System

Abstract

An object of a system according to an embodiment is to analyze behavior data of a driver and support safe driving.SOLUTION: A system includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects action data of a driver. The analysis unit analyzes the data collected by the collection unit and evaluates the driving behavior of the driver. The feedback unit provides feedback to the driver in real time based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately support safe driving based on driver behavior data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze driver behavior data and support safe driving. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects driver behavior data. The analysis unit analyzes the data collected by the collection unit and evaluates the driver's driving behavior. The feedback unit provides real-time feedback to the driver based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze driver behavior data and support safe driving. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A safe driving support system according to an embodiment of the present invention collects driver behavior data, analyzes it using AI, and provides real-time feedback. The safe driving support system collects driver behavior data, analyzes it using AI, and evaluates the driver's driving behavior. For example, it can detect dangerous driving behaviors such as sudden braking, sudden acceleration, and lane departure. Furthermore, it provides real-time feedback to the driver based on the analysis results. For example, it can alert the driver through audio or visual alerts, or vibrations of the seat or steering wheel. This supports safe driving and reduces the risk of traffic accidents. For example, the safe driving support system collects driver behavior data from an eye-tracking camera, steering wheel operation sensors, pedal operation sensors, and the like installed in a vehicle. Then, the safe driving support system uses AI to analyze the collected data and evaluate the driver's driving behavior. For example, it can detect dangerous driving behaviors such as sudden braking, sudden acceleration, and lane departure. Furthermore, the safe driving support system provides real-time feedback to the driver based on the analysis results. For example, it can alert the driver through audio or visual alerts, or vibrations of the seat or steering wheel. This allows the driver to immediately correct dangerous driving behaviors. This allows the safe driving support system to support the driver in safe driving and reduce the risk of traffic accidents. This allows the safe driving support system to monitor the driver's behavior in real time and provide appropriate feedback. For example, if the driver deviates from their lane, an audio alert can be issued to encourage the driver to return to their lane. In addition, if sudden braking or sudden acceleration is detected, a visual alert can be issued to encourage the driver to continue driving safely. This reduces the risk of traffic accidents and improves road safety.

[0029] A safe driving support system according to an embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects driver behavior data. The driver behavior data includes, but is not limited to, gaze data, steering wheel operation data, and pedal operation data. The collection unit collects the driver's gaze data using, for example, an eye-tracking camera. The collection unit can also collect steering wheel operation data using a steering wheel operation sensor. The collection unit can also collect pedal operation data using a pedal operation sensor. For example, the eye-tracking camera tracks the driver's gaze movement with high accuracy to collect gaze data. The steering wheel operation sensor measures the rotation angle and operation force of the steering wheel to collect steering wheel operation data. The pedal operation sensor measures the operation amount of the brake pedal and accelerator pedal to collect pedal operation data. The analysis unit analyzes the data collected by the collection unit and evaluates the driver's driving behavior. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit detects dangerous driving behaviors such as sudden braking, sudden acceleration, and lane departure based on the collected data. The analysis unit can also evaluate the safety and efficiency of the driver's driving behavior. The analysis unit can also predict the driver's driving behavior using a machine learning algorithm. For example, the analysis unit predicts future driving behavior based on past driving data and detects dangerous driving behavior in advance. The feedback unit provides feedback to the driver in real time based on the analysis results obtained by the analysis unit. The feedback can be, for example, an audio alert, a visual alert, or vibration of the seat or steering wheel, but is not limited to these examples. For example, the feedback unit can alert the driver to the driver's attention using an audio alert. The feedback unit can also alert the driver to ...The vibration of the seat and steering wheel provides physical feedback to the driver to alert them. This allows the safe driving support system according to the embodiment to monitor the driver's behavior in real time and provide appropriate feedback. This supports the driver's safe driving and reduces the risk of traffic accidents.

[0030] The collection unit can collect driver behavior data using an eye-tracking camera, a steering wheel operation sensor, and a pedal operation sensor. The eye-tracking camera, for example, tracks the driver's eye movement with high precision and collects gaze data. The resolution and installation position of the eye-tracking camera are adjusted to accurately track the driver's gaze. The steering wheel operation sensor, for example, measures the steering wheel rotation angle and operation force to collect steering wheel operation data. The sensitivity and installation position of the steering wheel operation sensor are adjusted to accurately measure the driver's steering operation. The pedal operation sensor, for example, measures the amount of brake pedal or accelerator pedal operation to collect pedal operation data. The sensitivity and installation position of the pedal operation sensor are adjusted to accurately measure the driver's pedal operation. In this way, detailed behavior data can be collected by using the eye-tracking camera, the steering wheel operation sensor, and the pedal operation sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input gaze data acquired by the eye-tracking camera to a generation AI and have the generation AI analyze the gaze data.

[0031] The analysis unit can detect dangerous driving behaviors such as sudden braking, sudden acceleration, and lane departure based on the collected data. The analysis unit detects sudden braking based on, for example, the collected data. Sudden braking is detected based on the speed at which the brake pedal is operated and the deceleration rate of the vehicle. For example, the analysis unit detects sudden braking when the brake pedal is suddenly operated or the vehicle suddenly decelerates. The analysis unit can also detect sudden acceleration based on the collected data. Sudden acceleration is detected based on the speed at which the accelerator pedal is operated and the acceleration rate of the vehicle. For example, the analysis unit detects sudden acceleration when the accelerator pedal is suddenly operated or the vehicle suddenly accelerates. The analysis unit can also detect lane departure based on the collected data. Lane departure is detected based on vehicle position data and lane position data. For example, the analysis unit detects lane departure when the vehicle deviates from its lane. This allows the driver's driving behavior to be evaluated by detecting dangerous driving behavior. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI detect sudden braking, sudden acceleration, and lane departure.

[0032] The feedback unit can alert the driver through audio alerts, visual alerts, and vibrations of the seat or steering wheel. The feedback unit can alert the driver using, for example, audio alerts. Audio alerts are used to warn the driver of dangerous driving behavior. For example, the feedback unit issues an audio alert such as "Sudden braking. Be careful" when it detects sudden braking. The feedback unit can also alert the driver using visual alerts. The visual alert alerts alert the driver by displaying a warning message on a display. For example, the feedback unit displays a message such as "Lane departure. Be careful" when it detects lane departure. The feedback unit can also alert the driver using vibrations of the seat or steering wheel. Vibrations of the seat or steering wheel provide physical feedback to the driver to alert them. For example, the feedback unit vibrates the seat or steering wheel when it detects sudden acceleration. This allows feedback to be provided in real time by alerting the driver through audio alerts, visual alerts, and vibrations of the seat or steering wheel. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the analysis results obtained by the analysis unit into the generation AI and have the generation AI select an appropriate feedback method.

[0033] The collection unit can analyze the driver's past driving history and select an appropriate data collection method. For example, if the driver has a history of frequently braking suddenly in the past, the collection unit can have the AI ​​analyze that pattern and collect detailed data at the time of the sudden braking. Furthermore, if the driver has a history of engaging in dangerous driving behavior during a specific time period in the past, the collection unit can also have the AI ​​focus on collecting data during that time period. Furthermore, if the driver has a history of engaging in dangerous driving behavior on a specific road in the past, the collection unit can also collect detailed data when the AI ​​passes through that road. This allows the optimal data collection method to be selected by analyzing the past driving history. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the driver's past driving data into the generation AI and have the generation AI select the optimal data collection method.

[0034] When collecting behavioral data, the collection unit can filter the data based on the driver's current driving situation and environment. For example, if the driver is driving on a highway, the collection unit uses an AI to detect the situation and prioritize collecting highway-specific data. Furthermore, if the driver is stuck in traffic, the collection unit can use an AI to detect the situation and collect detailed driving behavior data during traffic jams. Furthermore, if the driver is driving at night, the collection unit can use an AI to detect the situation and collect nighttime-specific driving behavior data. This allows for more relevant data to be collected by filtering data based on the current driving situation and environment. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit can input the driver's driving situation data to a generation AI and have the generation AI perform filtering.

[0035] When collecting behavioral data, the collection unit can select an appropriate collection means depending on the driver's input method. For example, if the driver uses voice input, the collection unit has an AI analyze the voice command and collect related behavioral data. Furthermore, if the driver uses gesture input, the collection unit can have an AI recognize the gesture and collect related behavioral data. Furthermore, if the driver uses gaze input, the collection unit can have an AI track gaze movements and collect related behavioral data. This enables more accurate data collection by selecting the optimal collection means depending on the driver's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the driver's voice data to a generation AI and have the generation AI analyze the voice command.

[0036] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the driver's geographical location information. For example, when the driver passes through a specific dangerous area, the collection unit causes the AI ​​to prioritize collecting data related to that area. Furthermore, when the driver passes through a specific intersection, the collection unit can also prioritize collecting data related to that intersection. Furthermore, when the driver drives on a specific road, the collection unit can also prioritize collecting data related to that road. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the driver's geographical location data to the generation AI and cause the generation AI to select highly relevant data.

[0037] When collecting behavioral data, the collection unit can analyze the driver's social media activities and collect related data. For example, the collection unit collects data related to the locations where the driver checked in on social media. The collection unit can also analyze the content of the driver's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the driver's friends on social media. In this way, related data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the driver's social media data into the generation AI and cause the generation AI to collect related data.

[0038] When collecting behavioral data, the collection unit can customize the collection method by reflecting the driver's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the driver in the past. The collection unit can also improve the collection method by taking into account problems pointed out by the driver in the past. The collection unit can also analyze the driver's past feedback and propose an optimal collection method. In this way, the collection method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the driver's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the driving behavior. For example, the analysis unit allows the AI ​​to perform a detailed analysis of dangerous driving behaviors such as sudden braking. The analysis unit can also allow the AI ​​to perform a simplified analysis of minor driving behaviors. The analysis unit can also allow the AI ​​to perform a detailed analysis of important driving behaviors and generate a detailed report. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the driving behavior. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input driving behavior data to a generation AI and cause the generation AI to perform an analysis with a level of detail according to the importance.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of driving behavior. For example, the analysis unit can have the AI ​​apply a specific analysis algorithm to sudden braking. The analysis unit can also have the AI ​​apply a different analysis algorithm to sudden acceleration. The analysis unit can also have the AI ​​apply a dedicated analysis algorithm to lane departure. This allows for more accurate analysis by applying different analysis algorithms depending on the category of driving behavior. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input driving behavior data to the generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the driver's past driving data. The analysis unit, for example, uses AI to improve the accuracy of the analysis based on the driver's past driving data. The analysis unit can also analyze the driver's past driving patterns and use AI to improve the accuracy of the analysis. The analysis unit can also use AI to improve the accuracy of the analysis by referring to the driver's past driving history. In this way, the accuracy of the analysis is improved by referring to the past driving data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past driving data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of occurrence of driving behavior. For example, the analysis unit allows the AI ​​to prioritize analysis of driving behaviors that occurred recently. The analysis unit can also allow the AI ​​to prioritize analysis of driving behaviors that occurred during a specific time period. The analysis unit can also allow the AI ​​to prioritize analysis of driving behaviors that occurred frequently in the past. This enables efficient analysis by determining the priority of analysis based on the time of occurrence of driving behavior. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input driving behavior data to the generation AI and cause the generation AI to perform analysis priorities based on the time of occurrence.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of driving behaviors. For example, the analysis unit adjusts the order of analysis by the AI, taking into account the relevance between sudden braking and sudden acceleration. The analysis unit can also adjust the order of analysis by the AI, taking into account the relevance between lane departure and sudden braking. The analysis unit can also adjust the order of analysis by the AI, taking into account the relevance of specific driving behaviors. In this way, adjusting the order of analysis based on the relevance of driving behaviors enables more relevant analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input driving behavior data to a generation AI and cause the generation AI to execute an analysis order based on the relevance.

[0044] The feedback unit can apply different feedback means depending on the category of driving behavior when providing feedback. For example, the feedback unit can have the AI ​​provide feedback using a voice alert in response to sudden braking. The feedback unit can also have the AI ​​provide feedback using a visual alert in response to sudden acceleration. The feedback unit can also have the AI ​​provide feedback using seat vibration in response to lane departure. This allows for more appropriate feedback to be provided by applying different feedback means depending on the category of driving behavior. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input driving behavior data to the generation AI and cause the generation AI to apply a feedback means depending on the category.

[0045] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the driver's past feedback results. For example, the feedback unit uses an AI to adjust the content of the feedback based on feedback provided by the driver in the past. The feedback unit can also improve the feedback method by taking into account problems pointed out by the driver in the past. The feedback unit can also analyze the driver's past feedback results and suggest the optimal feedback method. In this way, the accuracy of the feedback is improved by referring to the past feedback results. Some or all of the above-mentioned processing in the feedback unit may be performed using an AI, for example, or may be performed without using an AI. For example, the feedback unit can input past feedback data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0046] The feedback unit can determine the priority of feedback based on the time of occurrence of the driving behavior when providing feedback. For example, the feedback unit allows the AI ​​to prioritize feedback for driving behaviors that occurred recently. The feedback unit can also allow the AI ​​to prioritize feedback for driving behaviors that occurred in a specific time period. The feedback unit can also allow the AI ​​to prioritize feedback for driving behaviors that occurred frequently in the past. In this way, by determining the priority of feedback based on the time of occurrence of the driving behavior, more effective feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input driving behavior data to the generation AI and cause the generation AI to prioritize feedback based on the time of occurrence.

[0047] The feedback unit can adjust the order of feedback based on the relevance of driving behaviors when providing feedback. For example, the feedback unit adjusts the order of feedback using an AI, taking into account the relevance between sudden braking and sudden acceleration. The feedback unit can also adjust the order of feedback using an AI, taking into account the relevance between lane departure and sudden braking. The feedback unit can also adjust the order of feedback using an AI, taking into account the relevance of specific driving behaviors. In this way, by adjusting the order of feedback based on the relevance of driving behaviors, more effective feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, or without, an AI. For example, the feedback unit can input driving behavior data to a generation AI and cause the generation AI to execute a feedback order based on the relevance.

[0048] The feedback unit can adjust the use of technical terminology in the feedback depending on the driver's level of expertise when providing feedback. For example, if the driver has technical expertise, the feedback unit can have the AI ​​provide feedback using technical terminology. Alternatively, if the driver does not have technical expertise, the feedback unit can have the AI ​​provide feedback in simple language. Alternatively, the feedback unit can adjust the content of the feedback depending on the driver's level of expertise. As a result, by adjusting the use of technical terminology depending on the driver's level of expertise, it is possible to provide feedback that is easier to understand. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or can be performed without using AI. For example, the feedback unit can input the driver's level of expertise to the generation AI and have the generation AI adjust the use of technical terminology.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The analysis unit can take weather data into account when evaluating a driver's driving behavior. For example, it can relax the evaluation criteria for sudden braking in rainy weather and tighten them in sunny weather. In addition, since the risk of sudden acceleration and braking increases on snowy or icy roads, it can evaluate these behaviors more strictly. Furthermore, since the risk of lane departure increases in bad weather such as fog or strong winds, it can tighten the criteria for detecting lane departure. This makes it possible to evaluate driving behavior appropriately according to weather conditions.

[0051] The analysis unit can take vehicle maintenance data into account when evaluating a driver's driving behavior. For example, it can monitor the wear condition of brake pads and tire pressure, and if these conditions are poor, it can relax the evaluation criteria for sudden braking and sudden acceleration. Also, if the engine oil is past its due date for a change, engine performance will decline, so the evaluation of driving behavior can be adjusted. Furthermore, if the vehicle's maintenance history indicates that a specific part is deteriorating, it can tighten the evaluation of driving behavior related to that part. This makes it possible to evaluate driving behavior appropriately according to the vehicle's maintenance status.

[0052] The feedback unit can take the driver's driving experience into consideration when providing feedback on the driver's driving behavior. For example, it can provide feedback on basic driving techniques to a novice driver, and feedback on more advanced driving techniques to an experienced driver. It can also provide feedback that provides detailed information about things to be careful of while driving and tips for safe driving to a novice driver, and feedback that specifically points out areas for improvement in driving behavior to an experienced driver. Furthermore, it can adjust the frequency and content of the feedback depending on the driver's driving experience. This makes it possible to provide appropriate feedback according to the driver's driving experience.

[0053] The analysis unit can take into account the movements of other vehicles when evaluating a driver's driving behavior. For example, if a surrounding vehicle suddenly brakes, the evaluation criteria for the driver's sudden braking can be relaxed. Also, if a surrounding vehicle suddenly accelerates, the evaluation criteria for the driver's sudden acceleration can be relaxed. Furthermore, if a surrounding vehicle changes lanes, the evaluation criteria for the driver's lane departure can be adjusted. This makes it possible to evaluate appropriate driving behavior by taking into account the movements of other vehicles.

[0054] The collection unit can take the driver's driving style into consideration when collecting the driver's behavior data. For example, if the driver's driving style is one in which sudden braking is common, detailed data on sudden braking can be collected. Also, if the driver's driving style is one in which sudden acceleration is common, detailed data on sudden acceleration can be collected. Furthermore, if the driver's driving style is one in which frequent lane changes are common, detailed data on lane changes can be collected. This makes it possible to collect appropriate data according to the driver's driving style.

[0055] The feedback unit can take into consideration the driver's driving goals when providing feedback on the driver's driving behavior. For example, if the driver's goal is to improve fuel efficiency, the feedback unit can provide feedback on fuel efficiency. Also, if the driver's goal is safe driving, the feedback unit can provide feedback on safe driving. Furthermore, if the driver's goal is comfortable driving, the feedback unit can provide feedback on comfortable driving. This makes it possible to provide appropriate feedback according to the driver's driving goals.

[0056] The feedback unit can take the driver's driving history into consideration when providing feedback on the driver's driving behavior. For example, if the driver has frequently braked suddenly in the past, the feedback unit can provide feedback on sudden braking with emphasis. Also, if the driver has frequently accelerated suddenly in the past, the feedback unit can provide feedback on sudden acceleration with emphasis. Furthermore, if the driver has frequently changed lanes in the past, the feedback unit can provide feedback on lane changes with emphasis. This makes it possible to provide appropriate feedback according to the driver's driving history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects driver behavior data. The driver behavior data includes gaze data, steering wheel operation data, pedal operation data, etc. The collection unit collects gaze data using an eye tracking camera, collects steering wheel operation data using a steering wheel operation sensor, and collects pedal operation data using a pedal operation sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the driver's driving behavior. The analysis is performed using statistical analysis of the data and machine learning algorithms to detect risky driving behavior such as sudden braking, sudden acceleration, and lane departure. It can also evaluate the safety and efficiency of the driver's driving behavior and predict future driving behavior based on past driving data. Step 3: The feedback unit provides real-time feedback to the driver based on the analysis results obtained by the analysis unit. Feedback is provided through audio alerts, visual alerts, and vibrations of the seat and steering wheel. For example, if sudden braking is detected, an audio alert will alert the driver, a warning message will be displayed through a visual alert, and physical feedback will be provided through vibrations of the seat and steering wheel.

[0059] (Example 2) A safe driving support system according to an embodiment of the present invention collects driver behavior data, analyzes it using AI, and provides real-time feedback. The safe driving support system collects driver behavior data, analyzes it using AI, and evaluates the driver's driving behavior. For example, it can detect dangerous driving behaviors such as sudden braking, sudden acceleration, and lane departure. Furthermore, it provides real-time feedback to the driver based on the analysis results. For example, it can alert the driver through audio or visual alerts, or vibrations of the seat or steering wheel. This supports safe driving and reduces the risk of traffic accidents. For example, the safe driving support system collects driver behavior data from an eye-tracking camera, steering wheel operation sensors, pedal operation sensors, and the like installed in a vehicle. Then, the safe driving support system uses AI to analyze the collected data and evaluate the driver's driving behavior. For example, it can detect dangerous driving behaviors such as sudden braking, sudden acceleration, and lane departure. Furthermore, the safe driving support system provides real-time feedback to the driver based on the analysis results. For example, it can alert the driver through audio or visual alerts, or vibrations of the seat or steering wheel. This allows the driver to immediately correct dangerous driving behaviors. This allows the safe driving support system to support the driver in safe driving and reduce the risk of traffic accidents. This allows the safe driving support system to monitor the driver's behavior in real time and provide appropriate feedback. For example, if the driver deviates from their lane, an audio alert can be issued to encourage the driver to return to their lane. In addition, if sudden braking or sudden acceleration is detected, a visual alert can be issued to encourage the driver to continue driving safely. This reduces the risk of traffic accidents and improves road safety.

[0060] A safe driving support system according to an embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects driver behavior data. The driver behavior data includes, but is not limited to, gaze data, steering wheel operation data, and pedal operation data. The collection unit collects the driver's gaze data using, for example, an eye-tracking camera. The collection unit can also collect steering wheel operation data using a steering wheel operation sensor. The collection unit can also collect pedal operation data using a pedal operation sensor. For example, the eye-tracking camera tracks the driver's gaze movement with high accuracy to collect gaze data. The steering wheel operation sensor measures the rotation angle and operation force of the steering wheel to collect steering wheel operation data. The pedal operation sensor measures the operation amount of the brake pedal and accelerator pedal to collect pedal operation data. The analysis unit analyzes the data collected by the collection unit and evaluates the driver's driving behavior. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit detects dangerous driving behaviors such as sudden braking, sudden acceleration, and lane departure based on the collected data. The analysis unit can also evaluate the safety and efficiency of the driver's driving behavior. The analysis unit can also predict the driver's driving behavior using a machine learning algorithm. For example, the analysis unit predicts future driving behavior based on past driving data and detects dangerous driving behavior in advance. The feedback unit provides feedback to the driver in real time based on the analysis results obtained by the analysis unit. The feedback can be, for example, an audio alert, a visual alert, or vibration of the seat or steering wheel, but is not limited to these examples. For example, the feedback unit can alert the driver to the driver's attention using an audio alert. The feedback unit can also alert the driver to ...The vibration of the seat and steering wheel provides physical feedback to the driver to alert them. This allows the safe driving support system according to the embodiment to monitor the driver's behavior in real time and provide appropriate feedback. This supports the driver's safe driving and reduces the risk of traffic accidents.

[0061] The collection unit can collect driver behavior data using an eye-tracking camera, a steering wheel operation sensor, and a pedal operation sensor. The eye-tracking camera, for example, tracks the driver's eye movement with high precision and collects gaze data. The resolution and installation position of the eye-tracking camera are adjusted to accurately track the driver's gaze. The steering wheel operation sensor, for example, measures the steering wheel rotation angle and operation force to collect steering wheel operation data. The sensitivity and installation position of the steering wheel operation sensor are adjusted to accurately measure the driver's steering operation. The pedal operation sensor, for example, measures the amount of brake pedal or accelerator pedal operation to collect pedal operation data. The sensitivity and installation position of the pedal operation sensor are adjusted to accurately measure the driver's pedal operation. In this way, detailed behavior data can be collected by using the eye-tracking camera, the steering wheel operation sensor, and the pedal operation sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input gaze data acquired by the eye-tracking camera to a generation AI and have the generation AI analyze the gaze data.

[0062] The analysis unit can detect dangerous driving behaviors such as sudden braking, sudden acceleration, and lane departure based on the collected data. The analysis unit detects sudden braking based on, for example, the collected data. Sudden braking is detected based on the speed at which the brake pedal is operated and the deceleration rate of the vehicle. For example, the analysis unit detects sudden braking when the brake pedal is suddenly operated or the vehicle suddenly decelerates. The analysis unit can also detect sudden acceleration based on the collected data. Sudden acceleration is detected based on the speed at which the accelerator pedal is operated and the acceleration rate of the vehicle. For example, the analysis unit detects sudden acceleration when the accelerator pedal is suddenly operated or the vehicle suddenly accelerates. The analysis unit can also detect lane departure based on the collected data. Lane departure is detected based on vehicle position data and lane position data. For example, the analysis unit detects lane departure when the vehicle deviates from its lane. This allows the driver's driving behavior to be evaluated by detecting dangerous driving behavior. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI detect sudden braking, sudden acceleration, and lane departure.

[0063] The feedback unit can alert the driver through audio alerts, visual alerts, and vibrations of the seat or steering wheel. The feedback unit can alert the driver using, for example, audio alerts. Audio alerts are used to warn the driver of dangerous driving behavior. For example, the feedback unit issues an audio alert such as "Sudden braking. Be careful" when it detects sudden braking. The feedback unit can also alert the driver using visual alerts. The visual alert alerts alert the driver by displaying a warning message on a display. For example, the feedback unit displays a message such as "Lane departure. Be careful" when it detects lane departure. The feedback unit can also alert the driver using vibrations of the seat or steering wheel. Vibrations of the seat or steering wheel provide physical feedback to the driver to alert them. For example, the feedback unit vibrates the seat or steering wheel when it detects sudden acceleration. This allows feedback to be provided in real time by alerting the driver through audio alerts, visual alerts, and vibrations of the seat or steering wheel. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the analysis results obtained by the analysis unit into the generation AI and have the generation AI select an appropriate feedback method.

[0064] The collection unit can estimate the driver's emotions and adjust the timing of behavioral data collection based on the estimated driver's emotions. For example, if the driver is feeling stressed, the collection unit uses AI to estimate the driver's emotions and temporarily suspend data collection until the stress is reduced. Furthermore, if the driver is relaxed, the collection unit can also use AI to estimate the driver's emotions and collect detailed behavioral data while the driver remains relaxed. Furthermore, if the driver is tired, the collection unit can also use AI to estimate the driver's emotions and collect data before the driver's fatigue peaks, encouraging the driver to take a break at an appropriate time. This allows for more appropriate data collection by adjusting the collection timing based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the driver's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The collection unit can analyze the driver's past driving history and select an appropriate data collection method. For example, if the driver has a history of frequently braking suddenly in the past, the collection unit can have the AI ​​analyze that pattern and collect detailed data at the time of the sudden braking. Furthermore, if the driver has a history of engaging in dangerous driving behavior during a specific time period in the past, the collection unit can also have the AI ​​focus on collecting data during that time period. Furthermore, if the driver has a history of engaging in dangerous driving behavior on a specific road in the past, the collection unit can also collect detailed data when the AI ​​passes through that road. This allows the optimal data collection method to be selected by analyzing the past driving history. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the driver's past driving data into the generation AI and have the generation AI select the optimal data collection method.

[0066] When collecting behavioral data, the collection unit can filter the data based on the driver's current driving situation and environment. For example, if the driver is driving on a highway, the collection unit uses an AI to detect the situation and prioritize collecting highway-specific data. Furthermore, if the driver is stuck in traffic, the collection unit can use an AI to detect the situation and collect detailed driving behavior data during traffic jams. Furthermore, if the driver is driving at night, the collection unit can use an AI to detect the situation and collect nighttime-specific driving behavior data. This allows for more relevant data to be collected by filtering data based on the current driving situation and environment. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit can input the driver's driving situation data to a generation AI and have the generation AI perform filtering.

[0067] When collecting behavioral data, the collection unit can select an appropriate collection means depending on the driver's input method. For example, if the driver uses voice input, the collection unit has an AI analyze the voice command and collect related behavioral data. Furthermore, if the driver uses gesture input, the collection unit can have an AI recognize the gesture and collect related behavioral data. Furthermore, if the driver uses gaze input, the collection unit can have an AI track gaze movements and collect related behavioral data. This enables more accurate data collection by selecting the optimal collection means depending on the driver's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the driver's voice data to a generation AI and have the generation AI analyze the voice command.

[0068] The collection unit can estimate the driver's emotions and prioritize data to be collected based on the estimated driver's emotions. For example, if the driver is tense, the collection unit uses AI to estimate the driver's emotions and prioritize collecting data related to the tense state. Alternatively, if the driver is relaxed, the collection unit can use AI to estimate the driver's emotions and prioritize collecting data related to the relaxed state. Alternatively, if the driver is tired, the collection unit can use AI to estimate the driver's emotions and prioritize collecting data related to the tired state. This allows important data to be prioritized by prioritizing data based on the driver's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input the driver's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the driver's geographical location information. For example, when the driver passes through a specific dangerous area, the collection unit causes the AI ​​to prioritize collecting data related to that area. Furthermore, when the driver passes through a specific intersection, the collection unit can also prioritize collecting data related to that intersection. Furthermore, when the driver drives on a specific road, the collection unit can also prioritize collecting data related to that road. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the driver's geographical location data to the generation AI and cause the generation AI to select highly relevant data.

[0070] When collecting behavioral data, the collection unit can analyze the driver's social media activities and collect related data. For example, the collection unit collects data related to the locations where the driver checked in on social media. The collection unit can also analyze the content of the driver's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the driver's friends on social media. In this way, related data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the driver's social media data into the generation AI and cause the generation AI to collect related data.

[0071] When collecting behavioral data, the collection unit can customize the collection method by reflecting the driver's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the driver in the past. The collection unit can also improve the collection method by taking into account problems pointed out by the driver in the past. The collection unit can also analyze the driver's past feedback and propose an optimal collection method. In this way, the collection method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the driver's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0072] The analysis unit can estimate the driver's emotions and adjust the evaluation criteria for driving behavior based on the estimated driver's emotions. For example, if the driver is tense, the analysis unit uses an AI to estimate the driver's emotions and set evaluation criteria according to the driver's state of tension. Furthermore, if the driver is relaxed, the analysis unit can also use an AI to estimate the driver's emotions and set evaluation criteria according to the driver's state of relaxation. Furthermore, if the driver is tired, the analysis unit can also use an AI to estimate the driver's emotions and set evaluation criteria according to the driver's state of fatigue. This allows for more appropriate evaluation of driving behavior by adjusting the evaluation criteria based on the driver's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the driver's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the driving behavior. For example, the analysis unit allows the AI ​​to perform a detailed analysis of dangerous driving behaviors such as sudden braking. The analysis unit can also allow the AI ​​to perform a simplified analysis of minor driving behaviors. The analysis unit can also allow the AI ​​to perform a detailed analysis of important driving behaviors and generate a detailed report. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the driving behavior. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input driving behavior data to a generation AI and cause the generation AI to perform an analysis with a level of detail according to the importance.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of driving behavior. For example, the analysis unit can have the AI ​​apply a specific analysis algorithm to sudden braking. The analysis unit can also have the AI ​​apply a different analysis algorithm to sudden acceleration. The analysis unit can also have the AI ​​apply a dedicated analysis algorithm to lane departure. This allows for more accurate analysis by applying different analysis algorithms depending on the category of driving behavior. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input driving behavior data to the generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the driver's past driving data. The analysis unit, for example, uses AI to improve the accuracy of the analysis based on the driver's past driving data. The analysis unit can also analyze the driver's past driving patterns and use AI to improve the accuracy of the analysis. The analysis unit can also use AI to improve the accuracy of the analysis by referring to the driver's past driving history. In this way, the accuracy of the analysis is improved by referring to the past driving data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past driving data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0076] The analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated driver's emotions. For example, if the driver is nervous, the analysis unit uses AI to estimate the driver's emotions and provide a simple, highly visible display method. Alternatively, if the driver is relaxed, the analysis unit can use AI to estimate the driver's emotions and provide a display method that includes detailed information. Alternatively, if the driver is tired, the analysis unit can use AI to estimate the driver's emotions and provide a simple, easy-to-understand display method. This allows for more appropriate analysis results to be displayed by adjusting the display method based on the driver's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the driver's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of occurrence of driving behavior. For example, the analysis unit allows the AI ​​to prioritize analysis of driving behaviors that occurred recently. The analysis unit can also allow the AI ​​to prioritize analysis of driving behaviors that occurred during a specific time period. The analysis unit can also allow the AI ​​to prioritize analysis of driving behaviors that occurred frequently in the past. This enables efficient analysis by determining the priority of analysis based on the time of occurrence of driving behavior. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input driving behavior data to the generation AI and cause the generation AI to perform analysis priorities based on the time of occurrence.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of driving behaviors. For example, the analysis unit adjusts the order of analysis by the AI, taking into account the relevance between sudden braking and sudden acceleration. The analysis unit can also adjust the order of analysis by the AI, taking into account the relevance between lane departure and sudden braking. The analysis unit can also adjust the order of analysis by the AI, taking into account the relevance of specific driving behaviors. In this way, adjusting the order of analysis based on the relevance of driving behaviors enables more relevant analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input driving behavior data to a generation AI and cause the generation AI to execute an analysis order based on the relevance.

[0079] The feedback unit can estimate the driver's emotions and adjust the way feedback is expressed based on the estimated driver's emotions. For example, if the driver is nervous, the feedback unit can use AI to estimate the driver's emotions and provide voice feedback in a calm voice. Alternatively, if the driver is relaxed, the feedback unit can use AI to estimate the driver's emotions and provide voice feedback in a cheerful voice. Alternatively, if the driver is tired, the feedback unit can use AI to estimate the driver's emotions and provide concise, easy-to-understand feedback. This allows for more effective feedback by adjusting the way feedback is expressed based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the driver's facial expression data into the generation AI and cause the generation AI to estimate the driver's emotions.

[0080] The feedback unit can apply different feedback means depending on the category of driving behavior when providing feedback. For example, the feedback unit can have the AI ​​provide feedback using a voice alert in response to sudden braking. The feedback unit can also have the AI ​​provide feedback using a visual alert in response to sudden acceleration. The feedback unit can also have the AI ​​provide feedback using seat vibration in response to lane departure. This allows for more appropriate feedback to be provided by applying different feedback means depending on the category of driving behavior. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input driving behavior data to the generation AI and cause the generation AI to apply a feedback means depending on the category.

[0081] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the driver's past feedback results. For example, the feedback unit uses an AI to adjust the content of the feedback based on feedback provided by the driver in the past. The feedback unit can also improve the feedback method by taking into account problems pointed out by the driver in the past. The feedback unit can also analyze the driver's past feedback results and suggest the optimal feedback method. In this way, the accuracy of the feedback is improved by referring to the past feedback results. Some or all of the above-mentioned processing in the feedback unit may be performed using an AI, for example, or may be performed without using an AI. For example, the feedback unit can input past feedback data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0082] The feedback unit can estimate the driver's emotions and adjust the length of the feedback based on the estimated driver's emotions. For example, if the driver is nervous, the feedback unit can use AI to estimate the driver's emotions and provide short, to-the-point feedback. Alternatively, if the driver is relaxed, the feedback unit can use AI to estimate the driver's emotions and provide detailed feedback. Alternatively, if the driver is tired, the feedback unit can use AI to estimate the driver's emotions and provide concise, easy-to-understand feedback. This allows for more effective feedback by adjusting the length of the feedback based on the driver's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the driver's facial expression data into the generation AI and cause the generation AI to estimate the driver's emotions.

[0083] The feedback unit can determine the priority of feedback based on the time of occurrence of the driving behavior when providing feedback. For example, the feedback unit allows the AI ​​to prioritize feedback for driving behaviors that occurred recently. The feedback unit can also allow the AI ​​to prioritize feedback for driving behaviors that occurred in a specific time period. The feedback unit can also allow the AI ​​to prioritize feedback for driving behaviors that occurred frequently in the past. In this way, by determining the priority of feedback based on the time of occurrence of the driving behavior, more effective feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input driving behavior data to the generation AI and cause the generation AI to prioritize feedback based on the time of occurrence.

[0084] The feedback unit can adjust the order of feedback based on the relevance of driving behaviors when providing feedback. For example, the feedback unit adjusts the order of feedback using an AI, taking into account the relevance between sudden braking and sudden acceleration. The feedback unit can also adjust the order of feedback using an AI, taking into account the relevance between lane departure and sudden braking. The feedback unit can also adjust the order of feedback using an AI, taking into account the relevance of specific driving behaviors. In this way, by adjusting the order of feedback based on the relevance of driving behaviors, more effective feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, or without, an AI. For example, the feedback unit can input driving behavior data to a generation AI and cause the generation AI to execute a feedback order based on the relevance.

[0085] The feedback unit can adjust the use of technical terminology in the feedback depending on the driver's level of expertise when providing feedback. For example, if the driver has technical expertise, the feedback unit can have the AI ​​provide feedback using technical terminology. Alternatively, if the driver does not have technical expertise, the feedback unit can have the AI ​​provide feedback in simple language. Alternatively, the feedback unit can adjust the content of the feedback depending on the driver's level of expertise. As a result, by adjusting the use of technical terminology depending on the driver's level of expertise, it is possible to provide feedback that is easier to understand. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or can be performed without using AI. For example, the feedback unit can input the driver's level of expertise to the generation AI and have the generation AI adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect driver behavior data using the camera 42, steering wheel operation sensor, and pedal operation sensor of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the driver's driving behavior. The feedback unit is realized by the control unit 46A of the smart device 14 and provides real-time feedback to the driver through audio alerts, visual alerts, and vibrations of the seat and steering wheel. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect driver behavior data using the camera 42, steering wheel operation sensor, and pedal operation sensor of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the driver's driving behavior. The feedback unit is realized by the control unit 46A of the smart glasses 214 and provides real-time feedback to the driver through audio alerts, visual alerts, and vibrations of the seat and steering wheel. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect driver behavior data using the camera 42, steering wheel operation sensor, and pedal operation sensor of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the driver's driving behavior. The feedback unit is realized by the control unit 46A of the headset type terminal 314 and provides feedback to the driver in real time through audio alerts, visual alerts, and vibrations of the seat and steering wheel. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect driver behavior data using the camera 42, steering wheel operation sensor, and pedal operation sensor of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the driver's driving behavior. The feedback unit is realized by the control unit 46A of the robot 414 and provides real-time feedback to the driver through audio alerts, visual alerts, and vibrations of the seat and steering wheel.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The analysis unit can take weather data into account when evaluating a driver's driving behavior. For example, it can relax the evaluation criteria for sudden braking in rainy weather and tighten them in sunny weather. In addition, since the risk of sudden acceleration and braking increases on snowy or icy roads, it can evaluate these behaviors more strictly. Furthermore, since the risk of lane departure increases in bad weather such as fog or strong winds, it can tighten the criteria for detecting lane departure. This makes it possible to evaluate driving behavior appropriately according to weather conditions.

[0088] The collection unit can collect driver health data and reflect it in the evaluation of driving behavior. For example, it can monitor the driver's heart rate and blood pressure, and if abnormalities are detected, it can adjust the evaluation of driving behavior. Also, if the driver is fatigued, it can temporarily stop data collection and encourage the driver to take a break. Furthermore, if the driver is feeling stressed, it can provide feedback to reduce stress. This makes it possible to evaluate and provide feedback on driving behavior appropriate to the driver's health state.

[0089] The analysis unit can take vehicle maintenance data into account when evaluating a driver's driving behavior. For example, it can monitor the wear condition of brake pads and tire pressure, and if these conditions are poor, it can relax the evaluation criteria for sudden braking and sudden acceleration. Also, if the engine oil is past its due date for a change, engine performance will decline, so the evaluation of driving behavior can be adjusted. Furthermore, if the vehicle's maintenance history indicates that a specific part is deteriorating, it can tighten the evaluation of driving behavior related to that part. This makes it possible to evaluate driving behavior appropriately according to the vehicle's maintenance status.

[0090] The feedback unit can take the driver's driving experience into consideration when providing feedback on the driver's driving behavior. For example, it can provide feedback on basic driving techniques to a novice driver, and feedback on more advanced driving techniques to an experienced driver. It can also provide feedback that provides detailed information about things to be careful of while driving and tips for safe driving to a novice driver, and feedback that specifically points out areas for improvement in driving behavior to an experienced driver. Furthermore, it can adjust the frequency and content of the feedback depending on the driver's driving experience. This makes it possible to provide appropriate feedback according to the driver's driving experience.

[0091] The collection unit can estimate the driver's emotions and adjust the timing of feedback based on the estimated driver's emotions. For example, if the driver is tense, the AI ​​can estimate their emotions and temporarily delay feedback until the tension subsides. Alternatively, if the driver is relaxed, the AI ​​can estimate their emotions and provide feedback while the driver remains relaxed. Alternatively, if the driver is tired, the AI ​​can estimate their emotions and provide feedback before fatigue reaches its peak, encouraging the driver to take a break at the appropriate time. This allows for more effective feedback by adjusting the timing of feedback based on the driver's emotions.

[0092] The analysis unit can take into account the movements of other vehicles when evaluating a driver's driving behavior. For example, if a surrounding vehicle suddenly brakes, the evaluation criteria for the driver's sudden braking can be relaxed. Also, if a surrounding vehicle suddenly accelerates, the evaluation criteria for the driver's sudden acceleration can be relaxed. Furthermore, if a surrounding vehicle changes lanes, the evaluation criteria for the driver's lane departure can be adjusted. This makes it possible to evaluate appropriate driving behavior by taking into account the movements of other vehicles.

[0093] The collection unit can take the driver's driving style into consideration when collecting the driver's behavior data. For example, if the driver's driving style is one in which sudden braking is common, detailed data on sudden braking can be collected. Also, if the driver's driving style is one in which sudden acceleration is common, detailed data on sudden acceleration can be collected. Furthermore, if the driver's driving style is one in which frequent lane changes are common, detailed data on lane changes can be collected. This makes it possible to collect appropriate data according to the driver's driving style.

[0094] The feedback unit can take into consideration the driver's driving goals when providing feedback on the driver's driving behavior. For example, if the driver's goal is to improve fuel efficiency, the feedback unit can provide feedback on fuel efficiency. Also, if the driver's goal is safe driving, the feedback unit can provide feedback on safe driving. Furthermore, if the driver's goal is comfortable driving, the feedback unit can provide feedback on comfortable driving. This makes it possible to provide appropriate feedback according to the driver's driving goals.

[0095] The analysis unit can estimate the driver's emotions and predict driving behavior based on the estimated driver's emotions. For example, if the driver is tense, the AI ​​can estimate their emotions and predict driving behavior in a tense state. Also, if the driver is relaxed, the AI ​​can estimate their emotions and predict driving behavior in a relaxed state. Also, if the driver is tired, the AI ​​can estimate their emotions and predict driving behavior in a fatigued state. This allows for more appropriate evaluation and feedback of driving behavior by predicting driving behavior based on the driver's emotions.

[0096] The feedback unit can take the driver's driving history into consideration when providing feedback on the driver's driving behavior. For example, if the driver has frequently braked suddenly in the past, the feedback unit can provide feedback on sudden braking with emphasis. Also, if the driver has frequently accelerated suddenly in the past, the feedback unit can provide feedback on sudden acceleration with emphasis. Furthermore, if the driver has frequently changed lanes in the past, the feedback unit can provide feedback on lane changes with emphasis. This makes it possible to provide appropriate feedback according to the driver's driving history.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The collection unit collects driver behavior data. The driver behavior data includes gaze data, steering wheel operation data, pedal operation data, etc. The collection unit collects gaze data using an eye tracking camera, collects steering wheel operation data using a steering wheel operation sensor, and collects pedal operation data using a pedal operation sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the driver's driving behavior. The analysis is performed using statistical analysis of the data and machine learning algorithms to detect risky driving behavior such as sudden braking, sudden acceleration, and lane departure. It can also evaluate the safety and efficiency of the driver's driving behavior and predict future driving behavior based on past driving data. Step 3: The feedback unit provides real-time feedback to the driver based on the analysis results obtained by the analysis unit. Feedback is provided through audio alerts, visual alerts, and vibrations of the seat and steering wheel. For example, if sudden braking is detected, an audio alert will alert the driver, a warning message will be displayed through a visual alert, and physical feedback will be provided through vibrations of the seat and steering wheel.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0170] [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects driver behavior data; an analysis unit that analyzes the data collected by the collection unit and evaluates the driving behavior of the driver; a feedback unit that provides real-time feedback to the driver based on the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit Collecting driver behavior data using eye-tracking cameras, steering sensors, and pedal sensors 2. The system of claim 1.

3. The analysis unit Detect risky driving behaviors such as sudden braking, sudden acceleration, and lane departure based on collected data 2. The system of claim 1.

4. The feedback unit Alerts the driver through audio and visual alerts, seat and steering wheel vibrations 2. The system of claim 1.

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

2. The system of claim 1.

6. The collecting unit Analyze the driver's past driving history and select the appropriate data collection method 2. The system of claim 1.

7. The collecting unit When collecting behavioral data, it filters it based on the driver's current driving situation and environment.

2. The system of claim 1.

8. The collecting unit When collecting behavioral data, select the appropriate collection method depending on the driver's input method.

2. The system of claim 1.

9. The collecting unit Estimate the driver's emotions and prioritize the data to be collected based on the estimated driver's emotions 2. The system of claim 1.

10. The collecting unit When collecting behavioral data, the driver's geographic location is taken into account to prioritize the collection of relevant data.

2. The system of claim 1.

Images