system

Abstract

The system according to the embodiment aims to analyze driving data and provide safe driving advice in real time. According to an embodiment, the system includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires driving data. The analysis unit analyzes the driving data acquired by the acquisition unit. The provision unit provides the results of the analysis 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 technologies do not adequately analyze driving data in real time and provide safe driving advice, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze driving data and provide safe driving advice in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires driving data. The analysis unit analyzes the driving data acquired by the acquisition unit. The provision unit provides the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze driving data and provide safe driving advice in real time. [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 support system according to an embodiment of the present invention uses AI to analyze driving data in real time and provide safe driving advice to the driver. This system acquires driving data from a smartphone, analyzes it with AI, and provides safe driving advice to the driver in real time in conjunction with the vehicle's onboard navigation system. For example, the driving support system collects data such as the vehicle's position, speed, and acceleration using the smartphone's GPS and acceleration sensor. This data is input into the AI. The AI ​​then analyzes the input driving data and grasps the driver's driving situation in real time. For example, the AI ​​detects when the vehicle is approaching an intersection and suggests appropriate speed management. It also advises the driver to maintain a safe following distance if the following distance is becoming too short. Furthermore, the AI's analysis results are provided to the driver in conjunction with the vehicle's onboard navigation system. For example, advice such as "Please slow down" or "Please maintain a safe following distance" is displayed on the navigation screen. This allows the driver to receive safe driving advice in real time. This service also contributes to improving the driver's driving habits. For example, the AI ​​analyzes accumulated driving data to identify the driver's driving tendencies. Based on this, the system will suggest specific areas for improvement to the driver. For example, advice such as "You brake suddenly too often, so try braking earlier" will be provided. This will enable the driver to drive more safely and reduce the risk of traffic accidents. In this way, the driving support system can improve the driver's driving habits and promote safe driving.

[0029] A driving support system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires driving data from a smartphone. For example, the acquisition unit uses the smartphone's GPS and acceleration sensor to collect data such as the vehicle's position, speed, and acceleration. This data is input to an AI. The analysis unit uses the AI ​​to analyze the driving data acquired by the acquisition unit. The AI ​​understands the driver's driving situation in real time based on the driving data. For example, the AI ​​may detect that the vehicle is approaching an intersection and suggest appropriate speed management. Furthermore, if the following distance is becoming shorter, the AI ​​may advise the driver to maintain a safe following distance. The provision unit provides the results of the analysis by the analysis unit to the driver in cooperation with the vehicle's onboard navigation system. For example, advice such as "Please slow down" or "Please maintain a safe following distance" is displayed on the navigation screen. This allows the driver to receive safe driving advice in real time. This allows the driving support system according to an embodiment to improve the driver's driving habits and promote safe driving.

[0030] The acquisition unit can acquire driving data using the smartphone's GPS or acceleration sensor. The acquisition unit, for example, acquires vehicle location information using the smartphone's GPS. For example, the GPS has an accuracy of within a few meters, allowing the vehicle's exact location to be determined. The acquisition unit can also acquire vehicle acceleration data using the smartphone's acceleration sensor. For example, the acceleration sensor has very high sensitivity and can detect even the slightest movement of the vehicle. This makes it possible to collect data in real time by acquiring driving data using the smartphone's sensor. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired from the smartphone's sensor into the generation AI and have the generation AI perform preprocessing of the data.

[0031] The analysis unit can grasp the driver's driving situation in real time based on the driving data. The analysis unit, for example, analyzes the vehicle speed based on the driving data. For example, the analysis unit can detect sudden braking or sudden acceleration when the vehicle speed changes suddenly. The analysis unit can also analyze the vehicle's position based on the driving data. For example, the analysis unit can detect when the vehicle is approaching an intersection and suggest appropriate speed management. The analysis unit can also analyze the following distance based on the driving data. For example, the analysis unit can advise the driver to maintain a safe following distance when the following distance is getting shorter. In this way, by analyzing the driving data in real time, the driver's driving situation can be immediately grasped. 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 analyze the driving situation.

[0032] The providing unit can cooperate with the vehicle's onboard navigation system to provide the driver with safe driving advice in real time. The providing unit, for example, synchronizes data with the vehicle's onboard navigation system. For example, the providing unit displays advice such as "Please slow down" or "Please maintain a safe distance" on the navigation system screen. The providing unit can also cooperate with the navigation system to provide voice guidance. For example, the providing unit uses the navigation system's voice guidance function to provide advice to the driver in real time. In this way, by cooperating with the navigation system, appropriate advice can be provided to the driver in real time. 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 data from the analysis unit to a generation AI and cause the generation AI to generate advice.

[0033] The providing unit can display advice such as "Please slow down" or "Please maintain a safe distance" on the navigation screen. The providing unit, for example, adjusts the timing of displaying the advice on the navigation screen. For example, the providing unit displays "Please slow down" when the vehicle approaches an intersection. The providing unit can also display "Please maintain a safe distance" when the distance between vehicles becomes short. In this way, by displaying advice on the navigation screen, the driver can visually receive instructions for safe driving. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs data from the analysis unit into the generating AI and causes the generating AI to determine the timing of displaying the advice.

[0034] The analysis unit can analyze the accumulated driving data and understand the driver's driving tendencies. The analysis unit, for example, analyzes the driver's driving patterns based on the accumulated driving data. For example, the analysis unit analyzes the frequency of sudden braking and sudden acceleration to understand the driver's driving tendencies. The analysis unit can also analyze driving data on a specific route to understand the driver's driving habits. For example, the analysis unit analyzes data on speed and following distance on a specific route to identify the driver's driving tendencies. In this way, by analyzing the accumulated data, the driver's driving habits can be understood and improvements can be suggested. 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 accumulated driving data to a generation AI and have the generation AI perform an analysis of the driving tendencies.

[0035] The providing unit can suggest specific improvements to the driver. The providing unit suggests specific improvements based on, for example, the driver's driving tendencies. For example, the providing unit can provide advice such as "Brake earlier" to a driver who frequently brakes suddenly. The providing unit can also provide advice such as "Maintain a safe following distance" to a driver who keeps a short following distance. By suggesting specific improvements, the providing unit encourages improvement of the driver's driving habits. 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 data from the analysis unit into a generating AI and have the generating AI execute the suggested improvements.

[0036] The acquisition unit can analyze the driver's past driving history and select the optimal data acquisition method. For example, the acquisition unit can analyze the frequency of sudden braking based on the driver's past driving history. For example, if the driver has frequently used sudden braking in the past, the acquisition unit can focus on acquiring data related to braking operations. The acquisition unit can also analyze the driver's past driving routes and prioritize acquiring data related to a specific route. For example, if the driver has frequently used a specific route in the past, the acquisition unit can prioritize acquiring data related to that route. The acquisition unit can also analyze the driver's past speeding history and acquire detailed data related to speed. For example, if the driver has previously exceeded the speed limit, the acquisition unit can acquire detailed data related to speed. This allows the analysis of the past driving history to select the optimal data acquisition method. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input past driving history data into a generation AI and have the generation AI select the optimal data acquisition method.

[0037] When acquiring driving data, the acquisition unit can filter the driving data based on the driver's current driving situation and areas of interest. For example, when the driver is driving on a highway, the acquisition unit acquires data specific to the highway. For example, the acquisition unit prioritizes acquiring data related to the speed and following distance on the highway. Furthermore, when the driver is driving in an urban area, the acquisition unit can also prioritize acquiring data related to traffic lights and intersections. For example, the acquisition unit acquires data related to the timing of traffic lights in urban areas and the speed at which vehicles pass through intersections. Furthermore, the acquisition unit can also acquire data related to safe driving techniques in which the driver is interested. For example, the acquisition unit prioritizes acquiring data related to eco-driving in which the driver is interested. This allows only necessary data to be acquired by filtering the data based on the driving situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data related to the driving situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0038] When acquiring driving data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the driver's geographical location information. For example, when the driver is driving in a mountainous area, the acquisition unit prioritizes acquiring data related to curves and gradients. For example, the acquisition unit acquires data related to changes in curve angles and gradients in mountainous areas. The acquisition unit can also prioritize acquiring data related to traffic lights and intersections when the driver is driving in an urban area. For example, the acquisition unit acquires data related to the timing of traffic lights and the speed at which the vehicle passes through an intersection in an urban area. The acquisition unit can also prioritize acquiring data related to speed and following distance when the driver is driving on a highway. For example, the acquisition unit acquires data on speed and following distance on a highway. This allows highly relevant data to be acquired preferentially by taking into account the geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, or without, an AI. For example, the acquisition unit may input geographical location information to a generation AI and cause the generation AI to acquire highly relevant data.

[0039] When acquiring driving data, the acquisition unit can analyze the driver's social media activities and acquire related data. For example, if the driver posts about safe driving on social media, the acquisition unit prioritizes acquiring data about safe driving. For example, the acquisition unit acquires data based on safe driving tips and advice shared by the driver on social media. Furthermore, if the driver shares information about a specific route on social media, the acquisition unit can prioritize acquiring data about the route. For example, the acquisition unit acquires data about traffic conditions and road conditions for the route shared by the driver. Furthermore, if the driver shares information about vehicle performance on social media, the acquisition unit can prioritize acquiring data about vehicle performance. For example, the acquisition unit acquires data about the vehicle's fuel efficiency and acceleration performance shared by the driver. This allows related data to be acquired by analyzing social media activities. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit may input social media posting data to a generation AI and cause the generation AI to acquire related data.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the driving data. For example, the analysis unit performs a detailed analysis on important driving data (e.g., sudden braking or sudden acceleration). For example, the analysis unit may perform a detailed analysis of the frequency and intensity of sudden braking to understand the driver's driving tendencies. The analysis unit can also perform a simplified analysis on general driving data (e.g., normal driving speed). For example, the analysis unit may perform a simplified analysis of fluctuations in normal driving speed to understand the driver's driving patterns. The analysis unit can also perform a customized analysis on specific driving data (e.g., driving on a specific route). For example, the analysis unit may perform a detailed analysis of data on speed and following distance on a specific route to identify the driver's driving tendencies. In this way, by adjusting the level of detail of the analysis based on the importance of the driving data, a detailed analysis can be performed on important data. 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 may input driving data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of driving data. For example, the analysis unit applies a speed fluctuation analysis algorithm to speed data. For example, the analysis unit analyzes vehicle speed fluctuations based on the speed data to detect sudden acceleration or deceleration. The analysis unit can also apply an algorithm to analyze fluctuations in inter-vehicle distance data. For example, the analysis unit analyzes maintaining a safe inter-vehicle distance based on the inter-vehicle distance data. The analysis unit can also apply an algorithm to analyze the frequency and strength of braking use to braking operation data. For example, the analysis unit analyzes the frequency and strength of sudden braking based on the braking operation data. This enables more accurate analysis by applying different analysis algorithms depending on the category of driving 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.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time when the driving data was acquired. The analysis unit, for example, prioritizes analysis of the latest driving data and provides advice in real time. For example, the analysis unit can instantly understand the driver's driving situation based on the latest driving data and provide appropriate advice. The analysis unit can also analyze past driving data and suggest improvements to driving habits. For example, the analysis unit can understand the driver's driving tendencies based on the past driving data and suggest specific improvements. The analysis unit can also prioritize analysis of driving data from a specific time period (e.g., rush hour). For example, the analysis unit can analyze traffic conditions based on rush hour driving data and provide appropriate advice. This enables real-time advice by determining the priority of analysis based on the time when the driving data was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input driving data to a generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of driving data. For example, the analysis unit prioritizes analysis of important driving data (e.g., sudden braking or sudden acceleration). For example, the analysis unit can understand the driver's driving tendencies based on data on sudden braking or sudden acceleration and provide appropriate advice. The analysis unit can also postpone analysis of general driving data (e.g., normal driving speed). For example, the analysis unit can briefly analyze data on normal driving speed to understand the driver's driving patterns. The analysis unit can also prioritize analysis of specific driving data (e.g., driving on a specific route). For example, the analysis unit can analyze data on speed and following distance on a specific route in detail to identify the driver's driving tendencies. This allows important data to be analyzed preferentially by adjusting the order of analysis based on the relevance of driving data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input driving data to a generation AI and have the generation AI adjust the order of analysis.

[0044] 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, the providing unit provides detailed advice for important driving data (e.g., sudden braking or sudden acceleration). For example, the providing unit suggests specific improvements to the driver based on the frequency and strength of sudden braking. The providing unit can also provide simplified advice for general driving data (e.g., normal driving speed). For example, the providing unit can provide simple advice to the driver based on fluctuations in the normal driving speed. The providing unit can also provide customized advice for specific driving data (e.g., driving on a specific route). For example, the providing unit can provide specific advice to the driver based on data on the speed and following distance on a specific route. In this way, by adjusting the level of detail of the advice based on the importance of the driving data, detailed advice can be provided for important data. 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 adjust the level of detail of the advice.

[0045] When providing advice, the providing unit can apply different advice algorithms depending on the category of driving data. For example, the providing unit applies an algorithm that provides advice regarding speed management to speed data. For example, the providing unit provides advice to the driver on appropriate speed management based on the speed data. The providing unit can also apply an algorithm that provides advice regarding maintaining a safe following distance to inter-vehicle distance data. For example, the providing unit advises the driver to maintain a safe following distance based on the inter-vehicle distance data. The providing unit can also apply an algorithm that provides advice regarding the frequency and strength of braking use to brake operation data. For example, the providing unit advises the driver to avoid sudden braking based on the brake operation data. In this way, applying different advice algorithms depending on the category of driving data enables more accurate advice. Some or all of the above-mentioned processing by the providing unit may be performed using, or without, 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.

[0046] When providing advice, the providing unit can determine the priority of the advice based on the time when the driving data was acquired. The providing unit provides advice in real time based on, for example, the latest driving data. For example, the providing unit immediately provides appropriate advice to the driver based on the latest driving data. The providing unit can also suggest improvements to driving habits based on past driving data. For example, the providing unit can understand the driver's driving tendencies based on past driving data and suggest specific improvements. The providing unit can also provide advice preferentially based on driving data from a specific time period (e.g., rush hour). For example, the providing unit can analyze traffic conditions based on rush hour driving data and provide appropriate advice. This enables advice to be provided in real time by determining the priority of advice based on the time when the driving data was acquired. 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 driving data to a generation AI and cause the generation AI to determine the priority of advice.

[0047] When providing advice, the providing unit can adjust the order of advice based on the relevance of driving data. The providing unit, for example, prioritizes providing advice based on important driving data (e.g., sudden braking or sudden acceleration). For example, the providing unit immediately provides appropriate advice to the driver based on data on sudden braking or sudden acceleration. The providing unit can also provide advice later based on general driving data (e.g., normal driving speed). For example, the providing unit provides simple advice to the driver based on data on normal driving speed. The providing unit can also prioritize providing advice based on specific driving data (e.g., driving on a specific route). For example, the providing unit provides specific advice to the driver based on data on speed and following distance on a specific route. In this way, by adjusting the order of advice based on the relevance of driving data, advice based on important data can be prioritized. 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 adjust the order of advice.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can take weather information into account when analyzing the driver's driving data. For example, because roads become slippery in rainy weather, the analysis unit places particular emphasis on data on sudden braking and sudden acceleration, and provides advice to the driver to drive more carefully. In addition, on snowy or icy roads, the analysis unit can strengthen advice on maintaining a safe distance between vehicles and reducing speed. Furthermore, the analysis unit can analyze the driver's driving tendencies based on weather information and suggest ways to improve driving habits under specific weather conditions. In this way, taking weather information into consideration can support safer driving.

[0050] The information providing unit can provide eco-driving advice based on the driver's driving data. For example, the information providing unit can analyze the frequency of sudden acceleration and braking and provide advice to improve fuel efficiency. The information providing unit can also suggest appropriate gear change timing to the driver. Furthermore, the information providing unit can display information about eco-driving on the navigation screen to support the driver in practicing eco-driving in real time. This can promote the practice of eco-driving, contribute to improved fuel efficiency, and protect the environment.

[0051] The acquisition unit can monitor the driver's health condition and adjust the frequency of driving data acquisition based on the health condition. For example, the acquisition unit can measure the driver's heart rate and blood pressure with sensors, and increase the frequency of driving data acquisition if an abnormality is detected. In addition, if the driver is fatigued, the acquisition unit can increase the frequency of driving data acquisition and detect a decrease in attention. Furthermore, based on the driver's health condition, the acquisition unit can provide advice such as suggesting a break or encouraging the driver to stop driving. This makes it possible to provide driving support that takes the driver's health condition into consideration.

[0052] The provision unit can propose a training program aimed at improving driving skills based on the driver's driving data. For example, the provision unit can analyze the frequency of sudden braking and sudden acceleration and propose a specific training menu to the driver. The provision unit can also provide an individually customized training program based on the driver's driving tendencies. Furthermore, the provision unit can monitor the progress of training and provide feedback to the driver. This can support the improvement of the driver's driving skills and contribute to the realization of safe driving.

[0053] The information providing unit can display the degree of eco-driving achievement while driving in real time based on the driver's driving data. For example, the information providing unit can analyze data related to improving fuel efficiency and reducing CO2 emissions, and display the degree of eco-driving achievement to the driver on the navigation screen. The information providing unit can also suggest specific improvements to the driver based on the degree of eco-driving achievement. Furthermore, the information providing unit can increase the driver's motivation by visually displaying the degree of eco-driving achievement. This can promote the practice of eco-driving and contribute to environmental protection.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The acquisition unit acquires driving data from the smartphone. For example, it uses the smartphone's GPS and acceleration sensor to collect data such as the vehicle's position, speed, and acceleration. This data is then input into the AI. Step 2: The analysis unit uses AI to analyze the driving data acquired by the acquisition unit. Based on the driving data, the AI ​​grasps the driver's driving situation in real time. For example, the AI ​​detects when the vehicle is approaching an intersection and suggests appropriate speed management. Also, if the distance between vehicles is getting shorter, it advises the driver to maintain a safe distance. Step 3: The provider provides the results of the analysis by the analyzer to the driver in conjunction with the car's onboard navigation system. For example, advice such as "Please slow down" or "Please maintain a safe distance" is displayed on the navigation screen. This allows the driver to receive safe driving advice in real time.

[0056] (Example 2) A driving support system according to an embodiment of the present invention uses AI to analyze driving data in real time and provide safe driving advice to the driver. This system acquires driving data from a smartphone, analyzes it with AI, and provides safe driving advice to the driver in real time in conjunction with the vehicle's onboard navigation system. For example, the driving support system collects data such as the vehicle's position, speed, and acceleration using the smartphone's GPS and acceleration sensor. This data is input into the AI. The AI ​​then analyzes the input driving data and grasps the driver's driving situation in real time. For example, the AI ​​detects when the vehicle is approaching an intersection and suggests appropriate speed management. It also advises the driver to maintain a safe following distance if the following distance is becoming too short. Furthermore, the AI's analysis results are provided to the driver in conjunction with the vehicle's onboard navigation system. For example, advice such as "Please slow down" or "Please maintain a safe following distance" is displayed on the navigation screen. This allows the driver to receive safe driving advice in real time. This service also contributes to improving the driver's driving habits. For example, the AI ​​analyzes accumulated driving data to identify the driver's driving tendencies. Based on this, the system will suggest specific areas for improvement to the driver. For example, advice such as "You brake suddenly too often, so try braking earlier" will be provided. This will enable the driver to drive more safely and reduce the risk of traffic accidents. In this way, the driving support system can improve the driver's driving habits and promote safe driving.

[0057] A driving support system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires driving data from a smartphone. For example, the acquisition unit uses the smartphone's GPS and acceleration sensor to collect data such as the vehicle's position, speed, and acceleration. This data is input to an AI. The analysis unit uses the AI ​​to analyze the driving data acquired by the acquisition unit. The AI ​​understands the driver's driving situation in real time based on the driving data. For example, the AI ​​may detect that the vehicle is approaching an intersection and suggest appropriate speed management. Furthermore, if the following distance is becoming shorter, the AI ​​may advise the driver to maintain a safe following distance. The provision unit provides the results of the analysis by the analysis unit to the driver in cooperation with the vehicle's onboard navigation system. For example, advice such as "Please slow down" or "Please maintain a safe following distance" is displayed on the navigation screen. This allows the driver to receive safe driving advice in real time. This allows the driving support system according to an embodiment to improve the driver's driving habits and promote safe driving.

[0058] The acquisition unit can acquire driving data using the smartphone's GPS or acceleration sensor. The acquisition unit, for example, acquires vehicle location information using the smartphone's GPS. For example, the GPS has an accuracy of within a few meters, allowing the vehicle's exact location to be determined. The acquisition unit can also acquire vehicle acceleration data using the smartphone's acceleration sensor. For example, the acceleration sensor has very high sensitivity and can detect even the slightest movement of the vehicle. This makes it possible to collect data in real time by acquiring driving data using the smartphone's sensor. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired from the smartphone's sensor into the generation AI and have the generation AI perform preprocessing of the data.

[0059] The analysis unit can grasp the driver's driving situation in real time based on the driving data. The analysis unit, for example, analyzes the vehicle speed based on the driving data. For example, the analysis unit can detect sudden braking or sudden acceleration when the vehicle speed changes suddenly. The analysis unit can also analyze the vehicle's position based on the driving data. For example, the analysis unit can detect when the vehicle is approaching an intersection and suggest appropriate speed management. The analysis unit can also analyze the following distance based on the driving data. For example, the analysis unit can advise the driver to maintain a safe following distance when the following distance is getting shorter. In this way, by analyzing the driving data in real time, the driver's driving situation can be immediately grasped. 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 analyze the driving situation.

[0060] The providing unit can cooperate with the vehicle's onboard navigation system to provide the driver with safe driving advice in real time. The providing unit, for example, synchronizes data with the vehicle's onboard navigation system. For example, the providing unit displays advice such as "Please slow down" or "Please maintain a safe distance" on the navigation system screen. The providing unit can also cooperate with the navigation system to provide voice guidance. For example, the providing unit uses the navigation system's voice guidance function to provide advice to the driver in real time. In this way, by cooperating with the navigation system, appropriate advice can be provided to the driver in real time. 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 data from the analysis unit to a generation AI and cause the generation AI to generate advice.

[0061] The providing unit can display advice such as "Please slow down" or "Please maintain a safe distance" on the navigation screen. The providing unit, for example, adjusts the timing of displaying the advice on the navigation screen. For example, the providing unit displays "Please slow down" when the vehicle approaches an intersection. The providing unit can also display "Please maintain a safe distance" when the distance between vehicles becomes short. In this way, by displaying advice on the navigation screen, the driver can visually receive instructions for safe driving. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs data from the analysis unit into the generating AI and causes the generating AI to determine the timing of displaying the advice.

[0062] The analysis unit can analyze the accumulated driving data and understand the driver's driving tendencies. The analysis unit, for example, analyzes the driver's driving patterns based on the accumulated driving data. For example, the analysis unit analyzes the frequency of sudden braking and sudden acceleration to understand the driver's driving tendencies. The analysis unit can also analyze driving data on a specific route to understand the driver's driving habits. For example, the analysis unit analyzes data on speed and following distance on a specific route to identify the driver's driving tendencies. In this way, by analyzing the accumulated data, the driver's driving habits can be understood and improvements can be suggested. 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 accumulated driving data to a generation AI and have the generation AI perform an analysis of the driving tendencies.

[0063] The providing unit can suggest specific improvements to the driver. The providing unit suggests specific improvements based on, for example, the driver's driving tendencies. For example, the providing unit can provide advice such as "Brake earlier" to a driver who frequently brakes suddenly. The providing unit can also provide advice such as "Maintain a safe following distance" to a driver who keeps a short following distance. By suggesting specific improvements, the providing unit encourages improvement of the driver's driving habits. 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 data from the analysis unit into a generating AI and have the generating AI execute the suggested improvements.

[0064] The acquisition unit can estimate the driver's emotions and adjust the timing of driving data acquisition based on the estimated driver's emotions. For example, the acquisition unit captures the driver's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the driver is nervous, the acquisition interval for driving data can be increased to collect only necessary information if the driver is relaxed. Furthermore, if the driver is tired, the acquisition frequency for driving data can be increased to detect a decrease in attention. This enables more appropriate data collection by adjusting the timing of data acquisition according to the driver's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI. For example, the acquisition unit can input image data of the driver captured by a camera into the generation AI and have the generation AI estimate the driver's emotions.

[0065] The acquisition unit can analyze the driver's past driving history and select the optimal data acquisition method. For example, the acquisition unit can analyze the frequency of sudden braking based on the driver's past driving history. For example, if the driver has frequently used sudden braking in the past, the acquisition unit can focus on acquiring data related to braking operations. The acquisition unit can also analyze the driver's past driving routes and prioritize acquiring data related to a specific route. For example, if the driver has frequently used a specific route in the past, the acquisition unit can prioritize acquiring data related to that route. The acquisition unit can also analyze the driver's past speeding history and acquire detailed data related to speed. For example, if the driver has previously exceeded the speed limit, the acquisition unit can acquire detailed data related to speed. This allows the analysis of the past driving history to select the optimal data acquisition method. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input past driving history data into a generation AI and have the generation AI select the optimal data acquisition method.

[0066] When acquiring driving data, the acquisition unit can filter the driving data based on the driver's current driving situation and areas of interest. For example, when the driver is driving on a highway, the acquisition unit acquires data specific to the highway. For example, the acquisition unit prioritizes acquiring data related to the speed and following distance on the highway. Furthermore, when the driver is driving in an urban area, the acquisition unit can also prioritize acquiring data related to traffic lights and intersections. For example, the acquisition unit acquires data related to the timing of traffic lights in urban areas and the speed at which vehicles pass through intersections. Furthermore, the acquisition unit can also acquire data related to safe driving techniques in which the driver is interested. For example, the acquisition unit prioritizes acquiring data related to eco-driving in which the driver is interested. This allows only necessary data to be acquired by filtering the data based on the driving situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data related to the driving situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0067] The acquisition unit can estimate the driver's emotions and determine the priority of the driving data to be acquired based on the estimated driver's emotions. For example, the acquisition unit captures the driver's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the driver is nervous, data related to the vehicle-to-vehicle distance and speed can be prioritized. Also, if the driver is relaxed, data related to fuel efficiency and eco-driving can be prioritized. Furthermore, if the driver is tired, data related to rest points and rest times can be prioritized. By determining the priority of data according to the driver's emotions, important data can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input image data of the driver captured by a camera into the generation AI and have the generation AI estimate the driver's emotions.

[0068] When acquiring driving data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the driver's geographical location information. For example, when the driver is driving in a mountainous area, the acquisition unit prioritizes acquiring data related to curves and gradients. For example, the acquisition unit acquires data related to changes in curve angles and gradients in mountainous areas. The acquisition unit can also prioritize acquiring data related to traffic lights and intersections when the driver is driving in an urban area. For example, the acquisition unit acquires data related to the timing of traffic lights and the speed at which the vehicle passes through an intersection in an urban area. The acquisition unit can also prioritize acquiring data related to speed and following distance when the driver is driving on a highway. For example, the acquisition unit acquires data on speed and following distance on a highway. This allows highly relevant data to be acquired preferentially by taking into account the geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, or without, an AI. For example, the acquisition unit may input geographical location information to a generation AI and cause the generation AI to acquire highly relevant data.

[0069] When acquiring driving data, the acquisition unit can analyze the driver's social media activities and acquire related data. For example, if the driver posts about safe driving on social media, the acquisition unit prioritizes acquiring data about safe driving. For example, the acquisition unit acquires data based on safe driving tips and advice shared by the driver on social media. Furthermore, if the driver shares information about a specific route on social media, the acquisition unit can prioritize acquiring data about the route. For example, the acquisition unit acquires data about traffic conditions and road conditions for the route shared by the driver. Furthermore, if the driver shares information about vehicle performance on social media, the acquisition unit can prioritize acquiring data about vehicle performance. For example, the acquisition unit acquires data about the vehicle's fuel efficiency and acceleration performance shared by the driver. This allows related data to be acquired by analyzing social media activities. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit may input social media posting data to a generation AI and cause the generation AI to acquire related data.

[0070] The analysis unit can estimate the driver's emotions and adjust the way the analysis is presented based on the estimated driver's emotions. For example, the analysis unit captures the driver's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the driver is nervous, the analysis unit can provide a simple, highly visible analysis result. If the driver is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the driver is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the driver captured with a camera into the generation AI and have the generation AI perform emotion estimation.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the driving data. For example, the analysis unit performs a detailed analysis on important driving data (e.g., sudden braking or sudden acceleration). For example, the analysis unit may perform a detailed analysis of the frequency and intensity of sudden braking to understand the driver's driving tendencies. The analysis unit can also perform a simplified analysis on general driving data (e.g., normal driving speed). For example, the analysis unit may perform a simplified analysis of fluctuations in normal driving speed to understand the driver's driving patterns. The analysis unit can also perform a customized analysis on specific driving data (e.g., driving on a specific route). For example, the analysis unit may perform a detailed analysis of data on speed and following distance on a specific route to identify the driver's driving tendencies. In this way, by adjusting the level of detail of the analysis based on the importance of the driving data, a detailed analysis can be performed on important data. 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 may input driving data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of driving data. For example, the analysis unit applies a speed fluctuation analysis algorithm to speed data. For example, the analysis unit analyzes vehicle speed fluctuations based on the speed data to detect sudden acceleration or deceleration. The analysis unit can also apply an algorithm to analyze fluctuations in inter-vehicle distance data. For example, the analysis unit analyzes maintaining a safe inter-vehicle distance based on the inter-vehicle distance data. The analysis unit can also apply an algorithm to analyze the frequency and strength of braking use to braking operation data. For example, the analysis unit analyzes the frequency and strength of sudden braking based on the braking operation data. This enables more accurate analysis by applying different analysis algorithms depending on the category of driving 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.

[0073] The analysis unit can estimate the driver's emotions and adjust the length of the analysis based on the estimated driver's emotions. For example, the analysis unit captures the driver's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the driver is nervous, the analysis unit can provide a short, concise analysis result. If the driver is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the driver is in a hurry, the analysis unit can provide a concise, quick analysis result. This allows for adjusting the length of the analysis according to the driver's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the driver captured with a camera into the generation AI and have the generation AI estimate the driver's emotions.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time when the driving data was acquired. The analysis unit, for example, prioritizes analysis of the latest driving data and provides advice in real time. For example, the analysis unit can instantly understand the driver's driving situation based on the latest driving data and provide appropriate advice. The analysis unit can also analyze past driving data and suggest improvements to driving habits. For example, the analysis unit can understand the driver's driving tendencies based on the past driving data and suggest specific improvements. The analysis unit can also prioritize analysis of driving data from a specific time period (e.g., rush hour). For example, the analysis unit can analyze traffic conditions based on rush hour driving data and provide appropriate advice. This enables real-time advice by determining the priority of analysis based on the time when the driving data was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input driving data to a generation AI and have the generation AI determine the analysis priority.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of driving data. For example, the analysis unit prioritizes analysis of important driving data (e.g., sudden braking or sudden acceleration). For example, the analysis unit can understand the driver's driving tendencies based on data on sudden braking or sudden acceleration and provide appropriate advice. The analysis unit can also postpone analysis of general driving data (e.g., normal driving speed). For example, the analysis unit can briefly analyze data on normal driving speed to understand the driver's driving patterns. The analysis unit can also prioritize analysis of specific driving data (e.g., driving on a specific route). For example, the analysis unit can analyze data on speed and following distance on a specific route in detail to identify the driver's driving tendencies. This allows important data to be analyzed preferentially by adjusting the order of analysis based on the relevance of driving data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input driving data to a generation AI and have the generation AI adjust the order of analysis.

[0076] The providing unit can estimate the driver's emotions and adjust the way in which advice is presented based on the estimated driver's emotions. For example, the providing unit captures the driver's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the driver is nervous, the providing unit can provide simple, highly visible advice. 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 concise advice that focuses on the main points. This allows the providing unit to adjust the way in which advice is presented based on the driver's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input image data of the driver captured by a camera into the generation AI and cause the generation AI to estimate the driver's emotions.

[0077] 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, the providing unit provides detailed advice for important driving data (e.g., sudden braking or sudden acceleration). For example, the providing unit suggests specific improvements to the driver based on the frequency and strength of sudden braking. The providing unit can also provide simplified advice for general driving data (e.g., normal driving speed). For example, the providing unit can provide simple advice to the driver based on fluctuations in the normal driving speed. The providing unit can also provide customized advice for specific driving data (e.g., driving on a specific route). For example, the providing unit can provide specific advice to the driver based on data on the speed and following distance on a specific route. In this way, by adjusting the level of detail of the advice based on the importance of the driving data, detailed advice can be provided for important data. 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 adjust the level of detail of the advice.

[0078] When providing advice, the providing unit can apply different advice algorithms depending on the category of driving data. For example, the providing unit applies an algorithm that provides advice regarding speed management to speed data. For example, the providing unit provides advice to the driver on appropriate speed management based on the speed data. The providing unit can also apply an algorithm that provides advice regarding maintaining a safe following distance to inter-vehicle distance data. For example, the providing unit advises the driver to maintain a safe following distance based on the inter-vehicle distance data. The providing unit can also apply an algorithm that provides advice regarding the frequency and strength of braking use to brake operation data. For example, the providing unit advises the driver to avoid sudden braking based on the brake operation data. In this way, applying different advice algorithms depending on the category of driving data enables more accurate advice. Some or all of the above-mentioned processing by the providing unit may be performed using, or without, 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.

[0079] The providing unit can estimate the driver's emotions and adjust the length of advice based on the estimated driver's emotions. For example, the providing unit captures the driver's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the driver is nervous, the providing unit can provide short, to-the-point advice. 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 concise, quick advice. This allows the providing unit to adjust the length of advice according to the driver's emotions, thereby providing more appropriate advice. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input image data of the driver captured by a camera into the generation AI and cause the generation AI to estimate the driver's emotions.

[0080] When providing advice, the providing unit can determine the priority of the advice based on the time when the driving data was acquired. The providing unit provides advice in real time based on, for example, the latest driving data. For example, the providing unit immediately provides appropriate advice to the driver based on the latest driving data. The providing unit can also suggest improvements to driving habits based on past driving data. For example, the providing unit can understand the driver's driving tendencies based on past driving data and suggest specific improvements. The providing unit can also provide advice preferentially based on driving data from a specific time period (e.g., rush hour). For example, the providing unit can analyze traffic conditions based on rush hour driving data and provide appropriate advice. This enables advice to be provided in real time by determining the priority of advice based on the time when the driving data was acquired. 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 driving data to a generation AI and cause the generation AI to determine the priority of advice.

[0081] When providing advice, the providing unit can adjust the order of advice based on the relevance of driving data. The providing unit, for example, prioritizes providing advice based on important driving data (e.g., sudden braking or sudden acceleration). For example, the providing unit immediately provides appropriate advice to the driver based on data on sudden braking or sudden acceleration. The providing unit can also provide advice later based on general driving data (e.g., normal driving speed). For example, the providing unit provides simple advice to the driver based on data on normal driving speed. The providing unit can also prioritize providing advice based on specific driving data (e.g., driving on a specific route). For example, the providing unit provides specific advice to the driver based on data on speed and following distance on a specific route. In this way, by adjusting the order of advice based on the relevance of driving data, advice based on important data can be prioritized. 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 adjust the order of advice. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition 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 acquisition unit collects data such as the vehicle's position, speed, and acceleration using the GPS and acceleration sensor of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the driving data using AI. The provision unit provides the results of the analysis by the specific processing unit 290 of the data processing device 12 to the driver in cooperation with the vehicle's onboard navigation system. For example, advice such as "Please drive slowly" or "Please maintain a safe distance" is displayed on the navigation screen. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition 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 acquisition unit collects data such as the vehicle's position, speed, and acceleration using the GPS and acceleration sensor of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the driving data using AI. The provision unit provides the results analyzed by the specific processing unit 290 of the data processing device 12 to the driver in cooperation with the vehicle's onboard navigation system. For example, advice such as "Please drive slowly" or "Please maintain a safe distance" is displayed on the navigation screen. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit collects data such as the vehicle's position, speed, and acceleration using the GPS and acceleration sensor of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the driving data using AI. The provision unit provides the results of the analysis by the specific processing unit 290 of the data processing device 12 to the driver in cooperation with the on-board navigation system. For example, advice such as "Please drive slowly" or "Please maintain a safe distance between vehicles" is displayed on the navigation screen. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit collects data such as the vehicle's position, speed, and acceleration using the GPS and acceleration sensor of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the driving data using AI. The provision unit provides the results of the analysis by the specific processing unit 290 of the data processing device 12 to the driver in cooperation with the vehicle's onboard navigation system. For example, advice such as "Please drive slowly" or "Please maintain a safe distance between vehicles" is displayed on the navigation screen.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The analysis unit can take weather information into account when analyzing the driver's driving data. For example, because roads become slippery in rainy weather, the analysis unit places particular emphasis on data on sudden braking and sudden acceleration, and provides advice to the driver to drive more carefully. In addition, on snowy or icy roads, the analysis unit can strengthen advice on maintaining a safe distance between vehicles and reducing speed. Furthermore, the analysis unit can analyze the driver's driving tendencies based on weather information and suggest ways to improve driving habits under specific weather conditions. In this way, taking weather information into consideration can support safer driving.

[0084] The information providing unit can provide eco-driving advice based on the driver's driving data. For example, the information providing unit can analyze the frequency of sudden acceleration and braking and provide advice to improve fuel efficiency. The information providing unit can also suggest appropriate gear change timing to the driver. Furthermore, the information providing unit can display information about eco-driving on the navigation screen to support the driver in practicing eco-driving in real time. This can promote the practice of eco-driving, contribute to improved fuel efficiency, and protect the environment.

[0085] The acquisition unit can monitor the driver's health condition and adjust the frequency of driving data acquisition based on the health condition. For example, the acquisition unit can measure the driver's heart rate and blood pressure with sensors, and increase the frequency of driving data acquisition if an abnormality is detected. In addition, if the driver is fatigued, the acquisition unit can increase the frequency of driving data acquisition and detect a decrease in attention. Furthermore, based on the driver's health condition, the acquisition unit can provide advice such as suggesting a break or encouraging the driver to stop driving. This makes it possible to provide driving support that takes the driver's health condition into consideration.

[0086] The provision unit can propose a training program aimed at improving driving skills based on the driver's driving data. For example, the provision unit can analyze the frequency of sudden braking and sudden acceleration and propose a specific training menu to the driver. The provision unit can also provide an individually customized training program based on the driver's driving tendencies. Furthermore, the provision unit can monitor the progress of training and provide feedback to the driver. This can support the improvement of the driver's driving skills and contribute to the realization of safe driving.

[0087] The analysis unit can estimate the driver's stress level while driving based on the driver's driving data and provide advice to reduce stress. For example, the analysis unit can analyze the frequency of sudden braking and sudden acceleration, and if there is a possibility that stress is increasing, provide advice to relax. The analysis unit can also identify the cause of stress based on the driver's driving tendencies and suggest specific countermeasures. Furthermore, the analysis unit can monitor the driver's stress level while driving in real time and suggest taking a break as necessary. This can help reduce the driver's stress and promote safe driving.

[0088] The providing unit can estimate the driver's emotions and suggest music and entertainment for driving based on the estimated emotions. For example, if the driver is nervous, relaxing music can be suggested. Also, if the driver is tired, uplifting music can be suggested. Furthermore, if the driver is relaxed, preferred entertainment content can be suggested. In this way, entertainment suggestions according to the driver's emotions can be made, improving comfort while driving.

[0089] The analysis unit can estimate the driver's emotions and adjust the communication method while driving based on the estimated emotions. For example, if the driver is nervous, a simple, highly visible message can be provided. If the driver is relaxed, detailed information can be provided. Furthermore, if the driver is in a hurry, a concise message that hits the main points can be provided. This allows the communication method to be adjusted according to the driver's emotions, making it possible to provide more appropriate information.

[0090] The providing unit can estimate the driver's emotions and suggest rest points during driving based on the estimated emotions. For example, if the driver is tired, the providing unit can suggest nearby rest points. Also, if the driver is nervous, the providing unit can suggest places where the driver can relax. Furthermore, if the driver is relaxed, the providing unit can suggest tourist spots to enjoy the drive. In this way, by suggesting rest points according to the driver's emotions, comfort and safety during driving can be improved.

[0091] The analysis unit can estimate the driver's emotions and adjust the frequency of driving advice based on the estimated emotions. For example, if the driver is nervous, the analysis unit can provide frequent advice to reassure the driver. If the driver is relaxed, the analysis unit can provide the minimum amount of advice necessary. Furthermore, if the driver is in a hurry, the analysis unit can provide only important advice. This allows the system to adjust the frequency of advice according to the driver's emotions, enabling more appropriate support.

[0092] The information providing unit can display the degree of eco-driving achievement while driving in real time based on the driver's driving data. For example, the information providing unit can analyze data related to improving fuel efficiency and reducing CO2 emissions, and display the degree of eco-driving achievement to the driver on the navigation screen. The information providing unit can also suggest specific improvements to the driver based on the degree of eco-driving achievement. Furthermore, the information providing unit can increase the driver's motivation by visually displaying the degree of eco-driving achievement. This can promote the practice of eco-driving and contribute to environmental protection.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The acquisition unit acquires driving data from the smartphone. For example, it uses the smartphone's GPS and acceleration sensor to collect data such as the vehicle's position, speed, and acceleration. This data is then input into the AI. Step 2: The analysis unit uses AI to analyze the driving data acquired by the acquisition unit. Based on the driving data, the AI ​​grasps the driver's driving situation in real time. For example, the AI ​​detects when the vehicle is approaching an intersection and suggests appropriate speed management. Also, if the distance between vehicles is getting shorter, it advises the driver to maintain a safe distance. Step 3: The provider provides the results of the analysis by the analyzer to the driver in conjunction with the car's onboard navigation system. For example, advice such as "Please slow down" or "Please maintain a safe distance" is displayed on the navigation screen. This allows the driver to receive safe driving advice in real time.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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. an acquisition unit that acquires driving data; an analysis unit that analyzes the driving data acquired by the acquisition unit; a providing unit that provides the results analyzed by the analyzing unit. A system characterized by:

2. The acquisition unit Acquire driving data using the smartphone's GPS or acceleration sensor The system of claim 1 .

3. The analysis unit Understanding driver conditions in real time based on driving data The system of claim 1 .

4. The providing unit Links with the car's navigation system to provide drivers with safe driving advice in real time The system of claim 1 .

5. The providing unit Display distance advice on the navigation screen The system of claim 1 .

6. The analysis unit Analyze accumulated driving data to understand the driver's driving tendencies The system of claim 1 .

7. The providing unit Suggest specific improvements to drivers The system of claim 1 .

8. The acquisition unit Estimate the driver's emotions and adjust the timing of driving data acquisition based on the estimated driver emotions. The system of claim 1 .

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