system

Abstract

The system according to the embodiment aims to provide appropriate driving guidance in real time to drivers of vehicles without driving assistance functions. According to an embodiment, the system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects driving data. The analysis unit analyzes the driving data collected by the collection unit. The provision unit provides advice to the driver based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide appropriate driving guidance in real time to drivers of vehicles without driving assistance functions.

[0005] The system according to the embodiment aims to provide appropriate driving guidance in real time to drivers of vehicles without driving assistance functions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects driving data. The analysis unit analyzes the driving data collected by the collection unit. The provision unit provides advice 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 provide appropriate driving guidance in real time to drivers of vehicles without driving assistance functions. [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 driving instruction system according to an embodiment of the present invention provides driving instruction to drivers without driving assistance functions. This driving instruction system provides real-time lectures while the driver is driving to help improve the user's safe driving skills. The system customizes advice based on driving data, providing the driver with the feeling of having a driving school instructor nearby. For example, driving data is collected from sensors and cameras installed in the vehicle. Next, AI analyzes the collected data and evaluates the driver's driving situation in real time. For example, data such as speed, braking timing, and steering operation are analyzed. Based on the analysis results, the AI ​​provides appropriate advice to the driver, such as advice on avoiding sudden braking and instructions on maintaining an appropriate following distance. This allows the driver to improve their driving skills in real time. Furthermore, the system accumulates the driver's past driving data and can provide optimal advice to each individual driver. For example, for a driver who has frequently braked suddenly in the past, it can provide focused advice on braking timing. In this way, the driving instruction system can help improve the safe driving skills of drivers without driving assistance functions. This allows the driving instruction system to improve the driver's safe driving skills.

[0029] A driving training system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects driving data. The driving data includes, but is not limited to, speed, brake usage, and steering operation. The collection unit can collect driving data using, for example, cameras, speed sensors, and brake sensors arranged at the front, rear, left, and right sides of the vehicle. The collection unit can also estimate the driver's emotions and adjust the frequency of collecting driving data based on the estimated driver's emotions. The analysis unit analyzes the driving data collected by the collection unit. The analysis unit can analyze the driving data using, for example, deep learning to learn the driver's driving patterns. Deep learning has a neural network structure and can perform highly accurate analysis by learning from large amounts of data. The analysis unit can also estimate the driver's emotions and adjust the analysis algorithm based on the estimated driver's emotions. The provision unit provides advice to the driver based on the analysis results obtained by the analysis unit. The provision unit can provide advice to the driver through, for example, voice instructions or messages displayed on a display. The providing unit can also accumulate past driving data and provide optimal advice to each driver. This allows the driving training system according to the embodiment to improve the safe driving skills of the driver.

[0030] The collection unit may include cameras arranged at the front, rear, left and right sides of the vehicle, and speed or brake sensors. Examples of cameras include, but are not limited to, a front camera, a rear camera, and a wide-angle camera. For example, the collection unit may capture images of the front situation using a camera arranged at the front of the vehicle and the rear situation using a camera arranged at the rear. The collection unit may also capture images of the left and right situations using cameras arranged at the left and right sides of the vehicle. Examples of speed sensors include, but are not limited to, a GPS-based speed sensor and a wheel speed sensor. For example, the collection unit may measure the vehicle speed using a GPS-based speed sensor and the wheel rotation speed using a wheel speed sensor. Examples of brake sensors include, but are not limited to, a brake pressure sensor and a brake pedal position sensor. For example, the collection unit may measure the brake pressure using a brake pressure sensor and the brake pedal position using a brake pedal position sensor. This allows for the collection of driving data from multiple directions, enabling a more detailed understanding of the driving situation. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data obtained from a camera or sensor into the generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the driving data using deep learning to learn the driver's driving patterns. Deep learning includes, for example, a neural network structure, but is not limited to, this example. The analysis unit can analyze the driving data using, for example, a neural network to learn the driver's driving patterns. A neural network can perform highly accurate analysis by learning a large amount of data. The analysis unit can also use training data to learn the driver's driving patterns. The training data includes, for example, past driving data and simulation data, but is not limited to this example. The analysis unit can train the neural network using the training data to learn the driver's driving patterns. In this way, by using deep learning, the driver's driving patterns can be learned with high accuracy and appropriate advice can be provided. 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 the driving data to a generation AI and cause the generation AI to learn the driving patterns.

[0032] The providing unit can provide advice to the driver through voice instructions or a message displayed on a display. For example, voice synthesis technology is used for voice instructions, but this is not a limitation. The providing unit can provide advice to the driver through voice, for example, using voice synthesis technology. Voice synthesis technology is a technology that converts text data into voice, and can provide advice in a natural voice. The providing unit can also provide advice to the driver through a message displayed on a display. Examples of displays include, but are not limited to, text messages and graphical displays. For example, the providing unit can display a text message on a display to provide advice to the driver. The providing unit can also display a graphical display on a display to provide advice visually to the driver. By providing advice through voice or a display, the driver can receive the advice in real time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to generate advice.

[0033] The providing unit can accumulate past driving data and provide advice to each driver. Examples of past driving data include, but are not limited to, driving history and past driving patterns. For example, the providing unit can accumulate driving history and provide optimal advice to each driver based on the past driving data. The driving history includes a record of how the driver has driven in the past. The providing unit can analyze the driving history and understand the driver's driving pattern. The providing unit can also accumulate past driving patterns and provide optimal advice to each driver. The past driving patterns include the driver's past driving tendencies. The providing unit can analyze past driving patterns and understand the driver's driving tendencies. By providing optimal advice to each driver based on the past driving data, the providing unit can support the improvement of driving skills. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past driving data to a generation AI and cause the generation AI to generate optimal advice.

[0034] The collection unit can analyze the driver's past driving data and select optimal sensor placement. For example, if the past driving data indicates that caution is required in a specific direction, the collection unit can add a sensor in that direction. For example, if the past driving data indicates that the driver tends to be distracted when turning left, the collection unit can place an additional camera on the left side. Furthermore, if the past driving data indicates that data for a specific speed range is lacking, the collection unit can also strengthen sensors corresponding to that speed range. For example, if there is a lack of driving data on a highway, a speed sensor for measuring highway speeds can be added. Furthermore, if there is a lack of data for a specific driving situation in the past driving data, the collection unit can also add sensors corresponding to that situation. For example, if there is a lack of data for night driving, a camera for night driving can be added. This improves the accuracy of data collection by selecting optimal sensor placement based on past driving data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past driving data into a generation AI and cause the generation AI to select optimal sensor placement.

[0035] When collecting driving data, the collection unit can perform filtering based on the driver's current driving situation and environmental conditions. For example, the collection unit can prioritize collecting data from a forward camera because visibility is poor during rainy weather. The collection unit can also prioritize collecting data in dark places during nighttime driving. Furthermore, the collection unit can also prioritize collecting data from a speed sensor during highway driving. This allows important data to be collected preferentially through data filtering according to the driving situation and environmental conditions. Filtering criteria include, but are not limited to, the importance of the data and the type of environmental conditions. 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 data on driving situations and environmental conditions to a generation AI and have the generation AI perform filtering.

[0036] When collecting driving data, the collection unit can prioritize collecting highly relevant data by taking into account the driver's geographical location information. For example, the collection unit can prioritize collecting data from areas with high traffic volume in urban areas. The collection unit can also prioritize collecting data from areas with low speed limits in suburban areas. Furthermore, the collection unit can prioritize collecting data from areas with many curves in mountainous areas. This improves the usefulness of the data by preferentially collecting highly relevant data based on the geographical location information. Examples of geographical location information include, but are not limited to, GPS data and map information. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to preferentially collect highly relevant data.

[0037] The collection unit can analyze the driver's social media activities and collect related data when collecting driving data. For example, if the driver posts "traffic jam" on social media, the collection unit can prioritize collecting data on traffic jams. Furthermore, if the driver posts "rain" on social media, the collection unit can prioritize collecting data on rainy days. Furthermore, if the driver posts "night" on social media, the collection unit can prioritize collecting data on night driving. This allows data collection based on the driver's social media activities, enabling data collection according to the driver's situation. Social media activities include, but are not limited to, for example, the content of posts and the frequency of activities. Some or all of the above-described 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 social media data into a generation AI and cause the generation AI to collect related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the driving data. For example, if speed data is important, the analysis unit can perform a detailed analysis and provide advice regarding speed. Furthermore, if braking data is important, the analysis unit can perform a detailed analysis and provide advice regarding braking. Furthermore, if steering operation data is important, the analysis unit can perform a detailed analysis and provide advice regarding steering. In this way, by adjusting the level of detail of the analysis according to the importance of the driving data, analysis of important data is strengthened. The importance of driving data includes, but is not limited to, accident risk and driving performance, for example. 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 data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of driving data. For example, the analysis unit can apply a speed analysis algorithm to speed data. The analysis unit can also apply a brake analysis algorithm to braking data. The analysis unit can also apply a steering operation analysis algorithm to steering operation data. This improves the accuracy of analysis by applying an analysis algorithm depending on the category of driving data. Categories of driving data include, but are not limited to, speed data, braking data, and steering operation data. 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 data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0040] During analysis, the analysis unit can determine the analysis priority based on the collection time of the driving data. For example, the analysis unit can prioritize analysis of recently collected data. The analysis unit can also prioritize analysis of current data while referring to past data. Furthermore, the analysis unit can prioritize analysis of data collected during a specific time period. This allows the latest data to be analyzed preferentially by determining the analysis priority based on the collection time. Examples of collection time include, but are not limited to, time periods, seasons, and specific events. 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 the collection time of the driving data to the generation AI and have the generation AI determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of driving data. For example, if speed data and braking data are highly relevant, the analysis unit can prioritize analyzing these data. Furthermore, if steering operation data and speed data are highly relevant, the analysis unit can prioritize analyzing these data. Furthermore, if braking data and steering operation data are highly relevant, the analysis unit can prioritize analyzing these data. This enables efficient analysis by adjusting the analysis order based on the relevance of data. Data relevance includes, but is not limited to, correlation and causal relationship, for example. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of driving data to the generation AI and cause the generation AI to adjust the analysis order.

[0042] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the driving data. For example, if speed data is important, the providing unit can provide detailed advice. Furthermore, if braking data is important, the providing unit can also provide detailed advice. Furthermore, if steering operation data is important, the providing unit can also provide detailed advice. As a result, appropriate advice is provided by adjusting the level of detail of the advice according to the importance of the driving data. The level of detail of the advice includes, but is not limited to, detailed explanations and concise instructions, for example. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI, for example. For example, the providing unit can input driving data to a generating AI and cause the generating AI to adjust the level of detail of the advice.

[0043] When providing advice, the providing unit can apply different advice algorithms depending on the category of driving data. For example, the providing unit can apply a speed advice algorithm to speed data. The providing unit can also apply a braking advice algorithm to braking data. Furthermore, the providing unit can also apply a steering advice algorithm to steering operation data. As a result, appropriate advice is provided by applying an advice algorithm depending on the category of driving data. Advice algorithms include, but are not limited to, rule-based algorithms and machine learning algorithms. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input driving data to a generation AI and cause the generation AI to apply an advice algorithm depending on the category.

[0044] When providing advice, the providing unit can determine the priority of advice based on the collection time of driving data. The providing unit can provide advice based on, for example, recently collected data. The providing unit can also provide advice based on current data while referring to past data. Furthermore, the providing unit can provide advice based on data collected during a specific time period. As a result, advice based on the latest data is provided by determining the priority of advice based on the collection time. The priority of advice includes, for example, the importance of data and the collection time, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collection time of driving data to the generating AI and cause the generating AI to determine the priority of advice.

[0045] When providing advice, the providing unit can adjust the order of advice based on the relevance of driving data. For example, if speed data and braking data are highly relevant, the providing unit can provide advice based on these data. Furthermore, if steering operation data and speed data are highly relevant, the providing unit can also provide advice based on these data. Furthermore, if braking data and steering operation data are highly relevant, the providing unit can also provide advice based on these data. In this way, efficient advice is provided by adjusting the order of advice based on the relevance of data. The order of advice includes, for example, data relevance and importance of advice, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of driving data to a generating AI and cause the generating AI to adjust the order of advice.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The driving instruction system may further include a style learning unit that learns the driver's driving style. The style learning unit may learn, for example, the driver's acceleration and deceleration patterns and steering characteristics, and provide advice according to each individual driving style. For example, a driver who frequently accelerates suddenly may be provided with advice encouraging smoother acceleration. A driver who handles the steering wheel roughly may be provided with advice recommending more gentle steering. Furthermore, the system may provide advice for improving fuel efficiency based on the driver's driving style. This makes it possible to provide customized advice according to each driver's individual driving style, which is expected to improve driving skills.

[0048] The driving training system can further include a history analysis unit that analyzes the driver's driving history. The history analysis unit can, for example, analyze the driver's driving tendencies based on past driving data and support the long-term improvement of driving skills. For example, if many sudden braking incidents have been recorded in the past, it can provide advice focused on braking timing. Also, for a driver who is not good at driving on a particular road, it can provide detailed advice about that road. Furthermore, it can evaluate the progress of the driver's driving skill based on the driver's driving history and provide feedback to increase motivation. This can be expected to improve the driver's driving skill in the long term.

[0049] The driving guidance system may further include an environment monitoring unit that monitors the driver's driving environment. The environment monitoring unit can collect external environmental data, such as weather, road conditions, and traffic volume, and provide appropriate advice to the driver. For example, it can provide advice on reducing the risk of slipping when it is raining. It can also suggest relaxation methods to reduce stress when stuck in traffic. It can also provide advice on maintaining visibility when driving at night. This provides appropriate advice according to the driving environment, thereby strengthening support for safe driving.

[0050] The driving instruction system may further include a posture monitoring unit that monitors the driver's posture while driving. The posture monitoring unit may monitor, for example, the driver's sitting posture, back angle, shoulder position, etc., and provide advice to maintain proper posture. For example, if the driver's back is hunched, advice to encourage correct posture may be provided. Also, if the shoulders are raised, it may suggest stretches to relax. Furthermore, if the sitting posture is unnatural, it may suggest seat adjustments. This helps maintain the driver's posture properly and reduces fatigue caused by long hours of driving.

[0051] The driving instruction system may further include a visual enhancement unit that enhances the driver's visual information while driving. The visual enhancement unit may use, for example, augmented reality (AR) technology to display important information in the driver's field of vision. For example, the visual enhancement unit may overlay information such as the distance to the vehicle ahead, speed limits, and road signs on the driver's field of vision. It may also provide auxiliary lights to improve visibility in dark places when driving at night. It may also provide information to maintain clear visibility in bad weather. This enhances the driver's visual information and supports safe driving.

[0052] The driving instruction system may further include a communication support unit that supports communication with the driver while driving. The communication support unit can, for example, provide voice messages at appropriate times when the driver communicates with other vehicles or pedestrians. For example, the communication support unit can provide a message to pedestrians to warn them when turning right at an intersection. It can also provide a message to signal vehicles behind when changing lanes. Furthermore, it can provide a message to warn them of surrounding vehicles and obstacles when parking. This supports communication between the driver and enhances safe driving.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects driving data. The driving data includes speed, brake usage, steering operation, etc. The collection unit collects driving data using cameras, speed sensors, and brake sensors located at the front, rear, left and right of the vehicle. The collection unit can also estimate the driver's emotions and adjust the frequency of collecting driving data based on the estimated driver's emotions. Step 2: The analysis unit analyzes the driving data collected by the collection unit. The analysis unit uses deep learning to analyze the driving data and learn the driver's driving patterns. Deep learning has a neural network structure, and by learning large amounts of data, it can perform highly accurate analysis. The analysis unit can also estimate the driver's emotions and adjust the analysis algorithm based on the estimated driver's emotions. Step 3: The provider provides advice to the driver based on the analysis results obtained by the analyzer. The provider provides advice to the driver through voice instructions and messages displayed on the display. The provider can also accumulate past driving data and provide optimal advice to individual drivers.

[0055] (Example 2) A driving instruction system according to an embodiment of the present invention provides driving instruction to drivers without driving assistance functions. This driving instruction system provides real-time lectures while the driver is driving to help improve the user's safe driving skills. The system customizes advice based on driving data, providing the driver with the feeling of having a driving school instructor nearby. For example, driving data is collected from sensors and cameras installed in the vehicle. Next, AI analyzes the collected data and evaluates the driver's driving situation in real time. For example, data such as speed, braking timing, and steering operation are analyzed. Based on the analysis results, the AI ​​provides appropriate advice to the driver, such as advice on avoiding sudden braking and instructions on maintaining an appropriate following distance. This allows the driver to improve their driving skills in real time. Furthermore, the system accumulates the driver's past driving data and can provide optimal advice to each individual driver. For example, for a driver who has frequently braked suddenly in the past, it can provide focused advice on braking timing. In this way, the driving instruction system can help improve the safe driving skills of drivers without driving assistance functions. This allows the driving instruction system to improve the driver's safe driving skills.

[0056] A driving training system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects driving data. The driving data includes, but is not limited to, speed, brake usage, and steering operation. The collection unit can collect driving data using, for example, cameras, speed sensors, and brake sensors arranged at the front, rear, left, and right sides of the vehicle. The collection unit can also estimate the driver's emotions and adjust the frequency of collecting driving data based on the estimated driver's emotions. The analysis unit analyzes the driving data collected by the collection unit. The analysis unit can analyze the driving data using, for example, deep learning to learn the driver's driving patterns. Deep learning has a neural network structure and can perform highly accurate analysis by learning from large amounts of data. The analysis unit can also estimate the driver's emotions and adjust the analysis algorithm based on the estimated driver's emotions. The provision unit provides advice to the driver based on the analysis results obtained by the analysis unit. The provision unit can provide advice to the driver through, for example, voice instructions or messages displayed on a display. The providing unit can also accumulate past driving data and provide optimal advice to each driver. This allows the driving training system according to the embodiment to improve the safe driving skills of the driver.

[0057] The collection unit may include cameras arranged at the front, rear, left and right sides of the vehicle, and speed or brake sensors. Examples of cameras include, but are not limited to, a front camera, a rear camera, and a wide-angle camera. For example, the collection unit may capture images of the front situation using a camera arranged at the front of the vehicle and the rear situation using a camera arranged at the rear. The collection unit may also capture images of the left and right situations using cameras arranged at the left and right sides of the vehicle. Examples of speed sensors include, but are not limited to, a GPS-based speed sensor and a wheel speed sensor. For example, the collection unit may measure the vehicle speed using a GPS-based speed sensor and the wheel rotation speed using a wheel speed sensor. Examples of brake sensors include, but are not limited to, a brake pressure sensor and a brake pedal position sensor. For example, the collection unit may measure the brake pressure using a brake pressure sensor and the brake pedal position using a brake pedal position sensor. This allows for the collection of driving data from multiple directions, enabling a more detailed understanding of the driving situation. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data obtained from a camera or sensor into the generation AI and have the generation AI analyze the data.

[0058] The analysis unit can analyze the driving data using deep learning to learn the driver's driving patterns. Deep learning includes, for example, a neural network structure, but is not limited to, this example. The analysis unit can analyze the driving data using, for example, a neural network to learn the driver's driving patterns. A neural network can perform highly accurate analysis by learning a large amount of data. The analysis unit can also use training data to learn the driver's driving patterns. The training data includes, for example, past driving data and simulation data, but is not limited to this example. The analysis unit can train the neural network using the training data to learn the driver's driving patterns. In this way, by using deep learning, the driver's driving patterns can be learned with high accuracy and appropriate advice can be provided. 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 the driving data to a generation AI and cause the generation AI to learn the driving patterns.

[0059] The providing unit can provide advice to the driver through voice instructions or a message displayed on a display. For example, voice synthesis technology is used for voice instructions, but this is not a limitation. The providing unit can provide advice to the driver through voice, for example, using voice synthesis technology. Voice synthesis technology is a technology that converts text data into voice, and can provide advice in a natural voice. The providing unit can also provide advice to the driver through a message displayed on a display. Examples of displays include, but are not limited to, text messages and graphical displays. For example, the providing unit can display a text message on a display to provide advice to the driver. The providing unit can also display a graphical display on a display to provide advice visually to the driver. By providing advice through voice or a display, the driver can receive the advice in real time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to generate advice.

[0060] The providing unit can accumulate past driving data and provide advice to each driver. Examples of past driving data include, but are not limited to, driving history and past driving patterns. For example, the providing unit can accumulate driving history and provide optimal advice to each driver based on the past driving data. The driving history includes a record of how the driver has driven in the past. The providing unit can analyze the driving history and understand the driver's driving pattern. The providing unit can also accumulate past driving patterns and provide optimal advice to each driver. The past driving patterns include the driver's past driving tendencies. The providing unit can analyze past driving patterns and understand the driver's driving tendencies. By providing optimal advice to each driver based on the past driving data, the providing unit can support the improvement of driving skills. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past driving data to a generation AI and cause the generation AI to generate optimal advice.

[0061] The collection unit can estimate the driver's emotions and adjust the frequency of driving data collection based on the estimated driver's emotions. Examples of the driver's emotions include, but are not limited to, tension, relaxation, fatigue, etc. For example, when the driver is tense, the collection unit can increase the collection frequency to collect detailed data. Furthermore, when the driver is relaxed, the collection unit can reduce the collection frequency to collect the minimum necessary data. Furthermore, when the driver is tired, the collection unit can set the collection frequency to a medium level and prioritize collecting important data. This enables more appropriate data collection by adjusting the data collection frequency according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI. For example, the collection unit can input the driver's emotion data into the generation AI and cause the generation AI to adjust the collection frequency.

[0062] The collection unit can analyze the driver's past driving data and select optimal sensor placement. For example, if the past driving data indicates that caution is required in a specific direction, the collection unit can add a sensor in that direction. For example, if the past driving data indicates that the driver tends to be distracted when turning left, the collection unit can place an additional camera on the left side. Furthermore, if the past driving data indicates that data for a specific speed range is lacking, the collection unit can also strengthen sensors corresponding to that speed range. For example, if there is a lack of driving data on a highway, a speed sensor for measuring highway speeds can be added. Furthermore, if there is a lack of data for a specific driving situation in the past driving data, the collection unit can also add sensors corresponding to that situation. For example, if there is a lack of data for night driving, a camera for night driving can be added. This improves the accuracy of data collection by selecting optimal sensor placement based on past driving data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past driving data into a generation AI and cause the generation AI to select optimal sensor placement.

[0063] When collecting driving data, the collection unit can perform filtering based on the driver's current driving situation and environmental conditions. For example, the collection unit can prioritize collecting data from a forward camera because visibility is poor during rainy weather. The collection unit can also prioritize collecting data in dark places during nighttime driving. Furthermore, the collection unit can also prioritize collecting data from a speed sensor during highway driving. This allows important data to be collected preferentially through data filtering according to the driving situation and environmental conditions. Filtering criteria include, but are not limited to, the importance of the data and the type of environmental conditions. 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 data on driving situations and environmental conditions to a generation AI and have the generation AI perform filtering.

[0064] The collection unit can estimate the driver's emotions and determine the priority of data to be collected based on the estimated driver's emotions. For example, if the driver is nervous, the collection unit can prioritize collecting brake sensor data. Furthermore, if the driver is relaxed, the collection unit can prioritize collecting speed sensor data. Furthermore, if the driver is tired, the collection unit can prioritize collecting steering operation data. Thus, by determining the priority of data according to the driver's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the driver's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0065] When collecting driving data, the collection unit can prioritize collecting highly relevant data by taking into account the driver's geographical location information. For example, the collection unit can prioritize collecting data from areas with high traffic volume in urban areas. The collection unit can also prioritize collecting data from areas with low speed limits in suburban areas. Furthermore, the collection unit can prioritize collecting data from areas with many curves in mountainous areas. This improves the usefulness of the data by preferentially collecting highly relevant data based on the geographical location information. Examples of geographical location information include, but are not limited to, GPS data and map information. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to preferentially collect highly relevant data.

[0066] The collection unit can analyze the driver's social media activities and collect related data when collecting driving data. For example, if the driver posts "traffic jam" on social media, the collection unit can prioritize collecting data on traffic jams. Furthermore, if the driver posts "rain" on social media, the collection unit can prioritize collecting data on rainy days. Furthermore, if the driver posts "night" on social media, the collection unit can prioritize collecting data on night driving. This allows data collection based on the driver's social media activities, enabling data collection according to the driver's situation. Social media activities include, but are not limited to, for example, the content of posts and the frequency of activities. Some or all of the above-described 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 social media data into a generation AI and cause the generation AI to collect related data.

[0067] The analysis unit can estimate the driver's emotions and adjust the analysis algorithm based on the estimated driver's emotions. For example, if the driver is nervous, the analysis unit can perform a detailed analysis and provide detailed advice. Furthermore, if the driver is relaxed, the analysis unit can perform a simple analysis and provide basic advice. Furthermore, if the driver is tired, the analysis unit can perform a moderate analysis and provide important advice. This allows for more appropriate analysis results to be obtained by adjusting the analysis algorithm according to 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the driver's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0068] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the driving data. For example, if speed data is important, the analysis unit can perform a detailed analysis and provide advice regarding speed. Furthermore, if braking data is important, the analysis unit can perform a detailed analysis and provide advice regarding braking. Furthermore, if steering operation data is important, the analysis unit can perform a detailed analysis and provide advice regarding steering. In this way, by adjusting the level of detail of the analysis according to the importance of the driving data, analysis of important data is strengthened. The importance of driving data includes, but is not limited to, accident risk and driving performance, for example. 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 data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0069] During analysis, the analysis unit can apply different analysis algorithms depending on the category of driving data. For example, the analysis unit can apply a speed analysis algorithm to speed data. The analysis unit can also apply a brake analysis algorithm to braking data. The analysis unit can also apply a steering operation analysis algorithm to steering operation data. This improves the accuracy of analysis by applying an analysis algorithm depending on the category of driving data. Categories of driving data include, but are not limited to, speed data, braking data, and steering operation data. 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 data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0070] 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 can provide a simple, highly visible display method. Furthermore, if the driver is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the driver is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it easier to understand the analysis results by adjusting the display method according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the driver's emotion data into the generation AI and have the generation AI adjust the display method.

[0071] During analysis, the analysis unit can determine the analysis priority based on the collection time of the driving data. For example, the analysis unit can prioritize analysis of recently collected data. The analysis unit can also prioritize analysis of current data while referring to past data. Furthermore, the analysis unit can prioritize analysis of data collected during a specific time period. This allows the latest data to be analyzed preferentially by determining the analysis priority based on the collection time. Examples of collection time include, but are not limited to, time periods, seasons, and specific events. 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 the collection time of the driving data to the generation AI and have the generation AI determine the analysis priority.

[0072] During analysis, the analysis unit can adjust the order of analysis based on the relevance of driving data. For example, if speed data and braking data are highly relevant, the analysis unit can prioritize analyzing these data. Furthermore, if steering operation data and speed data are highly relevant, the analysis unit can prioritize analyzing these data. Furthermore, if braking data and steering operation data are highly relevant, the analysis unit can prioritize analyzing these data. This enables efficient analysis by adjusting the analysis order based on the relevance of data. Data relevance includes, but is not limited to, correlation and causal relationship, for example. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of driving data to the generation AI and cause the generation AI to adjust the analysis order.

[0073] The providing unit can estimate the driver's emotions and adjust the way in which advice is expressed based on the estimated driver's emotions. For example, if the driver is nervous, the providing unit can provide advice in a calm voice. Furthermore, if the driver is relaxed, the providing unit can provide advice in a cheerful voice. Furthermore, if the driver is in a hurry, the providing unit can provide quick and concise advice. This improves the ease with which the advice is accepted by adjusting the way in which advice is expressed according to the driver's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the driver's emotion data into the generation AI and cause the generation AI to adjust the way in which advice is expressed.

[0074] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the driving data. For example, if speed data is important, the providing unit can provide detailed advice. Furthermore, if braking data is important, the providing unit can also provide detailed advice. Furthermore, if steering operation data is important, the providing unit can also provide detailed advice. As a result, appropriate advice is provided by adjusting the level of detail of the advice according to the importance of the driving data. The level of detail of the advice includes, but is not limited to, detailed explanations and concise instructions, for example. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI, for example. For example, the providing unit can input driving data to a generating AI and cause the generating AI to adjust the level of detail of the advice.

[0075] When providing advice, the providing unit can apply different advice algorithms depending on the category of driving data. For example, the providing unit can apply a speed advice algorithm to speed data. The providing unit can also apply a braking advice algorithm to braking data. Furthermore, the providing unit can also apply a steering advice algorithm to steering operation data. As a result, appropriate advice is provided by applying an advice algorithm depending on the category of driving data. Advice algorithms include, but are not limited to, rule-based algorithms and machine learning algorithms. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input driving data to a generation AI and cause the generation AI to apply an advice algorithm depending on the category.

[0076] The providing unit can estimate the driver's emotions and adjust the length of the advice based on the estimated driver's emotions. For example, if the driver is nervous, the providing unit can provide short and to-the-point advice. Furthermore, if the driver is relaxed, the providing unit can provide detailed advice. Furthermore, if the driver is in a hurry, the providing unit can provide quick and concise advice. This adjusts the length of the advice according to the driver's emotions, thereby improving the ease with which the advice is accepted. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the driver's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0077] When providing advice, the providing unit can determine the priority of advice based on the collection time of driving data. The providing unit can provide advice based on, for example, recently collected data. The providing unit can also provide advice based on current data while referring to past data. Furthermore, the providing unit can provide advice based on data collected during a specific time period. As a result, advice based on the latest data is provided by determining the priority of advice based on the collection time. The priority of advice includes, for example, the importance of data and the collection time, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collection time of driving data to the generating AI and cause the generating AI to determine the priority of advice.

[0078] When providing advice, the providing unit can adjust the order of advice based on the relevance of driving data. For example, if speed data and braking data are highly relevant, the providing unit can provide advice based on these data. Furthermore, if steering operation data and speed data are highly relevant, the providing unit can also provide advice based on these data. Furthermore, if braking data and steering operation data are highly relevant, the providing unit can also provide advice based on these data. In this way, efficient advice is provided by adjusting the order of advice based on the relevance of data. The order of advice includes, for example, data relevance and importance of advice, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of driving data to a generating AI and cause the generating AI to adjust the order of advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 driving data using the camera 42 or a speed sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the driving data using deep learning. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides advice to the driver through voice instructions or display on a screen. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 driving data using the camera 42 or a speed sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the driving data using deep learning. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides advice to the driver through voice instructions or display on the display. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision 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 driving data using the camera 42 or a speed sensor of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driving data using deep learning. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides advice to the driver through voice instructions or display on a screen. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit 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 driving data using the camera 42 or a speed sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the driving data using deep learning. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides advice to the driver through voice instructions or display on a screen.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The driving instruction system may further include a health monitoring unit that monitors the driver's health condition. The health monitoring unit can collect biometric data such as heart rate, blood pressure, and stress level to monitor the driver's health condition in real time. For example, if the driver's heart rate suddenly rises, the system can advise the driver to take a break. If the driver's blood pressure is high, the system can suggest breathing techniques to help them relax. Furthermore, if the stress level is high, the system can play music to help the driver relax. This makes it possible to provide driving instruction that takes the driver's health condition into consideration, thereby strengthening support for safe driving.

[0081] The driving instruction system may further include a style learning unit that learns the driver's driving style. The style learning unit may learn, for example, the driver's acceleration and deceleration patterns and steering characteristics, and provide advice according to each individual driving style. For example, a driver who frequently accelerates suddenly may be provided with advice encouraging smoother acceleration. A driver who handles the steering wheel roughly may be provided with advice recommending more gentle steering. Furthermore, the system may provide advice for improving fuel efficiency based on the driver's driving style. This makes it possible to provide customized advice according to each driver's individual driving style, which is expected to improve driving skills.

[0082] The driving training system can further include a history analysis unit that analyzes the driver's driving history. The history analysis unit can, for example, analyze the driver's driving tendencies based on past driving data and support the long-term improvement of driving skills. For example, if many sudden braking incidents have been recorded in the past, it can provide advice focused on braking timing. Also, for a driver who is not good at driving on a particular road, it can provide detailed advice about that road. Furthermore, it can evaluate the progress of the driver's driving skill based on the driver's driving history and provide feedback to increase motivation. This can be expected to improve the driver's driving skill in the long term.

[0083] The driving guidance system may further include an environment monitoring unit that monitors the driver's driving environment. The environment monitoring unit can collect external environmental data, such as weather, road conditions, and traffic volume, and provide appropriate advice to the driver. For example, it can provide advice on reducing the risk of slipping when it is raining. It can also suggest relaxation methods to reduce stress when stuck in traffic. It can also provide advice on maintaining visibility when driving at night. This provides appropriate advice according to the driving environment, thereby strengthening support for safe driving.

[0084] The driving instruction system may further include an attention monitoring unit that monitors the driver's attention while driving. The attention monitoring unit can detect a decline in attention by monitoring, for example, the driver's line of sight, blinking frequency, head movement, etc. For example, if the driver's gaze is averted from the road, it can provide advice to the driver to pay attention. Furthermore, if the blinking frequency increases, it can indicate fatigue and provide advice to take a break. Furthermore, if the head movement is unnatural, it can indicate a decline in concentration and provide advice to focus on driving. This can maintain the driver's attention and support safe driving.

[0085] The driving coaching system can also estimate the driver's emotions and adjust the music and entertainment provided while driving based on the estimated emotions. For example, if the driver is nervous, it can play relaxing music. If the driver is relaxed, it can play lively music. Furthermore, if the driver is tired, it can play music that wakes the driver up. In this way, by adjusting the music and entertainment according to the driver's emotions, it is possible to reduce stress while driving and provide a comfortable driving environment.

[0086] The driving instruction system may further include a posture monitoring unit that monitors the driver's posture while driving. The posture monitoring unit may monitor, for example, the driver's sitting posture, back angle, shoulder position, etc., and provide advice to maintain proper posture. For example, if the driver's back is hunched, advice to encourage correct posture may be provided. Also, if the shoulders are raised, it may suggest stretches to relax. Furthermore, if the sitting posture is unnatural, it may suggest seat adjustments. This helps maintain the driver's posture properly and reduces fatigue caused by long hours of driving.

[0087] The driving instruction system may further include a visual enhancement unit that enhances the driver's visual information while driving. The visual enhancement unit may use, for example, augmented reality (AR) technology to display important information in the driver's field of vision. For example, the visual enhancement unit may overlay information such as the distance to the vehicle ahead, speed limits, and road signs on the driver's field of vision. It may also provide auxiliary lights to improve visibility in dark places when driving at night. It may also provide information to maintain clear visibility in bad weather. This enhances the driver's visual information and supports safe driving.

[0088] The driving guidance system can also estimate the driver's emotions and suggest rest periods while driving based on the estimated emotions. For example, if the driver is nervous, it can suggest a short break. If the driver is relaxed, it can recommend continuing driving for a long period of time. Furthermore, if the driver is tired, it can suggest an earlier break. In this way, by suggesting rest periods according to the driver's emotions, it is possible to reduce fatigue while driving and support safe driving.

[0089] The driving instruction system may further include a communication support unit that supports communication with the driver while driving. The communication support unit can, for example, provide voice messages at appropriate times when the driver communicates with other vehicles or pedestrians. For example, the communication support unit can provide a message to pedestrians to warn them when turning right at an intersection. It can also provide a message to signal vehicles behind when changing lanes. Furthermore, it can provide a message to warn them of surrounding vehicles and obstacles when parking. This supports communication between the driver and enhances safe driving.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The collection unit collects driving data. The driving data includes speed, brake usage, steering operation, etc. The collection unit collects driving data using cameras, speed sensors, and brake sensors located at the front, rear, left and right of the vehicle. The collection unit can also estimate the driver's emotions and adjust the frequency of collecting driving data based on the estimated driver's emotions. Step 2: The analysis unit analyzes the driving data collected by the collection unit. The analysis unit uses deep learning to analyze the driving data and learn the driver's driving patterns. Deep learning has a neural network structure, and by learning large amounts of data, it can perform highly accurate analysis. The analysis unit can also estimate the driver's emotions and adjust the analysis algorithm based on the estimated driver's emotions. Step 3: The provider provides advice to the driver based on the analysis results obtained by the analyzer. The provider provides advice to the driver through voice instructions and messages displayed on the display. The provider can also accumulate past driving data and provide optimal advice to individual drivers.

[0092] 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.

[0093] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 driving data; an analysis unit that analyzes the driving data collected by the collection unit; a providing unit that provides advice to the driver based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. The collecting unit Equipped with cameras and speed or brake sensors located at the front, rear, left and right of the vehicle 2. The system of claim 1.

3. The analysis unit Analyze driving data using deep learning to learn driver patterns 2. The system of claim 1.

4. The providing unit Provides advice to the driver through voice instructions or messages on the display 2. The system of claim 1.

5. The providing unit Accumulating past driving data and providing advice to individual drivers 2. The system of claim 1.

6. The collecting unit Estimating the driver's emotions and adjusting the frequency of collecting driving data based on the estimated driver's emotions 2. The system of claim 1.

7. The collecting unit Analyze the driver's past driving data and select sensor placement 2. The system of claim 1.

8. The collecting unit As driving data is collected, it is filtered based on the driver's current driving situation and environmental conditions.

2. The system of claim 1.

Images