system

Abstract

The system according to this embodiment aims to analyze driving data and provide customized advice. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a dialogue unit, and a learning unit. The collection unit collects driving data. The analysis unit analyzes the driving data collected by the collection unit. The provision unit provides customized advice based on the analysis results obtained by the analysis unit. The dialogue unit engages in interactive dialogue based on the advice provided by the provision unit. The learning unit learns the user's driving patterns based on the information obtained by the dialogue unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to utilize driving data to provide individual feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze driving data and provide customized advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a dialogue unit, and a learning unit. The data collection unit collects driving data. The analysis unit analyzes the driving data collected by the data collection unit. The data provision unit provides customized advice based on the analysis results obtained by the analysis unit. The dialogue unit engages in interactive dialogue based on the advice provided by the data provision unit. The learning unit learns the user's driving patterns based on the information obtained by the dialogue unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze driving data and provide customized advice. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 driver assistance system according to an embodiment of the present invention is a system that uses a generating AI to analyze driving data and provides hints for safe driving and personalized feedback. This driver assistance system supports the improvement of the user's driving skills and fuel efficiency. Specifically, the driver assistance system first uses AI to collect and analyze driving data in real time. Next, based on the analysis results, it provides customized advice to the user. For example, it may offer specific hints to reduce the frequency of sudden braking and sudden acceleration, and suggest efficient driving methods. The driver assistance system also encourages behavioral changes for safe driving through interactive dialogue. This improves the user's driving skills, reduces the accident rate, and improves fuel efficiency. Furthermore, the driver assistance system uses an evolving algorithm in which the AI ​​learns the user's driving patterns to provide more accurate feedback. This system is beneficial for new drivers, drivers who want to gain experience, car owners who want to improve fuel efficiency and reduce costs, and all drivers who want to enhance safety in their daily driving. For example, if the driver assistance system brakes suddenly while driving, it will provide specific advice to reduce the frequency of such braking. Also, if the driver assistance system accelerates suddenly, it will suggest efficient driving methods to reduce the frequency of such acceleration. Furthermore, the driver assistance system encourages users to change their behavior for safer driving through interactive dialogue. For example, the driver assistance system points out points that the user should pay attention to while driving and suggests specific ways to improve. This improves the user's driving skills, reduces accident rates, and improves fuel efficiency. The driver assistance system uses AI to learn the user's driving patterns and evolves algorithms to provide more accurate feedback. For example, the driver assistance system provides personalized feedback based on the user's driving history. This allows the user to receive specific advice on how to improve their driving skills. In this way, the driver assistance system can support the improvement of the user's driving skills and fuel efficiency.

[0029] The driver assistance system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a dialogue unit, and a learning unit. The data collection unit collects driving data. Driving data includes, but is not limited to, speed, acceleration, and frequency of brake use. The data collection unit collects driving data using, for example, sensors mounted on the vehicle. The data collection unit can also acquire driving data from the vehicle's ECU (Engine Control Unit). Furthermore, the data collection unit can also collect driving data using GPS data. For example, the data collection unit collects vehicle speed and location information in real time. The analysis unit analyzes the driving data collected by the data collection unit. The analysis unit analyzes the driving data to identify the frequency of sudden braking and sudden acceleration. The analysis unit can perform driving data analysis using a generation AI. For example, the analysis unit inputs driving data into a generation AI to identify the frequency of sudden braking and sudden acceleration. The data provision unit provides customized advice based on the analysis results obtained by the analysis unit. The data provision unit provides, for example, specific hints to reduce the frequency of sudden braking and sudden acceleration. The provisioning unit can generate customized advice using generative AI. For example, the provisioning unit inputs analysis results into the generative AI to generate specific hints. The dialogue unit engages in interactive dialogue based on the advice provided by the provisioning unit. The dialogue unit, for example, encourages the user to change their behavior for safer driving. The dialogue unit can engage in interactive dialogue using generative AI. For example, the dialogue unit inputs advice into the generative AI to generate a dialogue with the user. The learning unit learns the user's driving patterns based on the information obtained by the dialogue unit. The learning unit, for example, analyzes the user's driving history to identify driving patterns. The learning unit can learn driving patterns using generative AI. For example, the learning unit inputs driving history into the generative AI to identify driving patterns. As a result, the driving assistance system according to this embodiment can support the improvement of the user's driving skills and fuel efficiency.

[0030] The data collection unit collects driving data. This driving data includes, but is not limited to, speed, acceleration, and brake usage frequency. The data collection unit collects driving data using, for example, sensors mounted on the vehicle. Specifically, the vehicle's speed sensor measures the vehicle's current speed in real time, and the acceleration sensor detects the degree of acceleration and deceleration. The brake sensor records the frequency and intensity of brake pedal usage. The data obtained from these sensors is transmitted to the vehicle's ECU (Engine Control Unit), which centrally manages this data. The data collection unit can also acquire driving data from the vehicle's ECU. The ECU manages detailed data such as engine operating status, fuel consumption, and engine speed, and this data is also acquired by the data collection unit. Furthermore, the data collection unit can also collect driving data using GPS data. GPS data provides real-time vehicle location information and travel route, allowing for a detailed understanding of the driver's driving patterns and travel routes. For example, the data collection unit collects vehicle speed and location information in real time. This allows the data collection unit to gain a detailed understanding of the driver's driving behavior and the vehicle's operating status, and to provide necessary data to other departments within the driver assistance system. 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 provision departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes driving data collected by the data collection department. For example, the analysis department analyzes driving data to identify the frequency of sudden braking and sudden acceleration. Specifically, it analyzes collected speed and acceleration data to detect sudden changes in speed and fluctuations in acceleration. This allows the analysis department to understand the characteristics of the driver's driving style and behavior. The analysis department can also use generative AI to analyze driving data. Generative AI learns from large amounts of driving data and builds models to identify patterns in driving behavior. For example, the analysis department inputs driving data into the generative AI to identify the frequency of sudden braking and sudden acceleration. The generative AI can detect patterns of sudden braking and sudden acceleration from the driving data and identify their frequency and the conditions under which they occur. Furthermore, based on the results of the driving data analysis, the generative AI can analyze the characteristics of the driver's driving style and behavior in detail. This allows the analysis department to understand the driver's driving behavior in detail and provide necessary information to other departments of the driver assistance system. In addition, the analysis department can also use historical data and statistical information to analyze long-term trends and patterns of driving behavior. For example, based on past driving data, the system analyzes trends in changes and improvements in the driving behavior of specific drivers, providing data for future driving assistance. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual driving behavior or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp driving behavior in real time but also to analyze long-term driving behavior and detect anomalies, thereby improving the overall reliability and safety of the system.

[0032] The service provider provides customized advice based on the analysis results obtained by the analysis department. For example, the service provider provides specific hints to reduce the frequency of sudden braking and acceleration. Specifically, it provides specific advice on improving driving style and safe driving based on the characteristics of the driver's driving behavior. The service provider can generate customized advice using a generative AI. The generative AI identifies areas for improvement in the driver's driving behavior and generates specific advice based on the analysis results of the driving data provided by the analysis department. For example, the service provider inputs the analysis results into the generative AI and generates specific hints. The generative AI can analyze the characteristics of the driver's driving behavior and provide specific advice to reduce the frequency of sudden braking and acceleration. This allows the service provider to provide drivers with specific and practical advice, supporting the improvement of driving skills and the promotion of safe driving. Furthermore, the service provider can collect driver feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can review and improve the advice based on driver feedback to provide more effective advice. In addition, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide advice to drivers, supporting the improvement of driving skills and the promotion of safe driving.

[0033] The dialogue unit engages in interactive conversations based on advice provided by the service provider. For example, the dialogue unit encourages users to change their behavior for safer driving. Specifically, it explains areas for improvement in driving behavior and the importance of safe driving to drivers, engaging in conversations to deepen their understanding. The dialogue unit can conduct interactive conversations using generative AI. The generative AI generates conversations with drivers based on advice provided by the service provider. For example, the dialogue unit inputs advice into the generative AI to generate a conversation with the user. The generative AI can provide specific advice and explanations to drivers based on the characteristics and areas for improvement of their driving behavior. This allows the dialogue unit to provide drivers with specific and practical advice, supporting the improvement of driving skills and the promotion of safer driving. Furthermore, the dialogue unit can collect driver feedback and continuously improve the accuracy and effectiveness of the conversation content. For example, it can review and improve the conversation content based on driver feedback to provide more effective conversations. The dialogue unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the dialogue unit to provide drivers with quick and reliable advice, supporting improvements in driving skills and promoting safe driving.

[0034] The learning unit learns the user's driving patterns based on information obtained by the dialogue unit. For example, the learning unit analyzes the user's driving history to identify driving patterns. Specifically, it analyzes the characteristics and style of the driver's driving behavior in detail to identify the driver's driving patterns. The learning unit can learn driving patterns using generative AI. Generative AI learns from a large amount of driving data and builds a model to identify patterns of driving behavior. For example, the learning unit inputs driving history into the generative AI to identify driving patterns. The generative AI can analyze the characteristics and style of the driver's driving behavior to identify the driver's driving patterns. This allows the learning unit to understand the driver's driving behavior in detail and provide necessary information to other departments of the driver assistance system. Furthermore, the learning unit can also analyze long-term trends and patterns of driving behavior by utilizing past data and statistical information. For example, based on past driving data, it can analyze trends in changes and improvements in the driving behavior of a specific driver and provide data for future driver assistance. In addition, the learning unit can use anomaly detection algorithms to detect unusual driving behavior or abnormal data and issue warnings early. This allows the learning unit to not only grasp driving behavior in real time, but also to analyze long-term driving behavior and detect anomalies, thereby improving the reliability and safety of the entire system.

[0035] The data collection unit can collect driving data in real time. For example, the data collection unit collects driving data in real time using sensors mounted on the vehicle. For example, the data collection unit collects vehicle speed and location information in real time. The data collection unit can also acquire driving data in real time from the vehicle's ECU (Engine Control Unit). For example, the data collection unit collects vehicle engine speed and fuel consumption in real time. Furthermore, the data collection unit can also collect driving data in real time using GPS data. For example, the data collection unit collects vehicle location information in real time. This allows for analysis that reflects the latest driving conditions by collecting driving data in real time. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input data acquired from sensors mounted on the vehicle into a generation AI and have the generation AI perform real-time data collection.

[0036] The analysis unit can analyze collected driving data and identify the frequency of sudden braking and sudden acceleration. For example, the analysis unit can analyze driving data to identify the frequency of sudden braking. For example, the analysis unit can identify the frequency of sudden braking based on the braking force and the rate of speed reduction. The analysis unit can also analyze driving data to identify the frequency of sudden acceleration. For example, the analysis unit can identify the frequency of sudden acceleration based on the magnitude of acceleration and the change in speed per unit of time. Furthermore, the analysis unit can analyze driving data to comprehensively identify the frequency of sudden braking and sudden acceleration. For example, the analysis unit can identify the frequency of sudden braking and sudden acceleration by combining the braking force and the magnitude of acceleration. By identifying the frequency of sudden braking and sudden acceleration, specific areas for improvement for safe driving can be identified. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input collected driving data into a generative AI and have the generative AI perform the identification of the frequency of sudden braking and sudden acceleration.

[0037] The service provider can provide specific hints to reduce the frequency of sudden braking and sudden acceleration based on the analysis results. For example, the service provider can provide specific hints to reduce the frequency of sudden braking. For example, the service provider can suggest ways to adjust the timing and intensity of braking. The service provider can also provide specific hints to reduce the frequency of sudden acceleration. For example, the service provider can suggest ways to adjust how the accelerator is pressed and how the speed is increased. Furthermore, the service provider can also provide comprehensive hints to reduce the frequency of sudden braking and sudden acceleration. For example, the service provider can suggest ways to adjust the rhythm and timing of driving. By providing specific hints, it becomes easier for users to practice safe driving. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the analysis results into a generative AI and have the generative AI generate specific hints.

[0038] The dialogue unit can encourage behavioral changes for safe driving through interactive dialogue. For example, the dialogue unit can encourage users to change their behavior for safe driving. For example, the dialogue unit can point out points that users should pay attention to while driving and suggest specific ways to improve. The dialogue unit can also engage in dialogue with users to reflect on their driving behavior and find areas for improvement. For example, the dialogue unit can point out the frequency of sudden braking or acceleration while driving and suggest ways to improve. Furthermore, the dialogue unit can provide users with specific action plans to change their driving behavior. For example, the dialogue unit can provide users with step-by-step guidance on how to change their driving behavior. In this way, users can naturally acquire behaviors for safe driving through interactive dialogue. Some or all of the above processing in the dialogue unit may be performed using generative AI or not. For example, the dialogue unit can input the provided advice into generative AI and have the generative AI perform the generation of interactive dialogue.

[0039] The learning unit can learn the user's driving patterns and provide more accurate feedback using an evolving algorithm. For example, the learning unit can analyze the user's driving history and identify driving patterns. For example, the learning unit can identify the frequency of sudden braking and sudden acceleration from the user's driving history and learn those patterns. The learning unit can also analyze the user's driving style and identify driving patterns. For example, the learning unit can identify driving patterns based on the user's driving style. Furthermore, the learning unit can combine the user's driving history and driving style to comprehensively identify driving patterns. For example, the learning unit can identify driving patterns based on the user's driving history and driving style. This allows the learning unit to always provide the user with the latest feedback by using an evolving algorithm. Some or all of the above processing in the learning unit may be performed using generative AI, or it may be performed without using generative AI. For example, the learning unit can input the user's driving history into a generative AI and have the generative AI perform the learning of driving patterns.

[0040] The data collection unit can analyze the user's past driving history and select the optimal data collection method. For example, the data collection unit can focus on collecting data in locations where the user frequently applied sudden brakes in the past. For example, the data collection unit can input the user's driving history into a generating AI to identify locations with a high frequency of sudden braking. The data collection unit can also concentrate data collection during specific time periods based on the user's past driving history. For example, the data collection unit can collect data during specific time periods based on the user's driving history. Furthermore, the data collection unit can customize the data collection method according to the user's driving style. For example, the data collection unit can analyze the user's driving style and select the optimal data collection method. This allows the data collection unit to provide the user with the most suitable data collection method by analyzing past driving history. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input the user's driving history into the generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter driving data based on the user's current driving situation and the vehicle's condition. For example, if the user is driving on a highway, the data collection unit will collect highway-specific data. For example, the data collection unit will input the user's driving situation into the generating AI and collect highway-related data. The data collection unit can also prioritize the collection of data related to the user's vehicle's condition if it requires maintenance. For example, the data collection unit will input the vehicle's condition into the generating AI and collect maintenance-related data. Furthermore, if the user is driving in rainy weather, the data collection unit can focus on collecting driving data for rainy weather. For example, the data collection unit will input weather information into the generating AI and collect driving data for rainy weather. This allows for efficient collection of necessary data by filtering the data based on the current driving situation and vehicle condition. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input the driving situation and vehicle condition into the generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting driving data. For example, if the user is driving in an urban area, the data collection unit can collect urban-specific driving data. For example, the data collection unit can input the user's geographical location information into the generating AI and collect data related to urban areas. The data collection unit can also collect mountain-specific driving data if the user is driving in a mountainous area. For example, the data collection unit can collect data related to mountainous areas based on the user's geographical location information. Furthermore, if the user frequently drives in a particular region, the data collection unit can prioritize the collection of data related to that region. For example, the data collection unit can input the user's driving history into the generating AI and collect data related to that particular region. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input the user's geographical location information into the generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting driving data. For example, if the user mentions a specific driving style on social media, the data collection unit can collect data related to that style. For example, the data collection unit inputs the user's social media activity into a generating AI and collects data related to that specific driving style. The data collection unit can also collect data related to specific road conditions if the user mentions those conditions on social media. For example, the data collection unit collects data related to specific road conditions based on the user's social media activity. Furthermore, if the user mentions a specific vehicle function on social media, the data collection unit can collect data related to that function. For example, the data collection unit inputs the user's social media activity into a generating AI and collects data related to that specific vehicle function. This makes it possible to collect data based on the user's interests by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using the generating AI or not. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of the driving data. For example, the analysis unit can perform a detailed analysis on important driving data. For example, the analysis unit can input the importance of the driving data into the generating AI and perform a detailed analysis. The analysis unit can also perform a concise analysis on general driving data. For example, the analysis unit can perform a concise analysis based on the importance of the driving data. Furthermore, the analysis unit can perform an analysis of specific driving data at a moderate level of detail. For example, the analysis unit can perform an analysis at a moderate level of detail based on the importance of the driving data. By adjusting the level of detail of the analysis based on the importance of the driving data, the necessary information can be provided efficiently. Some or all of the above processing in the analysis unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the analysis unit can input the importance of the driving data into the generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of driving data during analysis. For example, the analysis unit can apply a specific algorithm to data related to sudden braking. For instance, the analysis unit inputs sudden braking data into the generating AI and applies a specific algorithm. The analysis unit can also apply a different algorithm to data related to sudden acceleration. For example, the analysis unit inputs sudden acceleration data into the generating AI and applies a different algorithm. Furthermore, the analysis unit can apply a different algorithm to data related to fuel efficiency. For example, the analysis unit inputs fuel efficiency data into the generating AI and applies a different algorithm. This allows for the application of an appropriate analysis algorithm according to the category of driving data, thereby providing highly accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without using the generating AI. For example, the analysis unit can input the categories of driving data into the generating AI and have the generating AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the timing of data collection during the analysis process. For example, the analysis unit can prioritize the analysis of recently collected data. For instance, the analysis unit can input the data collection timing into the generating AI and prioritize its analysis. The analysis unit can also postpone the analysis of past data. For example, the analysis unit can postpone its analysis based on the data collection timing. Furthermore, the analysis unit can set priorities for analysis of data collected during specific periods. For example, the analysis unit can set priorities based on the data collection timing and perform the analysis accordingly. This allows for the prioritization of the most recent data by determining the analysis priority based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may not be performed using the generating AI. For example, the analysis unit can input the data collection timing into the generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the driving data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant driving data. For example, the analysis unit can input the relevance of the driving data into the generating AI and prioritize its analysis. The analysis unit can also postpone the analysis of less relevant driving data. For example, the analysis unit can postpone the analysis of driving data based on its relevance. Furthermore, the analysis unit can set the order of analysis for specific driving data based on its relevance. For example, the analysis unit can set the order of analysis based on the relevance of the driving data. This allows for the prioritization of important data by adjusting the order of analysis based on the relevance of the driving data. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the relevance of the driving data into the generating AI and have the generating AI adjust the order of analysis.

[0048] The service provider can adjust the level of detail of the advice based on the importance of the driving data at the time of delivery. For example, the service provider can provide detailed advice for important driving data. For example, the service provider can input the importance of the driving data into the generating AI and provide detailed advice. The service provider can also provide concise advice for general driving data. For example, the service provider can provide concise advice based on the importance of the driving data. Furthermore, the service provider can provide advice with a moderate level of detail for specific driving data. For example, the service provider can provide advice with a moderate level of detail based on the importance of the driving data. This allows for the efficient provision of necessary information by adjusting the level of detail of the advice based on the importance of the driving data. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider can input the importance of the driving data into the generating AI and have the generating AI adjust the level of detail of the advice.

[0049] The service provider can apply different advice algorithms depending on the category of driving data at the time of service provision. For example, the service provider can apply a specific advice algorithm to data related to sudden braking. For example, the service provider can input sudden braking data into the generating AI and apply a specific advice algorithm. The service provider can also apply a different advice algorithm to data related to sudden acceleration. For example, the service provider can input sudden acceleration data into the generating AI and apply a different advice algorithm. Furthermore, the service provider can apply a different advice algorithm to data related to fuel efficiency. For example, the service provider can input fuel efficiency data into the generating AI and apply a different advice algorithm. This allows for the provision of highly accurate advice by applying the appropriate advice algorithm according to the category of driving data. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider can input the categories of driving data into the generating AI and have the generating AI perform the application of different advice algorithms.

[0050] The service provider can determine the priority of advice based on the timing of data collection at the time of service provision. For example, the service provider can prioritize advice for recently collected data. For example, the service provider can input the data collection timing into the generating AI and provide advice preferentially. The service provider can also postpone advice for past data. For example, the service provider can postpone advice based on the data collection timing. Furthermore, the service provider can set priorities for advice for data collected during a specific period. For example, the service provider can set priorities for advice based on the data collection timing. This allows for prioritizing advice based on the data collection timing, ensuring that the most recent data is prioritized. Some or all of the above processing in the service provider may be performed using the generating AI, or not. For example, the service provider can input the data collection timing into the generating AI and have the generating AI determine the priority of advice.

[0051] The service provider can adjust the order of advice based on the relevance of the driving data at the time of delivery. For example, the service provider can prioritize providing advice to highly relevant driving data. For example, the service provider can input the relevance of the driving data into the generating AI and prioritize providing advice. The service provider can also postpone providing advice to less relevant driving data. For example, the service provider can postpone providing advice based on the relevance of the driving data. Furthermore, the service provider can set the order of advice based on the relevance of specific driving data. For example, the service provider can set the order of advice based on the relevance of the driving data. In this way, by adjusting the order of advice based on the relevance of the driving data, important data can be prioritized for advice. Some or all of the above processing in the service provider may be performed using the generating AI or not using the generating AI. For example, the service provider can input the relevance of the driving data into the generating AI and have the generating AI perform the adjustment of the order of advice.

[0052] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's preferred dialogue style in the past. For example, the dialogue unit can input the user's past dialogue history into a generating AI and select the optimal dialogue method. The dialogue unit can also provide dialogue content tailored to the user's preferences based on the user's past dialogue history. For example, the dialogue unit can provide dialogue content tailored to the user's preferences based on the user's past dialogue history. Furthermore, the dialogue unit can adjust the timing of the conversation by referring to when the user previously interrupted a conversation. For example, the dialogue unit adjusts the timing of the conversation based on the user's past dialogue history. In this way, by referring to past dialogue history, the dialogue unit can provide the user with the most optimal dialogue method. Some or all of the above processing in the dialogue unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI and have the generating AI select the optimal dialogue method.

[0053] The dialogue unit can customize the dialogue content based on the user's current driving situation during a conversation. For example, if the user is driving on a highway, the dialogue unit will provide dialogue content related to highways. For example, the dialogue unit inputs the user's driving situation into the generating AI and provides dialogue content related to highways. The dialogue unit can also provide dialogue content related to urban areas if the user is driving in an urban area. For example, the dialogue unit provides dialogue content related to urban areas based on the user's driving situation. Furthermore, if the user is parked, the dialogue unit can provide dialogue content related to parking. For example, the dialogue unit provides dialogue content related to parking based on the user's driving situation. In this way, by customizing the dialogue content based on the current driving situation, it is possible to provide a dialogue that is appropriate for the user. Some or all of the above processing in the dialogue unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the dialogue unit can input the user's driving situation into the generating AI and have the generating AI perform the customization of the dialogue content.

[0054] The dialogue unit can provide optimal dialogue content by considering the user's geographical location information during a conversation. For example, if the user is driving in a specific area, the dialogue unit can provide dialogue content related to that area. For example, the dialogue unit can input the user's geographical location information into a generating AI and provide dialogue content related to that area. The dialogue unit can also provide tourist information if the user is driving in a tourist area. For example, the dialogue unit can provide tourist information based on the user's geographical location information. Furthermore, if the user is driving near their home, the dialogue unit can provide dialogue content related to their home. For example, the dialogue unit can provide dialogue content related to their home based on the user's geographical location information. In this way, by considering geographical location information, the dialogue unit can provide dialogue content that is highly relevant to the user. Some or all of the above processing in the dialogue unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the dialogue unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal dialogue content.

[0055] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, if the user mentions a specific topic on social media, the dialogue unit can provide dialogue content related to that topic. For example, the dialogue unit can input the user's social media activity into a generative AI and provide dialogue content related to that specific topic. The dialogue unit can also provide dialogue content related to a specific event if the user mentions that event on social media. For example, the dialogue unit can provide dialogue content related to a specific event based on the user's social media activity. Furthermore, if the user mentions a specific place on social media, the dialogue unit can provide dialogue content related to that place. For example, the dialogue unit can input the user's social media activity into a generative AI and provide dialogue content related to that place. In this way, by analyzing social media activity, it is possible to provide dialogue content based on the user's interests. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the dialogue unit can input the user's social media activity into a generative AI and have the generative AI suggest dialogue content.

[0056] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize the learning algorithm based on the user's past learning data. For example, the learning unit can input past learning data into a generating AI and optimize the learning algorithm. The learning unit can also select the optimal learning method from the user's past learning history. For example, the learning unit selects the optimal learning method based on past learning history. Furthermore, the learning unit can adjust the learning algorithm by referring to the user's past learning results. For example, the learning unit can input past learning results into a generating AI and adjust the learning algorithm. This allows for the optimization of the learning algorithm by referring to past learning data, thereby achieving highly accurate learning. Some or all of the above processes in the learning unit may be performed using a generating AI, or they may be performed without using a generating AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0057] The learning unit can customize the learning content based on the user's driving patterns during the learning process. For example, the learning unit can analyze the user's driving patterns and provide optimal learning content. For example, the learning unit can input the user's driving patterns into the generating AI and provide optimal learning content. The learning unit can also customize the learning content according to the user's driving style. For example, the learning unit can customize the learning content based on the user's driving style. Furthermore, the learning unit can adjust the learning content based on the user's driving history. For example, the learning unit can input the user's driving history into the generating AI and adjust the learning content. This allows the learning unit to provide optimal learning content for the user by customizing the learning content based on the user's driving patterns. Some or all of the above processes in the learning unit may be performed using the generating AI, or they may be performed without using the generating AI. For example, the learning unit can input the user's driving patterns into the generating AI and have the generating AI perform the customization of the learning content.

[0058] The learning unit can weight the training data based on the timing of data collection during training. For example, the learning unit can weight recently collected training data. For example, the learning unit can input the data collection timing into the generating AI and perform the weighting. The learning unit can also lightly weight past training data. For example, the learning unit can lightly weight the data based on the data collection timing. Furthermore, the learning unit can adjust the weighting for training data collected during a specific period. For example, the learning unit can adjust the weighting based on the data collection timing. This allows for training that prioritizes the latest data by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the learning unit can input the data collection timing into the generating AI and have the generating AI perform the weighting of the training data.

[0059] The learning unit can optimize the learning content by considering the user's geographical location information during the learning process. For example, if the user is driving in a specific area, the learning unit can provide learning content relevant to that area. For example, the learning unit can input the user's geographical location information into the generating AI and provide learning content relevant to that area. The learning unit can also provide learning content related to tourist information if the user is driving in a tourist area. For example, the learning unit can provide learning content related to tourist information based on the user's geographical location information. Furthermore, if the user is driving near their home, the learning unit can provide learning content related to their home. For example, the learning unit can provide learning content related to their home based on the user's geographical location information. In this way, by considering geographical location information, the learning unit can provide learning content that is highly relevant to the user. Some or all of the above processing in the learning unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the learning unit can input the user's geographical location information into the generating AI and have the generating AI perform the optimization of the learning content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The system can suggest music and podcasts to listen to while driving based on the user's driving data. For example, if the user frequently brakes suddenly, the system can suggest relaxing music. It can also suggest podcasts to help the user maintain concentration during long-distance drives. Furthermore, the system can suggest music to enhance the user's mood while driving, depending on their driving style. This can improve the user's driving experience.

[0062] The data collection unit can automatically adjust the cabin temperature while the vehicle is in motion based on the user's driving data. For example, if the user frequently accelerates rapidly, the unit will lower the cabin temperature to enhance the cooling effect. Furthermore, if the user drives for extended periods, the unit can adjust the cabin temperature to maintain a comfortable temperature. In addition, the unit can optimize the cabin temperature according to the user's driving style. This improves the user's comfort while driving.

[0063] The analysis unit can estimate the user's fatigue level while driving based on their driving data and suggest when to take a break. For example, if the user frequently brakes or accelerates suddenly, the analysis unit will estimate that the user is fatigued and suggest a break. Furthermore, if the user drives for extended periods, the analysis unit can suggest breaks at regular intervals. In addition, the analysis unit can suggest the optimal break timing based on the user's driving style. This can improve the user's safety while driving.

[0064] The system can provide eco-driving advice based on the user's driving data. For example, if the user frequently accelerates suddenly, the system can provide advice to improve fuel efficiency. Similarly, if the user frequently brakes suddenly, the system can provide advice to improve braking technique. Furthermore, the system can provide comprehensive eco-driving advice tailored to the user's driving style. This can improve the user's fuel efficiency and reduce their environmental impact.

[0065] The interactive unit can suggest relaxation methods to reduce stress while driving, based on the user's driving data. For example, if the user frequently brakes or accelerates suddenly, the unit may suggest deep breathing or relaxing music. It can also suggest stretching or light exercise if the user drives for long periods. Furthermore, the unit can suggest comprehensive relaxation methods to reduce stress, tailored to the user's driving style. This helps reduce stress while driving and supports a more comfortable driving experience.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects driving data. This driving data includes speed, acceleration, and brake usage frequency. The data collection unit acquires driving data from sensors mounted on the vehicle and from the vehicle's ECU (Engine Control Unit). It can also collect vehicle speed and location information in real time using GPS data. Step 2: The analysis unit analyzes the driving data collected by the collection unit. For example, it analyzes the driving data to identify the frequency of sudden braking and sudden acceleration. It is also possible to analyze the driving data using generative AI. Step 3: The service provider provides customized advice based on the analysis results obtained by the analysis provider. For example, it provides specific tips for reducing the frequency of sudden braking and acceleration. It can also generate customized advice using generative AI. Step 4: The dialogue unit engages in an interactive conversation based on the advice provided by the delivery unit. For example, it encourages the user to change their behavior for safer driving. Interactive conversations can also be conducted using generative AI. Step 5: The learning unit learns the user's driving patterns based on the information obtained by the dialogue unit. For example, it analyzes the user's driving history to identify driving patterns. It is also possible to learn driving patterns using generative AI.

[0068] (Example of form 2) The driver assistance system according to an embodiment of the present invention is a system that uses a generating AI to analyze driving data and provides hints for safe driving and personalized feedback. This driver assistance system supports the improvement of the user's driving skills and fuel efficiency. Specifically, the driver assistance system first uses AI to collect and analyze driving data in real time. Next, based on the analysis results, it provides customized advice to the user. For example, it may offer specific hints to reduce the frequency of sudden braking and sudden acceleration, and suggest efficient driving methods. The driver assistance system also encourages behavioral changes for safe driving through interactive dialogue. This improves the user's driving skills, reduces the accident rate, and improves fuel efficiency. Furthermore, the driver assistance system uses an evolving algorithm in which the AI ​​learns the user's driving patterns to provide more accurate feedback. This system is beneficial for new drivers, drivers who want to gain experience, car owners who want to improve fuel efficiency and reduce costs, and all drivers who want to enhance safety in their daily driving. For example, if the driver assistance system brakes suddenly while driving, it will provide specific advice to reduce the frequency of such braking. Also, if the driver assistance system accelerates suddenly, it will suggest efficient driving methods to reduce the frequency of such acceleration. Furthermore, the driver assistance system encourages users to change their behavior for safer driving through interactive dialogue. For example, the driver assistance system points out points that the user should pay attention to while driving and suggests specific ways to improve. This improves the user's driving skills, reduces accident rates, and improves fuel efficiency. The driver assistance system uses AI to learn the user's driving patterns and evolves algorithms to provide more accurate feedback. For example, the driver assistance system provides personalized feedback based on the user's driving history. This allows the user to receive specific advice on how to improve their driving skills. In this way, the driver assistance system can support the improvement of the user's driving skills and fuel efficiency.

[0069] The driver assistance system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a dialogue unit, and a learning unit. The data collection unit collects driving data. Driving data includes, but is not limited to, speed, acceleration, and frequency of brake use. The data collection unit collects driving data using, for example, sensors mounted on the vehicle. The data collection unit can also acquire driving data from the vehicle's ECU (Engine Control Unit). Furthermore, the data collection unit can also collect driving data using GPS data. For example, the data collection unit collects vehicle speed and location information in real time. The analysis unit analyzes the driving data collected by the data collection unit. The analysis unit analyzes the driving data to identify the frequency of sudden braking and sudden acceleration. The analysis unit can perform driving data analysis using a generation AI. For example, the analysis unit inputs driving data into a generation AI to identify the frequency of sudden braking and sudden acceleration. The data provision unit provides customized advice based on the analysis results obtained by the analysis unit. The data provision unit provides, for example, specific hints to reduce the frequency of sudden braking and sudden acceleration. The provisioning unit can generate customized advice using generative AI. For example, the provisioning unit inputs analysis results into the generative AI to generate specific hints. The dialogue unit engages in interactive dialogue based on the advice provided by the provisioning unit. The dialogue unit, for example, encourages the user to change their behavior for safer driving. The dialogue unit can engage in interactive dialogue using generative AI. For example, the dialogue unit inputs advice into the generative AI to generate a dialogue with the user. The learning unit learns the user's driving patterns based on the information obtained by the dialogue unit. The learning unit, for example, analyzes the user's driving history to identify driving patterns. The learning unit can learn driving patterns using generative AI. For example, the learning unit inputs driving history into the generative AI to identify driving patterns. As a result, the driving assistance system according to this embodiment can support the improvement of the user's driving skills and fuel efficiency.

[0070] The data collection unit collects driving data. This driving data includes, but is not limited to, speed, acceleration, and brake usage frequency. The data collection unit collects driving data using, for example, sensors mounted on the vehicle. Specifically, the vehicle's speed sensor measures the vehicle's current speed in real time, and the acceleration sensor detects the degree of acceleration and deceleration. The brake sensor records the frequency and intensity of brake pedal usage. The data obtained from these sensors is transmitted to the vehicle's ECU (Engine Control Unit), which centrally manages this data. The data collection unit can also acquire driving data from the vehicle's ECU. The ECU manages detailed data such as engine operating status, fuel consumption, and engine speed, and this data is also acquired by the data collection unit. Furthermore, the data collection unit can also collect driving data using GPS data. GPS data provides real-time vehicle location information and travel route, allowing for a detailed understanding of the driver's driving patterns and travel routes. For example, the data collection unit collects vehicle speed and location information in real time. This allows the data collection unit to gain a detailed understanding of the driver's driving behavior and the vehicle's operating status, and to provide necessary data to other departments within the driver assistance system. 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 provision departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0071] The analysis department analyzes driving data collected by the data collection department. For example, the analysis department analyzes driving data to identify the frequency of sudden braking and sudden acceleration. Specifically, it analyzes collected speed and acceleration data to detect sudden changes in speed and fluctuations in acceleration. This allows the analysis department to understand the characteristics of the driver's driving style and behavior. The analysis department can also use generative AI to analyze driving data. Generative AI learns from large amounts of driving data and builds models to identify patterns in driving behavior. For example, the analysis department inputs driving data into the generative AI to identify the frequency of sudden braking and sudden acceleration. The generative AI can detect patterns of sudden braking and sudden acceleration from the driving data and identify their frequency and the conditions under which they occur. Furthermore, based on the results of the driving data analysis, the generative AI can analyze the characteristics of the driver's driving style and behavior in detail. This allows the analysis department to understand the driver's driving behavior in detail and provide necessary information to other departments of the driver assistance system. In addition, the analysis department can also use historical data and statistical information to analyze long-term trends and patterns of driving behavior. For example, based on past driving data, the system analyzes trends in changes and improvements in the driving behavior of specific drivers, providing data for future driving assistance. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual driving behavior or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp driving behavior in real time but also to analyze long-term driving behavior and detect anomalies, thereby improving the overall reliability and safety of the system.

[0072] The service provider provides customized advice based on the analysis results obtained by the analysis department. For example, the service provider provides specific hints to reduce the frequency of sudden braking and acceleration. Specifically, it provides specific advice on improving driving style and safe driving based on the characteristics of the driver's driving behavior. The service provider can generate customized advice using a generative AI. The generative AI identifies areas for improvement in the driver's driving behavior and generates specific advice based on the analysis results of the driving data provided by the analysis department. For example, the service provider inputs the analysis results into the generative AI and generates specific hints. The generative AI can analyze the characteristics of the driver's driving behavior and provide specific advice to reduce the frequency of sudden braking and acceleration. This allows the service provider to provide drivers with specific and practical advice, supporting the improvement of driving skills and the promotion of safe driving. Furthermore, the service provider can collect driver feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can review and improve the advice based on driver feedback to provide more effective advice. In addition, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide advice to drivers, supporting the improvement of driving skills and the promotion of safe driving.

[0073] The dialogue unit engages in interactive conversations based on advice provided by the service provider. For example, the dialogue unit encourages users to change their behavior for safer driving. Specifically, it explains areas for improvement in driving behavior and the importance of safe driving to drivers, engaging in conversations to deepen their understanding. The dialogue unit can conduct interactive conversations using generative AI. The generative AI generates conversations with drivers based on advice provided by the service provider. For example, the dialogue unit inputs advice into the generative AI to generate a conversation with the user. The generative AI can provide specific advice and explanations to drivers based on the characteristics and areas for improvement of their driving behavior. This allows the dialogue unit to provide drivers with specific and practical advice, supporting the improvement of driving skills and the promotion of safer driving. Furthermore, the dialogue unit can collect driver feedback and continuously improve the accuracy and effectiveness of the conversation content. For example, it can review and improve the conversation content based on driver feedback to provide more effective conversations. The dialogue unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the dialogue unit to provide drivers with quick and reliable advice, supporting improvements in driving skills and promoting safe driving.

[0074] The learning unit learns the user's driving patterns based on information obtained by the dialogue unit. For example, the learning unit analyzes the user's driving history to identify driving patterns. Specifically, it analyzes the characteristics and style of the driver's driving behavior in detail to identify the driver's driving patterns. The learning unit can learn driving patterns using generative AI. Generative AI learns from a large amount of driving data and builds a model to identify patterns of driving behavior. For example, the learning unit inputs driving history into the generative AI to identify driving patterns. The generative AI can analyze the characteristics and style of the driver's driving behavior to identify the driver's driving patterns. This allows the learning unit to understand the driver's driving behavior in detail and provide necessary information to other departments of the driver assistance system. Furthermore, the learning unit can also analyze long-term trends and patterns of driving behavior by utilizing past data and statistical information. For example, based on past driving data, it can analyze trends in changes and improvements in the driving behavior of a specific driver and provide data for future driver assistance. In addition, the learning unit can use anomaly detection algorithms to detect unusual driving behavior or abnormal data and issue warnings early. This allows the learning unit to not only grasp driving behavior in real time, but also to analyze long-term driving behavior and detect anomalies, thereby improving the reliability and safety of the entire system.

[0075] The data collection unit can collect driving data in real time. For example, the data collection unit collects driving data in real time using sensors mounted on the vehicle. For example, the data collection unit collects vehicle speed and location information in real time. The data collection unit can also acquire driving data in real time from the vehicle's ECU (Engine Control Unit). For example, the data collection unit collects vehicle engine speed and fuel consumption in real time. Furthermore, the data collection unit can also collect driving data in real time using GPS data. For example, the data collection unit collects vehicle location information in real time. This allows for analysis that reflects the latest driving conditions by collecting driving data in real time. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input data acquired from sensors mounted on the vehicle into a generation AI and have the generation AI perform real-time data collection.

[0076] The analysis unit can analyze collected driving data and identify the frequency of sudden braking and sudden acceleration. For example, the analysis unit can analyze driving data to identify the frequency of sudden braking. For example, the analysis unit can identify the frequency of sudden braking based on the braking force and the rate of speed reduction. The analysis unit can also analyze driving data to identify the frequency of sudden acceleration. For example, the analysis unit can identify the frequency of sudden acceleration based on the magnitude of acceleration and the change in speed per unit of time. Furthermore, the analysis unit can analyze driving data to comprehensively identify the frequency of sudden braking and sudden acceleration. For example, the analysis unit can identify the frequency of sudden braking and sudden acceleration by combining the braking force and the magnitude of acceleration. By identifying the frequency of sudden braking and sudden acceleration, specific areas for improvement for safe driving can be identified. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input collected driving data into a generative AI and have the generative AI perform the identification of the frequency of sudden braking and sudden acceleration.

[0077] The service provider can provide specific hints to reduce the frequency of sudden braking and sudden acceleration based on the analysis results. For example, the service provider can provide specific hints to reduce the frequency of sudden braking. For example, the service provider can suggest ways to adjust the timing and intensity of braking. The service provider can also provide specific hints to reduce the frequency of sudden acceleration. For example, the service provider can suggest ways to adjust how the accelerator is pressed and how the speed is increased. Furthermore, the service provider can also provide comprehensive hints to reduce the frequency of sudden braking and sudden acceleration. For example, the service provider can suggest ways to adjust the rhythm and timing of driving. By providing specific hints, it becomes easier for users to practice safe driving. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the analysis results into a generative AI and have the generative AI generate specific hints.

[0078] The dialogue unit can encourage behavioral changes for safe driving through interactive dialogue. For example, the dialogue unit can encourage users to change their behavior for safe driving. For example, the dialogue unit can point out points that users should pay attention to while driving and suggest specific ways to improve. The dialogue unit can also engage in dialogue with users to reflect on their driving behavior and find areas for improvement. For example, the dialogue unit can point out the frequency of sudden braking or acceleration while driving and suggest ways to improve. Furthermore, the dialogue unit can provide users with specific action plans to change their driving behavior. For example, the dialogue unit can provide users with step-by-step guidance on how to change their driving behavior. In this way, users can naturally acquire behaviors for safe driving through interactive dialogue. Some or all of the above processing in the dialogue unit may be performed using generative AI or not. For example, the dialogue unit can input the provided advice into generative AI and have the generative AI perform the generation of interactive dialogue.

[0079] The learning unit can learn the user's driving patterns and provide more accurate feedback using an evolving algorithm. For example, the learning unit can analyze the user's driving history and identify driving patterns. For example, the learning unit can identify the frequency of sudden braking and sudden acceleration from the user's driving history and learn those patterns. The learning unit can also analyze the user's driving style and identify driving patterns. For example, the learning unit can identify driving patterns based on the user's driving style. Furthermore, the learning unit can combine the user's driving history and driving style to comprehensively identify driving patterns. For example, the learning unit can identify driving patterns based on the user's driving history and driving style. This allows the learning unit to always provide the user with the latest feedback by using an evolving algorithm. Some or all of the above processing in the learning unit may be performed using generative AI, or it may be performed without using generative AI. For example, the learning unit can input the user's driving history into a generative AI and have the generative AI perform the learning of driving patterns.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of driving data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of driving data collection to alleviate the user's burden. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also increase the frequency of driving data collection and collect more detailed data if the user is relaxed. For example, the data collection unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only the most important data. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the adjustment of data collection timing according to the user's emotions, reducing the user's burden and enabling appropriate data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the data collection unit can input user emotion data into a generation AI and have the generation AI adjust the timing of driving data collection.

[0081] The data collection unit can analyze the user's past driving history and select the optimal data collection method. For example, the data collection unit can focus on collecting data in locations where the user frequently applied sudden brakes in the past. For example, the data collection unit can input the user's driving history into a generating AI to identify locations with a high frequency of sudden braking. The data collection unit can also concentrate data collection during specific time periods based on the user's past driving history. For example, the data collection unit can collect data during specific time periods based on the user's driving history. Furthermore, the data collection unit can customize the data collection method according to the user's driving style. For example, the data collection unit can analyze the user's driving style and select the optimal data collection method. This allows the data collection unit to provide the user with the most suitable data collection method by analyzing past driving history. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input the user's driving history into the generating AI and have the generating AI select the optimal data collection method.

[0082] The data collection unit can filter driving data based on the user's current driving situation and the vehicle's condition. For example, if the user is driving on a highway, the data collection unit will collect highway-specific data. For example, the data collection unit will input the user's driving situation into the generating AI and collect highway-related data. The data collection unit can also prioritize the collection of data related to the user's vehicle's condition if it requires maintenance. For example, the data collection unit will input the vehicle's condition into the generating AI and collect maintenance-related data. Furthermore, if the user is driving in rainy weather, the data collection unit can focus on collecting driving data for rainy weather. For example, the data collection unit will input weather information into the generating AI and collect driving data for rainy weather. This allows for efficient collection of necessary data by filtering the data based on the current driving situation and vehicle condition. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input the driving situation and vehicle condition into the generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of driving data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to safe driving. For example, the data collection unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also prioritize collecting data related to fuel efficiency if the user is relaxed. For example, the data collection unit may record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data related to sudden braking or acceleration. For example, the data collection unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the priority collection of important data by determining the data priority 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the data collection unit can input user emotion data into a generation AI and have the generation AI determine the priority of driving data.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting driving data. For example, if the user is driving in an urban area, the data collection unit can collect urban-specific driving data. For example, the data collection unit can input the user's geographical location information into the generating AI and collect data related to urban areas. The data collection unit can also collect mountain-specific driving data if the user is driving in a mountainous area. For example, the data collection unit can collect data related to mountainous areas based on the user's geographical location information. Furthermore, if the user frequently drives in a particular region, the data collection unit can prioritize the collection of data related to that region. For example, the data collection unit can input the user's driving history into the generating AI and collect data related to that particular region. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input the user's geographical location information into the generating AI and have the generating AI perform the collection of highly relevant data.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data when collecting driving data. For example, if the user mentions a specific driving style on social media, the data collection unit can collect data related to that style. For example, the data collection unit inputs the user's social media activity into a generating AI and collects data related to that specific driving style. The data collection unit can also collect data related to specific road conditions if the user mentions those conditions on social media. For example, the data collection unit collects data related to specific road conditions based on the user's social media activity. Furthermore, if the user mentions a specific vehicle function on social media, the data collection unit can collect data related to that function. For example, the data collection unit inputs the user's social media activity into a generating AI and collects data related to that specific vehicle function. This makes it possible to collect data based on the user's interests by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using the generating AI or not. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is in a hurry, the analysis unit can provide concise and to-the-point analysis results. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis to be presented in a way that is easy for the user to understand, by adjusting the presentation of the analysis 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 includes, 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 a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the way the analysis is expressed.

[0087] The analysis unit can adjust the level of detail of its analysis based on the importance of the driving data. For example, the analysis unit can perform a detailed analysis on important driving data. For example, the analysis unit can input the importance of the driving data into the generating AI and perform a detailed analysis. The analysis unit can also perform a concise analysis on general driving data. For example, the analysis unit can perform a concise analysis based on the importance of the driving data. Furthermore, the analysis unit can perform an analysis of specific driving data at a moderate level of detail. For example, the analysis unit can perform an analysis at a moderate level of detail based on the importance of the driving data. By adjusting the level of detail of the analysis based on the importance of the driving data, the necessary information can be provided efficiently. Some or all of the above processing in the analysis unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the analysis unit can input the importance of the driving data into the generating AI and have the generating AI adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of driving data during analysis. For example, the analysis unit can apply a specific algorithm to data related to sudden braking. For instance, the analysis unit inputs sudden braking data into the generating AI and applies a specific algorithm. The analysis unit can also apply a different algorithm to data related to sudden acceleration. For example, the analysis unit inputs sudden acceleration data into the generating AI and applies a different algorithm. Furthermore, the analysis unit can apply a different algorithm to data related to fuel efficiency. For example, the analysis unit inputs fuel efficiency data into the generating AI and applies a different algorithm. This allows for the application of an appropriate analysis algorithm according to the category of driving data, thereby providing highly accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without using the generating AI. For example, the analysis unit can input the categories of driving data into the generating AI and have the generating AI execute the application of different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also provide a detailed analysis if the user is relaxed. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide a brief analysis. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to provide the optimal amount of information for the user by adjusting the length of the analysis according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of data collection during the analysis process. For example, the analysis unit can prioritize the analysis of recently collected data. For instance, the analysis unit can input the data collection timing into the generating AI and prioritize its analysis. The analysis unit can also postpone the analysis of past data. For example, the analysis unit can postpone its analysis based on the data collection timing. Furthermore, the analysis unit can set priorities for analysis of data collected during specific periods. For example, the analysis unit can set priorities based on the data collection timing and perform the analysis accordingly. This allows for the prioritization of the most recent data by determining the analysis priority based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may not be performed using the generating AI. For example, the analysis unit can input the data collection timing into the generating AI and have the generating AI determine the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the driving data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant driving data. For example, the analysis unit can input the relevance of the driving data into the generating AI and prioritize its analysis. The analysis unit can also postpone the analysis of less relevant driving data. For example, the analysis unit can postpone the analysis of driving data based on its relevance. Furthermore, the analysis unit can set the order of analysis for specific driving data based on its relevance. For example, the analysis unit can set the order of analysis based on the relevance of the driving data. This allows for the prioritization of important data by adjusting the order of analysis based on the relevance of the driving data. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the relevance of the driving data into the generating AI and have the generating AI adjust the order of analysis.

[0092] The service provider can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is stressed, the service provider can provide simple and easily understandable advice. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the service provider can provide detailed advice. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is in a hurry, the service provider can provide concise and to-the-point advice. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the service provider to adjust the presentation of advice according to the user's emotions, providing advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provision unit can input user emotion data into a generative AI and have the generative AI adjust the way the advice is expressed.

[0093] The service provider can adjust the level of detail of the advice based on the importance of the driving data at the time of delivery. For example, the service provider can provide detailed advice for important driving data. For example, the service provider can input the importance of the driving data into the generating AI and provide detailed advice. The service provider can also provide concise advice for general driving data. For example, the service provider can provide concise advice based on the importance of the driving data. Furthermore, the service provider can provide advice with a moderate level of detail for specific driving data. For example, the service provider can provide advice with a moderate level of detail based on the importance of the driving data. This allows for the efficient provision of necessary information by adjusting the level of detail of the advice based on the importance of the driving data. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider can input the importance of the driving data into the generating AI and have the generating AI adjust the level of detail of the advice.

[0094] The service provider can apply different advice algorithms depending on the category of driving data at the time of service provision. For example, the service provider can apply a specific advice algorithm to data related to sudden braking. For example, the service provider can input sudden braking data into the generating AI and apply a specific advice algorithm. The service provider can also apply a different advice algorithm to data related to sudden acceleration. For example, the service provider can input sudden acceleration data into the generating AI and apply a different advice algorithm. Furthermore, the service provider can apply a different advice algorithm to data related to fuel efficiency. For example, the service provider can input fuel efficiency data into the generating AI and apply a different advice algorithm. This allows for the provision of highly accurate advice by applying the appropriate advice algorithm according to the category of driving data. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider can input the categories of driving data into the generating AI and have the generating AI perform the application of different advice algorithms.

[0095] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the service provider can provide short, concise advice. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also provide detailed advice if the user is relaxed. For example, the service provider can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the service provider can provide concise advice. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the service provider to provide the optimal amount of information for the user by adjusting the length of the advice according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provision unit can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0096] The service provider can determine the priority of advice based on the timing of data collection at the time of service provision. For example, the service provider can prioritize advice for recently collected data. For example, the service provider can input the data collection timing into the generating AI and provide advice preferentially. The service provider can also postpone advice for past data. For example, the service provider can postpone advice based on the data collection timing. Furthermore, the service provider can set priorities for advice for data collected during a specific period. For example, the service provider can set priorities for advice based on the data collection timing. This allows for prioritizing advice based on the data collection timing, ensuring that the most recent data is prioritized. Some or all of the above processing in the service provider may be performed using the generating AI, or not. For example, the service provider can input the data collection timing into the generating AI and have the generating AI determine the priority of advice.

[0097] The service provider can adjust the order of advice based on the relevance of the driving data at the time of delivery. For example, the service provider can prioritize providing advice to highly relevant driving data. For example, the service provider can input the relevance of the driving data into the generating AI and prioritize providing advice. The service provider can also postpone providing advice to less relevant driving data. For example, the service provider can postpone providing advice based on the relevance of the driving data. Furthermore, the service provider can set the order of advice based on the relevance of specific driving data. For example, the service provider can set the order of advice based on the relevance of the driving data. In this way, by adjusting the order of advice based on the relevance of the driving data, important data can be prioritized for advice. Some or all of the above processing in the service provider may be performed using the generating AI or not using the generating AI. For example, the service provider can input the relevance of the driving data into the generating AI and have the generating AI perform the adjustment of the order of advice.

[0098] The dialogue unit can estimate the user's emotions and adjust the way it expresses the conversation based on those emotions. For example, if the user is stressed, the dialogue unit will speak in a calm tone. For example, the dialogue unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The dialogue unit can also speak in a bright tone if the user is relaxed. For example, the dialogue unit may record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the dialogue unit can speak quickly and concisely. For example, the dialogue unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the dialogue to be adjusted according to the user's emotions, providing a conversation that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 dialogue unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI adjust the way the dialogue is expressed.

[0099] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's preferred dialogue style in the past. For example, the dialogue unit can input the user's past dialogue history into a generating AI and select the optimal dialogue method. The dialogue unit can also provide dialogue content tailored to the user's preferences based on the user's past dialogue history. For example, the dialogue unit can provide dialogue content tailored to the user's preferences based on the user's past dialogue history. Furthermore, the dialogue unit can adjust the timing of the conversation by referring to when the user previously interrupted a conversation. For example, the dialogue unit adjusts the timing of the conversation based on the user's past dialogue history. In this way, by referring to past dialogue history, the dialogue unit can provide the user with the most optimal dialogue method. Some or all of the above processing in the dialogue unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI and have the generating AI select the optimal dialogue method.

[0100] The dialogue unit can customize the dialogue content based on the user's current driving situation during a conversation. For example, if the user is driving on a highway, the dialogue unit will provide dialogue content related to highways. For example, the dialogue unit inputs the user's driving situation into the generating AI and provides dialogue content related to highways. The dialogue unit can also provide dialogue content related to urban areas if the user is driving in an urban area. For example, the dialogue unit provides dialogue content related to urban areas based on the user's driving situation. Furthermore, if the user is parked, the dialogue unit can provide dialogue content related to parking. For example, the dialogue unit provides dialogue content related to parking based on the user's driving situation. In this way, by customizing the dialogue content based on the current driving situation, it is possible to provide a dialogue that is appropriate for the user. Some or all of the above processing in the dialogue unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the dialogue unit can input the user's driving situation into the generating AI and have the generating AI perform the customization of the dialogue content.

[0101] The dialogue unit can estimate the user's emotions and prioritize the conversation based on those emotions. For example, if the user is stressed, the dialogue unit will prioritize providing important conversational content. For instance, the dialogue unit might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the dialogue unit can provide detailed conversational content. For example, the dialogue unit might record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is in a hurry, the dialogue unit can provide concise conversational content. For example, the dialogue unit might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the dialogue unit to prioritize important conversational content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 dialogue unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI determine the priority of the dialogue.

[0102] The dialogue unit can provide optimal dialogue content by considering the user's geographical location information during a conversation. For example, if the user is driving in a specific area, the dialogue unit can provide dialogue content related to that area. For example, the dialogue unit can input the user's geographical location information into a generating AI and provide dialogue content related to that area. The dialogue unit can also provide tourist information if the user is driving in a tourist area. For example, the dialogue unit can provide tourist information based on the user's geographical location information. Furthermore, if the user is driving near their home, the dialogue unit can provide dialogue content related to their home. For example, the dialogue unit can provide dialogue content related to their home based on the user's geographical location information. In this way, by considering geographical location information, the dialogue unit can provide dialogue content that is highly relevant to the user. Some or all of the above processing in the dialogue unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the dialogue unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal dialogue content.

[0103] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, if the user mentions a specific topic on social media, the dialogue unit can provide dialogue content related to that topic. For example, the dialogue unit can input the user's social media activity into a generative AI and provide dialogue content related to that specific topic. The dialogue unit can also provide dialogue content related to a specific event if the user mentions that event on social media. For example, the dialogue unit can provide dialogue content related to a specific event based on the user's social media activity. Furthermore, if the user mentions a specific place on social media, the dialogue unit can provide dialogue content related to that place. For example, the dialogue unit can input the user's social media activity into a generative AI and provide dialogue content related to that place. In this way, by analyzing social media activity, it is possible to provide dialogue content based on the user's interests. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the dialogue unit can input the user's social media activity into a generative AI and have the generative AI suggest dialogue content.

[0104] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will select training data related to stress reduction. For example, the learning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The learning unit can also select training data related to relaxation if the user is relaxed. For example, the learning unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the learning unit can select training data related to efficient driving. For example, the learning unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By selecting training data according to the user's emotions, the learning unit can provide the user with the most optimal training content. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of learning data.

[0105] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize the learning algorithm based on the user's past learning data. For example, the learning unit can input past learning data into a generating AI and optimize the learning algorithm. The learning unit can also select the optimal learning method from the user's past learning history. For example, the learning unit selects the optimal learning method based on past learning history. Furthermore, the learning unit can adjust the learning algorithm by referring to the user's past learning results. For example, the learning unit can input past learning results into a generating AI and adjust the learning algorithm. This allows for the optimization of the learning algorithm by referring to past learning data, thereby achieving highly accurate learning. Some or all of the above processes in the learning unit may be performed using a generating AI, or they may be performed without using a generating AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0106] The learning unit can customize the learning content based on the user's driving patterns during the learning process. For example, the learning unit can analyze the user's driving patterns and provide optimal learning content. For example, the learning unit can input the user's driving patterns into the generating AI and provide optimal learning content. The learning unit can also customize the learning content according to the user's driving style. For example, the learning unit can customize the learning content based on the user's driving style. Furthermore, the learning unit can adjust the learning content based on the user's driving history. For example, the learning unit can input the user's driving history into the generating AI and adjust the learning content. This allows the learning unit to provide optimal learning content for the user by customizing the learning content based on the user's driving patterns. Some or all of the above processes in the learning unit may be performed using the generating AI, or they may be performed without using the generating AI. For example, the learning unit can input the user's driving patterns into the generating AI and have the generating AI perform the customization of the learning content.

[0107] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit will reduce the learning frequency. For instance, the learning unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The learning unit can also increase the learning frequency if the user is relaxed. For example, the learning unit may record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the learning unit can adjust the learning frequency if the user is in a hurry. For example, the learning unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the learning unit to provide the user with an optimal learning pace by adjusting the learning frequency according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 learning unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning frequency.

[0108] The learning unit can weight the training data based on the timing of data collection during training. For example, the learning unit can weight recently collected training data. For example, the learning unit can input the data collection timing into the generating AI and perform the weighting. The learning unit can also lightly weight past training data. For example, the learning unit can lightly weight the data based on the data collection timing. Furthermore, the learning unit can adjust the weighting for training data collected during a specific period. For example, the learning unit can adjust the weighting based on the data collection timing. This allows for training that prioritizes the latest data by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the learning unit can input the data collection timing into the generating AI and have the generating AI perform the weighting of the training data.

[0109] The learning unit can optimize the learning content by considering the user's geographical location information during the learning process. For example, if the user is driving in a specific area, the learning unit can provide learning content relevant to that area. For example, the learning unit can input the user's geographical location information into the generating AI and provide learning content relevant to that area. The learning unit can also provide learning content related to tourist information if the user is driving in a tourist area. For example, the learning unit can provide learning content related to tourist information based on the user's geographical location information. Furthermore, if the user is driving near their home, the learning unit can provide learning content related to their home. For example, the learning unit can provide learning content related to their home based on the user's geographical location information. In this way, by considering geographical location information, the learning unit can provide learning content that is highly relevant to the user. Some or all of the above processing in the learning unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the learning unit can input the user's geographical location information into the generating AI and have the generating AI perform the optimization of the learning content.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The system can suggest music and podcasts to listen to while driving based on the user's driving data. For example, if the user frequently brakes suddenly, the system can suggest relaxing music. It can also suggest podcasts to help the user maintain concentration during long-distance drives. Furthermore, the system can suggest music to enhance the user's mood while driving, depending on their driving style. This can improve the user's driving experience.

[0112] The data collection unit can automatically adjust the cabin temperature while the vehicle is in motion based on the user's driving data. For example, if the user frequently accelerates rapidly, the unit will lower the cabin temperature to enhance the cooling effect. Furthermore, if the user drives for extended periods, the unit can adjust the cabin temperature to maintain a comfortable temperature. In addition, the unit can optimize the cabin temperature according to the user's driving style. This improves the user's comfort while driving.

[0113] The analysis unit can estimate the user's fatigue level while driving based on their driving data and suggest when to take a break. For example, if the user frequently brakes or accelerates suddenly, the analysis unit will estimate that the user is fatigued and suggest a break. Furthermore, if the user drives for extended periods, the analysis unit can suggest breaks at regular intervals. In addition, the analysis unit can suggest the optimal break timing based on the user's driving style. This can improve the user's safety while driving.

[0114] The system can provide eco-driving advice based on the user's driving data. For example, if the user frequently accelerates suddenly, the system can provide advice to improve fuel efficiency. Similarly, if the user frequently brakes suddenly, the system can provide advice to improve braking technique. Furthermore, the system can provide comprehensive eco-driving advice tailored to the user's driving style. This can improve the user's fuel efficiency and reduce their environmental impact.

[0115] The interactive unit can suggest relaxation methods to reduce stress while driving, based on the user's driving data. For example, if the user frequently brakes or accelerates suddenly, the unit may suggest deep breathing or relaxing music. It can also suggest stretching or light exercise if the user drives for long periods. Furthermore, the unit can suggest comprehensive relaxation methods to reduce stress, tailored to the user's driving style. This helps reduce stress while driving and supports a more comfortable driving experience.

[0116] The data collection unit can estimate the user's emotions and adjust the method of collecting driving data based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of driving data collection to lessen the user's burden. Conversely, if the user is relaxed, the unit can increase the frequency of driving data collection to collect more detailed data. Furthermore, if the user is in a hurry, the unit can prioritize collecting only the most important data. By adjusting the data collection method according to the user's emotions, the system can reduce the user's burden and ensure appropriate data collection.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on those estimated emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand results. If the user is relaxed, the analysis unit can provide detailed results. Furthermore, if the user is in a hurry, the analysis unit can provide concise results that get straight to the point. By adjusting the presentation of the analysis results according to the user's emotions, the system can provide analysis results that are easy for the user to understand.

[0118] The service provider can estimate the user's emotions and adjust the content of the advice provided based on those emotions. For example, if the user is feeling stressed, the service provider will provide relaxing advice. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user is in a hurry, the service provider can provide concise advice that gets straight to the point. In this way, by adjusting the content of the advice according to the user's emotions, the service provider can provide the most appropriate advice for the user.

[0119] The dialogue unit can estimate the user's emotions and adjust the content of the conversation based on those emotions. For example, if the user is stressed, the dialogue unit will use a calm tone. Conversely, if the user is relaxed, it can use a cheerful tone. Furthermore, if the user is in a hurry, the dialogue unit can use a quick and concise approach. By adjusting the content of the conversation according to the user's emotions, it can provide a dialogue that is easy for the user to understand.

[0120] The learning unit can estimate the user's emotions and adjust the learning content based on those emotions. For example, if the user is feeling stressed, the learning unit will provide learning content related to stress reduction. It can also provide learning content related to relaxation if the user is relaxed. Furthermore, if the user is in a hurry, it can provide learning content related to efficient driving. This allows the learning unit to provide the most suitable learning content for the user by adjusting it according to their emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects driving data. This driving data includes speed, acceleration, and brake usage frequency. The data collection unit acquires driving data from sensors mounted on the vehicle and from the vehicle's ECU (Engine Control Unit). It can also collect vehicle speed and location information in real time using GPS data. Step 2: The analysis unit analyzes the driving data collected by the collection unit. For example, it analyzes the driving data to identify the frequency of sudden braking and sudden acceleration. It is also possible to analyze the driving data using generative AI. Step 3: The service provider provides customized advice based on the analysis results obtained by the analysis provider. For example, it provides specific tips for reducing the frequency of sudden braking and acceleration. It can also generate customized advice using generative AI. Step 4: The dialogue unit engages in an interactive conversation based on the advice provided by the delivery unit. For example, it encourages the user to change their behavior for safer driving. Interactive conversations can also be conducted using generative AI. Step 5: The learning unit learns the user's driving patterns based on the information obtained by the dialogue unit. For example, it analyzes the user's driving history to identify driving patterns. It is also possible to learn driving patterns using generative AI.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, dialogue unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects driving data using the sensors and camera 42 of the smart device 14 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The provision unit provides advice generated by the specific processing unit 290 of the data processing unit 12 to the user through the display 40A and speaker 40B of the smart device 14. The dialogue unit engages in interactive dialogue with the user using the microphone 38B of the smart device 14, and the learning unit learns the user's driving patterns using the specific processing unit 290 of the data processing unit 12. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, dialogue unit, and learning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The provision unit provides advice generated by the specific processing unit 290 of the data processing unit 12 to the user through the speaker 240 of the smart glasses 214. The dialogue unit engages in interactive dialogue with the user using the microphone 238 of the smart glasses 214, and the learning unit learns the user's driving patterns using the specific processing unit 290 of the data processing unit 12. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, dialogue unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The provision unit provides advice generated by the specific processing unit 290 of the data processing unit 12 to the user through the speaker 240 of the headset terminal 314. The dialogue unit engages in interactive dialogue with the user using the microphone 238 of the headset terminal 314, and the learning unit learns the user's driving patterns using the specific processing unit 290 of the data processing unit 12. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, dialogue unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects driving data using the camera 42 and microphone 238 of the robot 414 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The provision unit provides advice generated by the specific processing unit 290 of the data processing unit 12 to the user through the speaker 240 of the robot 414. The dialogue unit engages in interactive dialogue with the user using the microphone 238 of the robot 414, and the learning unit learns the user's driving patterns using the specific processing unit 290 of the data processing unit 12. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects driving data, An analysis unit analyzes the driving data collected by the aforementioned collection unit, A provision unit that provides customized advice based on the analysis results obtained by the aforementioned analysis unit, A dialogue unit that engages in interactive dialogue based on the advice provided by the aforementioned provision unit, The system includes a learning unit that learns the user's driving patterns based on the information obtained by the dialogue unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect driving data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is By analyzing the collected driving data, the frequency of sudden braking and sudden acceleration is identified. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the analysis results, we will provide specific tips to reduce the frequency of sudden braking and acceleration. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, Encouraging behavioral changes for safer driving through interactive dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, It learns the user's driving patterns and uses an evolving algorithm to provide more accurate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of driving 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 Analyze the user's past driving history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting driving data, filtering is performed based on the user's current driving status and the vehicle's condition. 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 determines the priority of driving data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting driving data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting driving data, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of driving data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the driving data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the advice, we adjust the level of detail based on the importance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the data, different advice algorithms are applied depending on the category of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we prioritize it based on when the driving data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, the order of advice will be adjusted based on the relevance of the driving data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During interaction, the conversation content is customized based on the user's current driving status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, During conversations, the system provides optimal dialogue content while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and suggests conversation topics. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning unit, During the learning process, the learning content is customized based on the user's driving patterns. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning unit, During training, the training data is weighted based on the timing of driving data collection. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning unit, During learning, the learning content is optimized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects driving data, An analysis unit analyzes the driving data collected by the aforementioned collection unit, A provision unit that provides customized advice based on the analysis results obtained by the aforementioned analysis unit, A dialogue unit that engages in interactive dialogue based on the advice provided by the aforementioned provision unit, The system includes a learning unit that learns the user's driving patterns based on the information obtained by the dialogue unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect driving data in real time. The system according to feature 1.

3. The aforementioned analysis unit is By analyzing the collected driving data, the frequency of sudden braking and sudden acceleration is identified. The system according to feature 1.

4. The aforementioned supply unit is, Based on the analysis results, we will provide specific tips to reduce the frequency of sudden braking and acceleration. The system according to feature 1.

5. The aforementioned dialogue unit, Encouraging behavioral changes for safer driving through interactive dialogue. The system according to feature 1.

6. The aforementioned learning unit, It learns the user's driving patterns and uses an evolving algorithm to provide more accurate feedback. The system according to feature 1.

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

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

9. The aforementioned collection unit is When collecting driving data, filtering is performed based on the user's current driving status and the vehicle's condition. The system according to feature 1.

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

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