimal data plan by taking into account the usage of the entertainment system. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input entertainment system usage data into a generating AI and have the generating AI propose an optimal data plan.

[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 analysis unit can consider the user's health data when analyzing their driving behavior. For example, it can collect data on the user's heart rate and blood pressure and analyze its relationship to driving behavior. This allows it to clarify the impact of health status on driving behavior and provide driving advice tailored to the user's health status. It can also issue alerts to encourage users to take breaks if they are fatigued. Furthermore, it can offer suggestions to avoid long hours of driving based on health data.

[0057] The feedback system can provide training programs to improve driving skills based on the user's driving behavior. For example, it can suggest training using a driving simulator to help users acquire safe driving techniques. It can also provide online courses to improve driving skills, allowing users to study at home. Furthermore, it can introduce workshops and seminars to improve driving skills, providing opportunities to receive training in a real driving environment.

[0058] The system can optimize vehicle maintenance schedules based on user driving behavior data. For example, it can analyze driving behavior data to predict tire wear and oil change timing. This allows for maintenance to be performed at the appropriate time, maintaining vehicle performance. It can also notify users of maintenance needs and encourage them to make reservations. Furthermore, it can manage maintenance history and use it as a reference for future maintenance planning.

[0059] The data collection unit can suggest eco-driving practices based on vehicle operation data. For example, it can suggest fuel-efficient driving methods to help users save fuel. It can also evaluate the effectiveness of eco-driving practices and provide feedback to users. Furthermore, it can visualize the environmental contribution of eco-driving practices, thereby increasing user motivation. This encourages users to drive in an environmentally friendly manner.

[0060] The analysis unit can evaluate driving risks based on user driving behavior data. For example, it can analyze the frequency of sudden braking and acceleration to identify high-risk driving behaviors. This allows the system to suggest risk reduction methods to the user. Furthermore, the results of the driving risk evaluation can be provided to insurance companies for use as a reference for obtaining premium discounts. In addition, it is possible to propose training programs to improve driving skills based on the driving risk evaluation results.

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

[0062] Step 1: The data collection unit collects data from sensors mounted on the vehicle. The data collection unit collects data such as the vehicle's speed, position, acceleration, and brake usage. For example, the data collection unit can collect data if the vehicle applies the brakes suddenly. The data collection unit can also monitor the vehicle's speed in real time and collect data on speeding. For example, the data collection unit can collect vehicle position data using GPS. Step 2: The analysis unit analyzes the data collected by the collection unit and analyzes driving behavior. For example, the analysis unit analyzes driving behavior based on the collected data and identifies individual driving patterns. For example, the analysis unit can analyze the frequency of sudden braking or the tendency to exceed the speed limit. The analysis unit can also reveal characteristics of driving behavior based on the collected data. For example, the analysis unit can identify characteristics of driving behavior and provide advice to promote safe driving. Step 3: The feedback unit provides real-time feedback based on the analysis results obtained by the analysis unit. The feedback unit provides, for example, advice to encourage safe driving. The feedback unit can suggest, for example, driving methods to improve fuel efficiency. The feedback unit can also show the driver areas for improvement in driving behavior in real time. The feedback unit can provide, for example, a visual aid to show the driver areas for improvement in driving behavior. Step 4: The provider provides each user with Wi-Fi for installation in their car. The provider provides each user with Wi-Fi for installation in their car at a low cost. The provider can acquire data in a highly secure manner. The provider also enables users to enjoy an internet environment in their car and enjoy video streaming services, etc. The provider enables users to watch movies during long drives, for example.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes IoT and generative AI to collect vehicle data in real time and provide individually optimized insurance, driving feedback, and travel recommendations through analysis of driving behavior. This system collects data in real time from sensors mounted on the vehicle, and the generative AI analyzes it to analyze driving behavior, thereby providing real-time feedback and recommendations tailored to individual driving patterns. In addition, each user is provided with Wi-Fi for installation in their car at a low cost, and data is acquired in a highly confidential state. This allows users to enjoy an internet environment in their car and enjoy video streaming services, etc. For example, data is collected in real time from sensors mounted on the vehicle. At this time, detailed data such as the vehicle's speed, position, acceleration, and brake usage is collected. For example, if the vehicle brakes suddenly, that data is collected. This provides detailed data on driving behavior. Next, the generative AI analyzes the collected data. Based on the collected data, the generative AI analyzes driving behavior and identifies individual driving patterns. For example, the frequency of sudden braking and the tendency to exceed the speed limit are analyzed. This reveals the characteristics of driving behavior. Based on the analysis results, real-time feedback and recommendations tailored to individual driving patterns are provided. For example, advice to encourage safe driving and suggestions for driving methods to improve fuel efficiency are offered. This allows drivers to improve their driving behavior. Furthermore, each user is provided with Wi-Fi for installation in their car at a low cost, and data is acquired in a highly confidential manner. This allows users to enjoy an internet environment in their car and enjoy video streaming services, etc. For example, they can watch movies during long drives. This system provides individually optimized insurance, driving feedback, and travel recommendations, improving the quality of life for users. In addition, the collected data can be networked to enable industry-wide trend analysis. This is expected to lead to the development of new insurance products and contributions to the tourism industry. As a result, the system can collect vehicle data in real time and provide individually optimized insurance, driving feedback, and travel recommendations through the analysis of driving behavior.

[0064] The system according to the embodiment comprises a data collection unit, an analysis unit, a feedback unit, and a data provision unit. The data collection unit collects data from sensors mounted on the vehicle. The data collection unit collects data such as the vehicle's speed, position, acceleration, and brake usage. For example, the data collection unit can collect data when the vehicle applies sudden brakes. The data collection unit can also monitor the vehicle's speed in real time and collect data on speeding. For example, the data collection unit can collect vehicle position data using GPS. The analysis unit analyzes the data collected by the data collection unit and analyzes driving behavior. For example, the analysis unit analyzes driving behavior based on the collected data and identifies individual driving patterns. For example, the analysis unit can analyze the frequency of sudden braking and the tendency for speeding. The analysis unit can also clarify the characteristics of driving behavior based on the collected data. For example, the analysis unit can identify the characteristics of driving behavior and provide advice to promote safe driving. The feedback unit provides real-time feedback based on the analysis results obtained by the analysis unit. For example, the feedback unit provides advice to promote safe driving. The feedback unit can, for example, suggest driving methods to improve fuel efficiency. The feedback unit can also provide the driver with real-time suggestions for improving their driving behavior. The feedback unit can, for example, provide the driver with visual aids indicating areas for improvement in their driving behavior. The service provider provides each user with Wi-Fi for installation in their vehicle. The service provider can, for example, provide Wi-Fi for installation in each user at a low cost. The service provider can, for example, acquire data while maintaining high confidentiality. Furthermore, the service provider enables users to enjoy an internet environment in their vehicles and access video streaming services, etc. The service provider can, for example, enable users to watch movies during long drives. As a result, the system according to this embodiment can collect vehicle data in real time and provide individually optimized insurance, driving feedback, and travel recommendations through analysis of driving behavior.

[0065] The data collection unit collects data from sensors mounted on the vehicle. Specifically, it collects data such as vehicle speed, position, acceleration, and brake usage. Vehicle speed is monitored in real time using a speed sensor, and if speeding occurs, the data is urgently collected. Position data is accurately acquired using GPS, and the vehicle's current position and travel path are recorded. Acceleration data is obtained using an acceleration sensor to understand the vehicle's acceleration and deceleration in detail. Brake usage is monitored using a brake sensor to collect data such as sudden braking and braking frequency. This allows the data collection unit to comprehensively understand the vehicle's driving conditions and collect data in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and feedback units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the data collected by the data collection unit to analyze driving behavior. Specifically, it analyzes driving behavior in detail based on the collected data to identify individual driving patterns. For example, it analyzes the frequency of sudden braking and the tendency to exceed the speed limit to reveal the driver's driving style and risk factors. The analysis unit uses AI to process data in real time and identify characteristics of driving behavior. The AI ​​uses machine learning algorithms to learn patterns of driving behavior from the collected data and detect abnormal or high-risk driving behavior. For example, it provides advice to drivers who frequently brake suddenly to improve their braking timing. It also provides advice to drivers who tend to exceed the speed limit to help them maintain an appropriate speed. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term driving behavior trends and predict future risks. As a result, the analysis unit can provide individually optimized advice to drivers through detailed analysis of driving behavior, promoting safe driving.

[0067] The feedback unit provides real-time feedback based on the analysis results obtained by the analysis unit. Specifically, it provides drivers with advice to promote safe driving and suggestions for improving their driving methods. For example, drivers who frequently brake suddenly will receive specific advice on how to improve their braking timing. The feedback unit can also provide drivers with real-time visual aids that show areas for improvement in their driving behavior. This makes it easier for drivers to visually understand areas for improvement. The feedback unit can also use a voice assistant to notify drivers of areas for improvement in their driving behavior. For example, if speeding occurs, the voice assistant will prompt the driver to slow down. Furthermore, the feedback unit can continuously monitor the driver's driving behavior and provide positive feedback when improvements are observed. This makes it easier for drivers to maintain motivation to improve their driving behavior. The feedback unit can provide drivers with individually optimized feedback, thereby promoting safe driving.

[0068] The service provider will provide each user with Wi-Fi for installation in their vehicle. Specifically, it will provide each user with Wi-Fi for installation in their vehicle at a low cost, and will be able to acquire data while enhancing data confidentiality. The service provider will enable users to enjoy an internet environment in their vehicles and enjoy video streaming services, etc. For example, they will be able to watch movies during long drives. The service provider will provide a high-speed and stable communication environment to ensure a stable internet connection in the vehicle. This will allow users to comfortably use the internet in their vehicles. In addition, the service provider can improve the overall system performance by providing the communication infrastructure necessary for collecting and analyzing vehicle data. For example, it will establish a communication environment that allows the collection and analysis units to send and receive data in real time. Furthermore, the service provider will implement data encryption and security measures to protect user privacy. This will allow users to use the internet in their vehicles with peace of mind. The service provider can provide users with a comfortable internet environment and improve the overall system performance.

[0069] The data collection unit can collect data such as vehicle speed, position, acceleration, and brake usage. For example, the data collection unit can monitor vehicle speed in real time and collect data on speeding. For example, the data collection unit can collect vehicle position data using GPS. The data collection unit can also collect data when the vehicle applies sudden brakes. For example, the data collection unit can collect vehicle acceleration data to obtain data on sudden acceleration and sudden deceleration. This allows for detailed analysis of driving behavior by collecting detailed vehicle data. 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 process data on speeding.

[0070] The analysis unit can analyze driving behavior based on collected data and identify individual driving patterns. For example, the analysis unit can analyze driving behavior based on collected data and identify the frequency of sudden braking and the tendency to exceed the speed limit. For example, the analysis unit can reveal the characteristics of driving behavior. For example, the analysis unit can identify the characteristics of driving behavior and provide advice to promote safe driving. The analysis unit can also indicate areas for improvement in driving behavior based on collected data. For example, the analysis unit can provide a visual aid that shows areas for improvement in driving behavior. This allows for the provision of individually optimized feedback by revealing the characteristics of driving behavior. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input collected data into a generative AI and have the generative AI perform the analysis of driving behavior.

[0071] The feedback unit can provide advice to encourage safe driving and suggest driving methods to improve fuel efficiency. For example, the feedback unit can provide advice to encourage safe driving. For example, the feedback unit can suggest driving methods to improve fuel efficiency. The feedback unit can also show the driver areas for improvement in their driving behavior in real time. For example, the feedback unit can provide the driver with visual aids showing areas for improvement in their driving behavior. This allows the driver to improve their own driving behavior. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the analysis results obtained by the analysis unit into a generating AI and have the generating AI perform real-time feedback.

[0072] The service provider can provide each user with Wi-Fi for installation in their car at a low cost and acquire data in a highly confidential manner. The service provider can, for example, provide each user with Wi-Fi for installation in their car at a low cost. The service provider can, for example, acquire data in a highly confidential manner. The service provider can also enable users to enjoy an internet environment in their car and enjoy video streaming services, etc. The service provider can, for example, enable users to watch movies during long drives. This allows users to enjoy an internet environment in their car and enjoy video streaming services, etc. 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 the Wi-Fi settings to be provided to each user into a generating AI and have the generating AI perform the Wi-Fi provision.

[0073] The service provider can enable users to enjoy an internet environment in their car and access video streaming services, etc. For example, the service provider can enable users to watch movies during long drives. The service provider can enable users to enjoy an internet environment in their car and access video streaming services, etc. Furthermore, the service provider can enable users to enjoy an internet environment in their car and access music streaming. The service provider can also enable users to enjoy an internet environment in their car and access online games. This allows users to watch movies during long drives. 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 the settings for the internet environment to be provided to each user into a generating AI and have the generating AI provide the internet environment.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to allow the user to concentrate on driving. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to obtain more detailed driving data. The data collection unit can also reduce the data collection load by prioritizing the collection of only important data if the user is in a hurry. This allows the user to concentrate on driving by adjusting the timing of data collection according to their 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 user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0075] The data collection unit can dynamically change the frequency of data collection according to the vehicle's operating conditions. For example, when driving on a highway, the data collection unit can increase the frequency of data collection to obtain detailed driving data. For example, when driving in an urban area, the data collection unit can decrease the frequency of data collection to reduce the burden on the driver. Furthermore, during traffic congestion, the data collection unit can increase the frequency of data collection to record changes in driving behavior in detail. In this way, detailed driving data can be obtained by changing the frequency of data collection according to the operating conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input vehicle operating condition data into a generating AI and have the generating AI change the frequency of data collection.

[0076] The data collection unit can collect vehicle maintenance information and analyze its relevance to driving behavior. For example, the data collection unit can record the timing of vehicle oil changes and analyze changes in driving behavior. For example, the data collection unit can record the wear status of tires and analyze its relevance to driving behavior. The data collection unit can also record the timing of brake pad replacement and analyze changes in driving behavior. In this way, changes in driving behavior can be analyzed by collecting maintenance information. 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 maintenance information into a generating AI and have the generating AI perform an analysis of its relevance to driving behavior.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data, thereby reducing the data collection burden. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed driving data. Also, if the user is in a hurry, the data collection unit can prioritize collecting data related to driving behavior. This reduces the data collection burden by prioritizing the data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0078] The data collection unit can collect external environmental data of the vehicle (weather, traffic conditions, etc.) and analyze its relationship to driving behavior. For example, the data collection unit can record driving behavior in rainy weather and analyze its relationship to the weather. For example, the data collection unit can record driving behavior during traffic congestion and analyze its relationship to traffic conditions. The data collection unit can also record driving behavior at night and analyze its relationship to the time of day. In this way, by collecting external environmental data, the relationship to driving behavior can be analyzed. 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 vehicle's external environmental data into a generating AI and have the generating AI perform an analysis of its relationship to driving behavior.

[0079] The data collection unit can collect data on the use of the vehicle's entertainment system and analyze its relationship to driving behavior. For example, the data collection unit can record driving behavior while music is playing and analyze its relationship to the entertainment system. For example, the data collection unit can record driving behavior while watching videos and analyze its relationship to the entertainment system. The data collection unit can also record driving behavior while playing games and analyze its relationship to the entertainment system. In this way, by collecting data on the use of the entertainment system, its relationship to driving behavior can be analyzed. 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 data on the use of the vehicle's entertainment system into a generating AI and have the generating AI perform an analysis of its relationship to driving behavior.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can simplify the analysis algorithm and perform a rapid analysis. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and improve accuracy. Furthermore, if the user is in a hurry, the analysis unit can prioritize the analysis of only the important data. This allows for rapid and highly accurate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0081] The analysis unit can identify abnormal driving patterns by comparing them with past driving data during the analysis of driving behavior. For example, the analysis unit can identify an anomaly if the frequency of sudden braking has increased compared to past driving data. For example, the analysis unit can identify an anomaly if there is a tendency for excessive speed compared to past driving data. The analysis unit can also identify an anomaly if the frequency of sudden acceleration has increased compared to past driving data. In this way, abnormal driving patterns can be identified by comparing them with past driving data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past driving data into a generating AI and have the generating AI perform the identification of abnormal driving patterns.

[0082] The analysis unit can improve the accuracy of its analysis of driving behavior by considering vehicle maintenance information. For example, the analysis unit can analyze changes in driving behavior by considering the timing of vehicle oil changes. For example, the analysis unit can analyze changes in driving behavior by considering the wear status of tires. Furthermore, the analysis unit can analyze changes in driving behavior by considering the timing of brake pad replacement. This improves the accuracy of the analysis by considering maintenance information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input vehicle maintenance information into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a highly visible display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0084] The analysis unit can improve the accuracy of its analysis by considering external environmental data (weather, traffic conditions, etc.) when analyzing driving behavior. For example, the analysis unit can analyze driving behavior in rainy weather and consider its relationship to the weather. For example, the analysis unit can analyze driving behavior during traffic congestion and consider its relationship to traffic conditions. Furthermore, the analysis unit can analyze driving behavior at night and consider its relationship to the time of day. By considering external environmental data, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input external environmental data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0085] The analysis unit can improve the accuracy of its analysis of driving behavior by considering the usage status of the vehicle's entertainment system. For example, the analysis unit can analyze driving behavior while music is playing and consider its relationship to the entertainment system. For example, the analysis unit can analyze driving behavior while watching videos and consider its relationship to the entertainment system. Furthermore, the analysis unit can analyze driving behavior while playing games and consider its relationship to the entertainment system. This improves the accuracy of the analysis by considering the usage status of the entertainment system. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input entertainment system usage data into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0086] The feedback unit can estimate the user's emotions and adjust the way the feedback is presented based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide simple and easily understandable feedback. If the user is relaxed, the feedback unit can provide feedback that includes detailed information. Furthermore, if the user is in a hurry, the feedback unit can provide concise feedback. This allows for easily understandable feedback by adjusting the presentation of feedback according to the user'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-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the feedback.

[0087] The feedback unit can provide visual aids to specifically indicate areas for improvement in driving behavior during feedback. For example, the feedback unit can provide graphs or charts showing areas for improvement in driving behavior. For example, the feedback unit can provide videos showing areas for improvement in driving behavior. The feedback unit can also provide animations showing areas for improvement in driving behavior. This makes it easier for drivers to understand areas for improvement by specifically showing them. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the creation of visual aids showing areas for improvement in driving behavior into a generating AI and have the generating AI provide the visual aids.

[0088] The feedback unit can provide optimal advice by referring to past feedback history during the feedback process. For example, the feedback unit can provide optimal advice based on past feedback history. For example, the feedback unit can indicate areas for improvement in driving behavior based on past feedback history. The feedback unit can also indicate changes in driving behavior based on past feedback history. This allows the feedback unit to provide optimal advice by referring to past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past feedback history into a generating AI and have the generating AI perform the task of providing optimal advice.

[0089] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can reduce the frequency of feedback to allow the user to concentrate on driving. For example, if the user is relaxed, the feedback unit can increase the frequency of feedback and provide more detailed information. The feedback unit can also prioritize providing only important feedback if the user is in a hurry. This allows the user to concentrate on driving by adjusting the timing of feedback according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the timing of feedback.

[0090] The feedback unit can provide advice by taking external environmental data (weather, traffic conditions, etc.) into consideration when providing feedback. For example, the feedback unit can provide advice on driving behavior in rainy weather. For example, the feedback unit can provide advice on driving behavior in traffic congestion. The feedback unit can also provide advice on driving behavior at night. By taking external environmental data into consideration, appropriate advice can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input external environmental data into a generating AI and have the generating AI perform the task of providing advice.

[0091] The feedback unit can provide advice when providing feedback, taking into account the usage of the vehicle's entertainment system. For example, the feedback unit can provide advice regarding driving behavior while music is playing. For example, the feedback unit can provide advice regarding driving behavior while watching videos. The feedback unit can also provide advice regarding driving behavior while playing games. This allows for the provision of appropriate advice by taking into account the usage of the entertainment system. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input entertainment system usage data into a generating AI and have the generating AI perform the task of providing advice.

[0092] The service provider can estimate the user's emotions and adjust the timing of Wi-Fi provision based on the estimated emotions. For example, if the user is stressed, the service provider can reduce the frequency of Wi-Fi provision to allow the user to concentrate on driving. For example, if the user is relaxed, the service provider can increase the frequency of Wi-Fi provision to enhance the internet environment. Furthermore, if the user is in a hurry, the service provider can prioritize providing only important data. In this way, by adjusting the timing of Wi-Fi provision according to the user's emotions, the service provider can allow the user to concentrate on driving. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the timing of Wi-Fi provision.

[0093] The service provider can monitor the user's data usage when providing Wi-Fi and propose the optimal data plan. For example, if the user is likely to exceed their data usage limit, the service provider can propose the optimal data plan. For example, if the user wants to conserve data usage, the service provider can propose a low-data-usage plan. Furthermore, if the user uses a large amount of data, the service provider can propose an unlimited data plan. In this way, the service provider can propose the optimal data plan by monitoring the user's data usage. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's data usage into a generating AI and have the generating AI propose the optimal data plan.

[0094] The service provider can dynamically change the priority of data usage according to the vehicle's operating status when providing Wi-Fi. For example, when driving on a highway, the service provider can set a high priority for data usage to provide a stable internet connection. For example, when driving in an urban area, the service provider can set a low priority for data usage to reduce the burden on the driver. In addition, during traffic congestion, the service provider can set a high priority for data usage to provide entertainment services. In this way, a stable internet connection can be provided by changing the priority of data usage according to the operating status. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input vehicle operating status data into a generating AI and have the generating AI execute the change in data usage priority.

[0095] The service provider can estimate the user's emotions and determine the priority of Wi-Fi provision based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize providing only important data. For example, if the user is relaxed, the service provider can prioritize providing detailed data. Also, if the user is in a hurry, the service provider can prioritize providing data related to driving behavior. In this way, by determining the priority of Wi-Fi provision according to the user's emotions, important data can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the determination of Wi-Fi provision priorities.

[0096] The service provider can propose an optimal data plan when providing Wi-Fi, taking into account external environmental data (weather, traffic conditions, etc.). For example, during rainy weather, the service provider can propose a data plan that provides a stable internet connection. During traffic congestion, for example, the service provider can propose a data plan that provides entertainment services. Furthermore, the service provider can propose a low-data usage plan at night. In this way, the service provider can propose an optimal data plan by taking external environmental data into consideration. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input external environmental data into a generating AI and have the generating AI propose an optimal data plan.

[0097] The service provider can propose an optimal data plan when providing Wi-Fi, taking into account the usage of the vehicle's entertainment system. For example, the service provider can propose a low-data usage plan when music is being played. For example, the service provider can propose a data plan that provides a stable internet connection when watching videos. The service provider can also propose a high-speed data plan when playing games. In this way, the service provider can propose an optimal data plan by taking into account the usage of the entertainment system. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input entertainment system usage data into a generating AI and have the generating AI propose an optimal data plan.

[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 analysis unit can consider the user's health data when analyzing their driving behavior. For example, it can collect data on the user's heart rate and blood pressure and analyze its relationship to driving behavior. This allows it to clarify the impact of health status on driving behavior and provide driving advice tailored to the user's health status. It can also issue alerts to encourage users to take breaks if they are fatigued. Furthermore, it can offer suggestions to avoid long hours of driving based on health data.

[0100] The feedback system can provide training programs to improve driving skills based on the user's driving behavior. For example, it can suggest training using a driving simulator to help users acquire safe driving techniques. It can also provide online courses to improve driving skills, allowing users to study at home. Furthermore, it can introduce workshops and seminars to improve driving skills, providing opportunities to receive training in a real driving environment.

[0101] The system can optimize vehicle maintenance schedules based on user driving behavior data. For example, it can analyze driving behavior data to predict tire wear and oil change timing. This allows for maintenance to be performed at the appropriate time, maintaining vehicle performance. It can also notify users of maintenance needs and encourage them to make reservations. Furthermore, it can manage maintenance history and use it as a reference for future maintenance planning.

[0102] The data collection unit can suggest eco-driving practices based on vehicle operation data. For example, it can suggest fuel-efficient driving methods to help users save fuel. It can also evaluate the effectiveness of eco-driving practices and provide feedback to users. Furthermore, it can visualize the environmental contribution of eco-driving practices, thereby increasing user motivation. This encourages users to drive in an environmentally friendly manner.

[0103] The analysis unit can evaluate driving risks based on user driving behavior data. For example, it can analyze the frequency of sudden braking and acceleration to identify high-risk driving behaviors. This allows the system to suggest risk reduction methods to the user. Furthermore, the results of the driving risk evaluation can be provided to insurance companies for use as a reference for obtaining premium discounts. In addition, it is possible to propose training programs to improve driving skills based on the driving risk evaluation results.

[0104] The feedback unit can estimate the user's emotions and suggest music and entertainment for driving based on those estimates. For example, if the user is feeling stressed, it can suggest relaxing music. If the user is relaxed, it can suggest enjoyable music or podcasts. Furthermore, if the user is tired, it can suggest uplifting music. By providing entertainment tailored to the user's emotions, this system can improve driving comfort.

[0105] The system can estimate the user's emotions and suggest appropriate break times while driving based on those emotions. For example, if the user is feeling stressed, it can suggest taking a break earlier. If the user is relaxed, it can suggest continuing to drive for a longer period. Furthermore, if the user is tired, it can issue an alert prompting them to take a break. By suggesting break times that match the user's emotions, it can support safe driving.

[0106] The analysis unit can estimate the user's emotions and customize the analysis results of driving behavior based on those estimated emotions. For example, if the user is stressed, it can provide concise and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that highlight only the important points. In this way, by providing analysis results that match the user's emotions, it can support improvements in driving behavior.

[0107] The feedback unit can estimate the user's emotions and adjust the content of the driving feedback based on those emotions. For example, if the user is stressed, it can provide more positive feedback. If the user is relaxed, it can provide feedback that includes detailed areas for improvement. Furthermore, if the user is in a hurry, it can provide concise and to-the-point feedback. In this way, by providing feedback that matches the user's emotions, it can support improvements in driving behavior.

[0108] The system can estimate the user's emotions and adjust the in-driving entertainment content based on those emotions. For example, if the user is stressed, it can provide relaxing movies or music. If the user is relaxed, it can provide enjoyable movies or music. Furthermore, if the user is tired, it can provide uplifting movies or music. By providing entertainment content tailored to the user's emotions, the system can improve comfort while driving.

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

[0110] Step 1: The data collection unit collects data from sensors mounted on the vehicle. The data collection unit collects data such as the vehicle's speed, position, acceleration, and brake usage. For example, the data collection unit can collect data if the vehicle applies the brakes suddenly. The data collection unit can also monitor the vehicle's speed in real time and collect data on speeding. For example, the data collection unit can collect vehicle position data using GPS. Step 2: The analysis unit analyzes the data collected by the collection unit and analyzes driving behavior. For example, the analysis unit analyzes driving behavior based on the collected data and identifies individual driving patterns. For example, the analysis unit can analyze the frequency of sudden braking or the tendency to exceed the speed limit. The analysis unit can also reveal characteristics of driving behavior based on the collected data. For example, the analysis unit can identify characteristics of driving behavior and provide advice to promote safe driving. Step 3: The feedback unit provides real-time feedback based on the analysis results obtained by the analysis unit. The feedback unit provides, for example, advice to encourage safe driving. The feedback unit can suggest, for example, driving methods to improve fuel efficiency. The feedback unit can also show the driver areas for improvement in driving behavior in real time. The feedback unit can provide, for example, a visual aid to show the driver areas for improvement in driving behavior. Step 4: The provider provides each user with Wi-Fi for installation in their car. The provider provides each user with Wi-Fi for installation in their car at a low cost. The provider can acquire data in a highly secure manner. The provider also enables users to enjoy an internet environment in their car and enjoy video streaming services, etc. The provider enables users to watch movies during long drives, for example.

[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, feedback unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects vehicle data using the camera 42 and sensors of the smart device 14 and acquires the data with the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze driving behavior. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14, and provides real-time feedback based on the analysis results. The provision unit provides, for example, Wi-Fi via the communication I / F 44 of the smart device 14, allowing the user to enjoy an internet environment in the vehicle. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[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, feedback unit, and 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 vehicle data using the camera 42 and sensors of the smart glasses 214 and acquires the data with the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze driving behavior. The feedback unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides real-time feedback based on the analysis results. The provision unit provides, for example, Wi-Fi via the communication I / F 44 of the smart glasses 214, allowing the user to enjoy an internet environment in the vehicle. 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, feedback unit, and 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 vehicle data using the camera 42 and sensors of the headset terminal 314 and acquires the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze driving behavior. The feedback unit is implemented in the control unit 46A of the headset terminal 314 and provides real-time feedback based on the analysis results. The provision unit provides Wi-Fi via the communication I / F 44 of the headset terminal 314, allowing the user to enjoy an internet environment in the vehicle. 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, feedback unit, and 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 vehicle data using the camera 42 and sensors of the robot 414 and acquires the data with the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to analyze driving behavior. The feedback unit is implemented, for example, by the control unit 46A of the robot 414, and provides real-time feedback based on the analysis results. The provision unit provides, for example, Wi-Fi via the communication I / F 44 of the robot 414, allowing the user to enjoy an internet environment in the vehicle. The correspondence between each unit and the devices and control units 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 data from sensors mounted on the vehicle, An analysis unit analyzes the data collected by the aforementioned collection unit and analyzes driving behavior, A feedback unit provides real-time feedback based on the analysis results obtained by the aforementioned analysis unit, It includes a provisioning unit that provides Wi-Fi for installation in the vehicle for each user. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as vehicle speed, position, acceleration, and brake usage. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, driving behavior is analyzed to identify individual driving patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is We provide advice to encourage safe driving and suggest driving methods to improve fuel efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide each user with a low-cost Wi-Fi device for installation in their car, and acquire data while maintaining high levels of confidentiality. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, To enable users to enjoy an internet environment inside their vehicles and to enjoy video streaming services, etc. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The frequency of data collection is dynamically changed according to the vehicle's operating status. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We collect vehicle maintenance information and analyze its correlation with driving behavior. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Collect external environmental data from vehicles and analyze its correlation with driving behavior. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We collect data on the use of in-vehicle entertainment systems and analyze their correlation with driving behavior. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing driving behavior, abnormal driving patterns can be identified by comparing them with past driving data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing driving behavior, we improve the accuracy of the analysis by taking into account vehicle maintenance information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user'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, When analyzing driving behavior, external environmental data is taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing driving behavior, we improve the accuracy of the analysis by taking into account the usage of the vehicle's entertainment system. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is Provide visual aids to specifically indicate areas for improvement in driving behavior during feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, we refer to past feedback history to offer the best possible advice. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, we offer advice that takes external environmental data into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, we offer advice that takes into account the usage of the vehicle's entertainment system. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of Wi-Fi provision based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing Wi-Fi, we monitor users' data usage and suggest the optimal data plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing Wi-Fi, the priority of data usage is dynamically changed according to the vehicle's operating status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates user sentiment and determines Wi-Fi provision priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing Wi-Fi, we propose the optimal data plan considering external environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing Wi-Fi, we will suggest the optimal data plan considering the usage of the vehicle's entertainment system. 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 data from sensors mounted on the vehicle, An analysis unit analyzes the data collected by the aforementioned collection unit and analyzes driving behavior, A feedback unit provides real-time feedback based on the analysis results obtained by the aforementioned analysis unit, It includes a provisioning unit that provides Wi-Fi for installation in the vehicle for each user. A system characterized by the following features.

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

3. The aforementioned analysis unit, Based on the collected data, driving behavior is analyzed to identify individual driving patterns. The system according to feature 1.

4. The aforementioned feedback unit is We provide advice to encourage safe driving and suggest driving methods to improve fuel efficiency. The system according to feature 1.

5. The aforementioned supply unit is, We provide each user with a low-cost Wi-Fi device for installation in their car, and acquire data while maintaining high levels of confidentiality. The system according to feature 1.

6. The aforementioned supply unit is, To enable users to enjoy an internet environment inside their vehicles and to enjoy video streaming services, etc. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The frequency of data collection is dynamically changed according to the vehicle's operating status. The system according to feature 1.

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