system

Abstract

The system according to this embodiment aims to enable drivers to understand surrounding traffic conditions and road information in real time, and to enjoy safe and comfortable driving. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a projection unit, and a provision unit. The acquisition unit acquires data from an in-vehicle camera or sensor. The analysis unit analyzes the data acquired by the acquisition unit. The projection unit projects the information identified by the analysis unit onto the glasses portion. The provision unit provides advice and warnings based on the information projected by the projection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a driver to grasp the surrounding traffic conditions and road information in real time, and there is room for improvement in improving safety.

[0005] The system according to the embodiment aims to enable a driver to grasp the surrounding traffic conditions and road information in real time and enjoy driving safely and comfortably.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a projection unit, and a provision unit. The acquisition unit acquires data from an in-vehicle camera or sensor. The analysis unit analyzes the data acquired by the acquisition unit. The projection unit projects the information identified by the analysis unit onto the glasses. The provision unit provides advice and warnings based on the information projected by the projection unit. [Effects of the Invention]

[0007] The system according to this embodiment allows the driver to understand the surrounding traffic conditions and road information in real time, enabling them to enjoy safe and comfortable driving. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The drive support agent according to an embodiment of the present invention is a glasses-type device that utilizes generative AI to enable drivers to enjoy safe and comfortable driving. This drive support agent uses generative AI to analyze data acquired from in-vehicle cameras and sensors, analyzing surrounding traffic conditions and road information in real time. The generative AI identifies important information and points of attention and projects them onto the glasses. This allows the driver to check the information without moving their eyes. Furthermore, the generative AI provides advice and warnings through bone conduction speakers. This allows the driver to receive information both visually and aurally, making it easier to maintain attention. This mechanism reduces the stress of long-distance driving and driving in unfamiliar areas, and improves safety. For example, even when fatigue and distraction tend to occur during long-distance driving, the generative AI supports the driver's attention by providing important information in real time. Also, when driving in unfamiliar areas, even when it is difficult to instantly grasp road signs and traffic conditions, the generative AI reduces the burden on the driver by providing the necessary information. In this way, the drive support agent can provide information that enables drivers to enjoy safe and comfortable driving.

[0029] The drive support agent according to this embodiment comprises an acquisition unit, an analysis unit, a projection unit, and a provision unit. The acquisition unit acquires data from in-vehicle cameras and sensors. For example, the acquisition unit can use an in-vehicle camera to photograph the surrounding traffic conditions and acquire image data. The acquisition unit can also use sensors to detect road information and vehicle status and acquire sensor data. For example, the acquisition unit can use a GPS sensor to acquire vehicle location information. Furthermore, the acquisition unit can use a weather sensor to acquire weather information. For example, the acquisition unit can use a temperature sensor to measure the temperature and acquire data. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit uses a generation AI to analyze the acquired data in real time and identify important information and points of caution. For example, the analysis unit can use an image analysis algorithm to analyze image data acquired from an in-vehicle camera and identify the location of vehicles and traffic lights ahead. Furthermore, the analysis unit can use data mining techniques to analyze sensor data and identify road conditions and traffic conditions. For example, the analysis unit can use data mining techniques to analyze road congestion and identify traffic congestion information. The projection unit projects information identified by the analysis unit onto the eyeglasses. The projection unit can display information on the lenses of the eyeglasses using display technology. For example, the projection unit can project information into the field of view using HUD (Head-Up Display) technology. The projection unit can also overlay information onto the real-world scenery using AR (Augmented Reality) technology. For example, the projection unit can overlay information on road signs and traffic lights onto the field of view using AR technology. The information provider unit provides advice and warnings based on the information projected by the projection unit. The information provider unit can provide advice and warnings to the driver via voice using bone conduction speakers. For example, the information provider unit can use bone conduction speakers to inform the driver of changes in traffic signals or sudden braking by vehicles ahead. The information provider unit can also provide advice and warnings to the driver visually using visual alerts. For example, the information provider unit can display warning messages on a display.This enables the drive support agent according to the embodiment to provide information that allows the driver to enjoy driving safely and comfortably.

[0030] The data acquisition unit acquires data from in-vehicle cameras and sensors. For example, the data acquisition unit can use an in-vehicle camera to photograph the surrounding traffic conditions and acquire image data. Specifically, the in-vehicle camera can capture high-resolution video in real time, allowing for detailed information on vehicles, pedestrians, traffic lights, road signs, and other objects ahead. The data acquisition unit can also use sensors to detect road information and vehicle status and acquire sensor data. For example, a LiDAR sensor mounted on the vehicle can accurately measure the distance to surrounding objects and understand the position and movement of obstacles. Furthermore, the data acquisition unit can acquire vehicle location information using a GPS sensor. A GPS sensor receives signals from satellites and can pinpoint the vehicle's current location with high accuracy. This allows the data acquisition unit to understand information such as the vehicle's travel path, speed, and direction of travel in real time. In addition, the data acquisition unit can acquire weather information using weather sensors. For example, a temperature sensor can measure the temperature, a humidity sensor can detect humidity, and a rain sensor can measure rainfall. This allows the data acquisition unit to understand changes in weather and road conditions in real time and provide appropriate information to the driver. This data is transmitted to the central control unit within the vehicle and used for processing in the analysis unit. The acquisition unit integrates data from these various sensors, enabling it to comprehensively understand the vehicle's surrounding environment. This allows the acquisition unit to provide the driver with fundamental information to support safe and comfortable driving.

[0031] The analysis unit analyzes the data acquired by the acquisition unit. Using generative AI, the analysis unit analyzes the acquired data in real time to identify important information and points of caution. Specifically, it can use image analysis algorithms to analyze image data acquired from in-vehicle cameras and identify the positions of vehicles and traffic lights ahead. The generative AI utilizes deep learning technology and learns from a vast dataset to achieve highly accurate image recognition. For example, the generative AI can analyze the shape, color, and movement patterns of vehicles to identify the type, distance, and speed of vehicles ahead. It can also analyze the color and illumination status of traffic lights to detect changes in signals in real time. Furthermore, the analysis unit can use data mining techniques to analyze sensor data and identify road conditions and traffic situations. For example, it can use data mining techniques to analyze road congestion and identify traffic jam information. This allows the analysis unit to suggest the optimal route to the driver and provide information to avoid traffic jams. In addition, the analysis unit can monitor the vehicle's condition and detect abnormalities. For example, it can monitor engine temperature, oil condition, tire pressure, etc., and issue a warning to the driver if an abnormality is detected. This allows the analysis unit to ensure vehicle safety and support the prevention and early response to malfunctions. By integrating this diverse data and performing comprehensive analysis, the analysis unit can provide comprehensive support to drivers.

[0032] The projection unit projects information identified by the analysis unit onto the eyeglasses. The projection unit can display information on the lenses of the eyeglasses using display technology. Specifically, it can project information into the driver's field of view using HUD (Head-Up Display) technology. HUD technology uses a transparent display to show information without obstructing the driver's view. For example, the projection unit can project information such as the position and speed of vehicles ahead, traffic light status, and road sign information into the driver's field of view, providing important information to the driver in real time. Furthermore, the projection unit can also overlay information onto the real-world landscape using AR (Augmented Reality) technology. AR technology overlays digital information onto the real-world landscape, enabling intuitive and easy-to-understand information delivery. For example, the projection unit can overlay information on road signs and traffic lights onto the driver's field of view using AR technology. This allows the driver to simultaneously view the real-world landscape and digital information, obtaining necessary information while maintaining attention while driving. Additionally, the projection unit allows for customization of how information is displayed. For example, the type, position, and size of the displayed information can be adjusted according to the driver's preference. This allows the projection unit to provide optimal information to the driver, supporting safe and comfortable driving. By utilizing these technologies, the projection unit can provide visually easy-to-understand information to the driver, delivering necessary information while maintaining their attention during driving.

[0033] The service provider provides advice and warnings based on information projected by the projection unit. The service provider can also provide voice advice and warnings to the driver using bone conduction speakers. Specifically, bone conduction speakers utilize technology to transmit sound through the skull, providing clear audio to the driver. For example, the service provider can notify the driver of changes in traffic signals or sudden braking by vehicles ahead. This allows the driver to obtain important information not only through visual information but also through auditory information. Furthermore, the service provider can provide visual advice and warnings to the driver using visual alerts. For example, the service provider can display warning messages on the display, allowing the driver to visually confirm important information. In addition, the service provider can monitor the driver's condition and provide advice and warnings at appropriate times. For example, it can detect driver fatigue and provide advice encouraging them to take a break. The service provider can also analyze the driver's driving style and provide advice for safe driving. This allows the service provider to provide comprehensive support to the driver, assisting in safe and comfortable driving. The service provider can utilize these technologies to provide drivers with appropriate advice and warnings, thereby improving safety while driving.

[0034] The analysis unit can analyze data including traffic conditions, road information, weather information, and vehicle status. For example, the analysis unit can analyze data related to traffic conditions and provide real-time traffic information. For instance, it can use a traffic flow analysis algorithm to analyze traffic flow and identify congestion information. The analysis unit can also analyze data related to road information and identify road conditions. For example, it can analyze road construction information and accident information and inform drivers. Furthermore, the analysis unit can analyze data related to weather information and identify meteorological conditions. For example, it can analyze data acquired from weather sensors and identify temperature and precipitation. The analysis unit can also analyze data related to vehicle status and identify vehicle status. For example, it can analyze engine status and tire pressure and inform drivers. This enables the provision of more accurate information by analyzing diverse data. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs data related to traffic conditions, road information, weather information, and vehicle status into the generation AI, which analyzes the data and identifies important information.

[0035] The system can provide advice and warnings, including those related to changes in traffic signals, sudden braking by vehicles ahead, speeding, and lane departures. For example, the system can detect changes in traffic signals and notify the driver. For example, it can detect changes in the color of traffic lights and notify the driver when the light turns red. The system can also detect sudden braking by vehicles ahead and warn the driver. For example, it can detect a sudden decrease in the distance between vehicles and notify the driver when the vehicle ahead has braked suddenly. Furthermore, the system can detect speeding and warn the driver. For example, it can warn the driver if the speed limit is exceeded. The system can also detect lane departures and warn the driver. For example, it can recognize lane markers and warn the driver when the vehicle deviates from its lane. This improves driver safety by providing specific advice and warnings. Some or all of the above processing in the system is performed using generative AI. For example, the data provider inputs information about changes in traffic signals, sudden braking by vehicles ahead, speeding, and lane departures into the generating AI, which then analyzes the data to provide advice and warnings.

[0036] The data acquisition unit can analyze the driver's past driving history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize acquiring data on routes that the driver has frequently traveled in the past. For example, the data acquisition unit can analyze the driver's driving history data and prioritize acquiring data related to a specific route. The data acquisition unit can also predict and acquire data necessary for a specific time period based on the driver's past driving history. For example, the data acquisition unit can analyze the driver's driving history data and prioritize acquiring traffic information for a specific time period. Furthermore, the data acquisition unit can analyze the driver's past driving patterns and select the optimal data acquisition timing. For example, the data acquisition unit can analyze the driver's driving pattern data and select the optimal data acquisition timing. This enables efficient information provision by selecting a data acquisition method based on past driving history. Some or all of the above processing in the data acquisition unit is performed using a generating AI. For example, the data acquisition unit inputs the driver's driving history data into the generating AI, which analyzes the data and selects the optimal data acquisition method.

[0037] The data acquisition unit can filter data based on the driver's current driving status and areas of interest. For example, if the driver is driving on a highway, the data acquisition unit will prioritize acquiring information related to the highway. For instance, it can prioritize acquiring highway traffic information and service area information. Furthermore, if the driver is heading to a specific destination, the data acquisition unit can acquire information related to that destination. For example, it can acquire parking information and tourist spot information around the destination. In addition, the data acquisition unit can prioritize acquiring and providing information that the driver has shown interest in. For example, it can prioritize acquiring information of interest based on information the driver has previously searched for or shared on social media. This allows for the priority provision of necessary information through data filtering based on driving status and areas of interest. Some or all of the above processing in the data acquisition unit is performed using a generating AI. For example, the data acquisition unit inputs data on the driver's driving status and areas of interest into the generating AI, which then analyzes and filters the data.

[0038] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the driver's geographical location information when acquiring data. For example, if the driver is in a specific area, the data acquisition unit can prioritize the acquisition of traffic information for that area. For example, the data acquisition unit can analyze the driver's GPS data and prioritize the acquisition of traffic information around the current location. Also, if the driver is approaching a destination, the data acquisition unit can prioritize the acquisition of information around the destination. For example, the data acquisition unit can acquire information on parking lots and tourist attractions around the destination. Furthermore, if the driver is traveling on a specific route, the data acquisition unit can prioritize the acquisition of information related to that route. For example, the data acquisition unit can prioritize the acquisition of traffic information and road conditions related to a specific route. This allows for the provision of highly relevant information through data acquisition based on geographical location information. Some or all of the above processing in the data acquisition unit is performed using a generation AI. For example, the data acquisition unit inputs the driver's geographical location information into the generation AI, which analyzes the data and prioritizes the acquisition of highly relevant data.

[0039] The data acquisition unit can analyze the driver's social media activity and acquire relevant data when acquiring data. For example, the data acquisition unit can acquire information related to places shared by the driver on social media. For example, the data acquisition unit can analyze the driver's social media posts and acquire information about the shared places. The data acquisition unit can also acquire information related to events that the driver has shown interest in on social media. For example, the data acquisition unit can analyze the driver's social media activity and acquire information about events that the driver has shown interest in. Furthermore, the data acquisition unit can acquire information related to accounts that the driver follows on social media. For example, the data acquisition unit can analyze the content of posts from accounts that the driver follows and acquire relevant information. This allows for the provision of information tailored to the driver's interests through data acquisition based on social media activity. Some or all of the above processing in the data acquisition unit is performed using a generation AI. For example, the data acquisition unit inputs the driver's social media activity data into the generation AI, which analyzes the data and acquires relevant information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis of important traffic information. For instance, it can use a traffic flow analysis algorithm to analyze traffic flow in detail. The analysis unit can also perform a simplified analysis of general road information. For example, it can simplify and analyze road information and provide it to drivers. Furthermore, the analysis unit can perform a rapid analysis of information that is of high urgency. For example, it can quickly analyze urgent traffic information and provide it to drivers. This enables efficient information provision by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs information about the importance of the data into the generation AI, and the generation AI analyzes the data and adjusts the level of detail.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a traffic flow analysis algorithm to traffic information. For example, the analysis unit can analyze traffic flow using a traffic flow analysis algorithm. The analysis unit can also apply a meteorological analysis algorithm to weather information. For example, the analysis unit can analyze meteorological data using a meteorological analysis algorithm. Furthermore, the analysis unit can apply a vehicle diagnostic algorithm to vehicle status. For example, the analysis unit can analyze the status of a vehicle using a vehicle diagnostic algorithm. This enables the provision of accurate information by applying analysis algorithms according to the data category. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs information about the data category into the generation AI, and the generation AI analyzes the data and applies an appropriate algorithm.

[0042] The analysis unit can determine the priority of analysis based on the data acquisition time during analysis. For example, the analysis unit can prioritize the analysis of the latest traffic information. For example, the analysis unit can prioritize the analysis of real-time traffic data and provide the latest traffic information. The analysis unit can also predict the current situation by referring to past data. For example, the analysis unit can analyze past traffic data and predict the current traffic situation. Furthermore, the analysis unit can also prioritize the analysis of real-time data. For example, the analysis unit can prioritize the analysis of real-time weather data and provide the latest weather information. This enables efficient information provision by determining the priority of analysis based on the data acquisition time. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs information about the data acquisition time into the generation AI, and the generation AI analyzes the data and determines the priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of important traffic information. For instance, it can use a traffic flow analysis algorithm to prioritize the analysis of traffic flow. The analysis unit can also prioritize the analysis of highly relevant data. For example, it can prioritize the analysis of highly relevant road information and provide it to drivers. Furthermore, the analysis unit can adjust the order of analysis based on the driver's interests. For example, it can prioritize the analysis of specific information based on the driver's interests. This allows for efficient information provision by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs information about the relevance of the data into the generative AI, which then analyzes the data and adjusts the order.

[0044] The projection unit can adjust the level of detail of the projection based on the importance of the information during projection. For example, the projection unit can display important traffic information in detail. For instance, it can use a traffic flow display algorithm to display traffic flow in detail. The projection unit can also display general road information in a simplified manner. For example, it can display simplified road information and provide it to drivers. Furthermore, the projection unit can quickly display information that is of high urgency. For example, it can quickly display urgent traffic information and provide it to drivers. This enables efficient information provision by adjusting the level of detail of the projection according to the importance of the information. Some or all of the above processing in the projection unit is performed using a generation AI. For example, the projection unit inputs information about the importance of the information into the generation AI, and the generation AI analyzes the data and adjusts the level of detail.

[0045] The projection unit can apply different projection algorithms depending on the category of information during projection. For example, the projection unit can apply a traffic flow display algorithm to traffic information. For example, the projection unit can display traffic flow using the traffic flow display algorithm. The projection unit can also apply a weather display algorithm to weather information. For example, the projection unit can display weather data using the weather display algorithm. Furthermore, the projection unit can apply a vehicle diagnostic display algorithm to vehicle status. For example, the projection unit can display the vehicle status using the vehicle diagnostic display algorithm. This enables the provision of accurate information by applying projection algorithms according to the category of information. Some or all of the above processing in the projection unit is performed using a generation AI. For example, the projection unit inputs information about the category of information into the generation AI, and the generation AI analyzes the data and applies an appropriate algorithm.

[0046] The projection unit can determine projection priorities based on the timing of information acquisition during projection. For example, the projection unit can prioritize the display of the latest traffic information. For instance, it can prioritize the display of real-time traffic data to provide the latest traffic information. The projection unit can also predict current conditions by referring to past data. For example, it can analyze past traffic data to predict current traffic conditions. Furthermore, the projection unit can prioritize the display of real-time data. For example, it can prioritize the display of real-time weather data to provide the latest weather information. This enables efficient information provision by determining projection priorities based on the timing of information acquisition. Some or all of the above processing in the projection unit is performed using a generation AI. For example, the projection unit inputs information about the timing of information acquisition into the generation AI, which analyzes the data and determines the priority.

[0047] The projection unit can adjust the order of projections based on the relevance of the information during projection. For example, the projection unit can prioritize the display of important traffic information. For instance, it can prioritize the display of traffic flow using a traffic flow display algorithm. The projection unit can also prioritize the display of highly relevant information. For example, it can prioritize the display of highly relevant road information and provide it to the driver. Furthermore, the projection unit can adjust the order of projections based on the driver's interests. For example, it can prioritize the display of specific information based on the driver's interests. This allows for efficient information provision by adjusting the order of projections based on the relevance of the information. Some or all of the above processing in the projection unit is performed using a generative AI. For example, the projection unit inputs information about the relevance of the information into the generative AI, which analyzes the data and adjusts the order.

[0048] The information provider can adjust the level of detail provided based on the importance of the information when offering advice or warnings. For example, the information provider can provide detailed advice or warnings for important traffic information. For instance, it can use a traffic flow advice algorithm to provide detailed advice on traffic flow. The information provider can also provide simplified advice or warnings for general road information. For example, it can provide simplified road information to drivers. Furthermore, the information provider can provide advice or warnings quickly for urgent information. For example, it can quickly provide urgent traffic information and issue warnings to drivers. This enables efficient information provision by adjusting the level of detail of advice and warnings according to the importance of the information. Some or all of the above processing in the information provider is performed using a generative AI. For example, the information provider inputs information on the importance of the information into the generative AI, which analyzes the data and adjusts the level of detail.

[0049] The information provider can apply different information provision algorithms depending on the information category when providing advice or warnings. For example, the information provider can apply a traffic flow advice algorithm to traffic information. For example, the information provider can use the traffic flow advice algorithm to provide advice on traffic flow. The information provider can also apply a weather advice algorithm to weather information. For example, the information provider can use the weather advice algorithm to provide advice on weather data. Furthermore, the information provider can apply a vehicle diagnostic advice algorithm to vehicle status. For example, the information provider can use the vehicle diagnostic advice algorithm to provide advice on vehicle status. This enables accurate information provision by applying information provision algorithms according to the information category. Some or all of the above processing in the information provider is performed using a generation AI. For example, the information provider inputs information about the information category into the generation AI, which analyzes the data and applies an appropriate algorithm.

[0050] The information delivery unit can prioritize the delivery of advice and warnings based on when the information was acquired. For example, the unit can prioritize the delivery of the latest traffic information. For example, the unit can prioritize the delivery of real-time traffic data to provide the latest traffic information. The unit can also predict current conditions by referring to past data. For example, the unit can analyze past traffic data to predict current traffic conditions. Furthermore, the unit can also prioritize the delivery of real-time data. For example, the unit can prioritize the delivery of real-time weather data to provide the latest weather information. This enables efficient information delivery by determining the priority of advice and warnings based on when the information was acquired. Some or all of the above processing in the information delivery unit is performed using a generative AI. For example, the unit inputs information about when the information was acquired into the generative AI, and the generative AI analyzes the data to determine the priority.

[0051] The information delivery unit can adjust the order in which information is delivered based on its relevance when providing advice and warnings. For example, the information delivery unit can prioritize the delivery of important traffic information. For example, the information delivery unit can use a traffic flow advice algorithm to prioritize the delivery of advice regarding traffic flow. The information delivery unit can also prioritize the delivery of highly relevant information. For example, the information delivery unit can prioritize the delivery of highly relevant road information and provide it to drivers. Furthermore, the information delivery unit can adjust the order in which information is delivered based on the driver's interests. For example, the information delivery unit can prioritize the delivery of specific information based on the driver's interests. This enables efficient information delivery by adjusting the order of advice and warnings based on the relevance of the information. Some or all of the above processing in the information delivery unit is performed using a generative AI. For example, the information delivery unit inputs information about the relevance of the information into the generative AI, and the generative AI analyzes the data and adjusts the order.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The information acquisition unit can learn the driver's driving style and provide individually optimized information. For example, if the driver frequently uses sudden braking, the unit can more closely monitor the distance to the vehicle ahead and issue warnings as needed. Also, if the driver frequently uses highways, the unit can prioritize acquiring and providing highway-related information. Furthermore, if the driver prefers night driving, the unit can prioritize acquiring night-specific information (e.g., highly visible signs and road lighting conditions). This enables the provision of information tailored to the driver's driving style, improving driving safety and comfort.

[0054] The analysis unit can suggest improvements to the driver's driving based on their driving history. For example, if a driver frequently brakes suddenly, the analysis unit can analyze the cause and suggest smoother braking techniques. Furthermore, if a driver frequently stops at a particular intersection, the analysis unit can analyze the traffic conditions at that intersection and suggest the optimal timing for passing through. Additionally, if a driver experiences poor fuel efficiency at a specific speed range, the analysis unit can suggest driving methods to avoid that speed range. This is expected to improve both the driver's driving skills and fuel efficiency.

[0055] The system can monitor the driver's health while driving and suggest breaks as needed. For example, it can monitor the driver's heart rate and blood pressure and suggest a break if an abnormality is detected. It can also estimate the driver's fatigue level and suggest a break when a certain level of fatigue is reached. Furthermore, it can monitor the driver's hydration and suggest hydration if there is a risk of dehydration. This helps maintain the driver's health and supports safe driving.

[0056] The data acquisition unit can learn the driver's behavior while driving and analyze driving patterns. For example, if the driver frequently stops at a particular intersection, the unit can analyze the traffic conditions at that intersection and suggest the optimal timing for passing through. Furthermore, if the driver's fuel efficiency deteriorates at a particular speed range, the unit can suggest driving methods to avoid that speed range. Additionally, if the driver frequently uses a particular route, the unit can prioritize acquiring and providing information related to that route. This enables the provision of information tailored to the driver's driving patterns, improving driving efficiency and comfort.

[0057] The analysis unit can analyze the driver's behavior while driving and suggest ways to improve driving. For example, if the driver frequently brakes suddenly, the analysis unit can analyze the cause and suggest smoother braking methods. Furthermore, if the driver frequently stops at a particular intersection, the analysis unit can analyze the traffic conditions at that intersection and suggest the optimal timing for passing through. In addition, if the driver's fuel efficiency deteriorates at a specific speed range, the analysis unit can suggest driving methods to avoid that speed range. This is expected to improve both the driver's driving skills and fuel efficiency.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The acquisition unit acquires data from in-vehicle cameras and sensors. For example, it uses an in-vehicle camera to photograph the surrounding traffic conditions and acquire image data. It also uses sensors to detect road information and vehicle status and acquire sensor data. Specifically, it can acquire vehicle location information using a GPS sensor and weather information using a weather sensor. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. Using a generation AI, it analyzes the acquired data in real time to identify important information and points of caution. For example, it uses an image analysis algorithm to analyze image data acquired from an in-vehicle camera to identify the location of vehicles and traffic lights ahead. It also uses data mining techniques to analyze sensor data to identify road conditions and traffic situations. Step 3: The projection unit projects the information identified by the analysis unit onto the glasses. Display technology is used to display the information on the lenses of the glasses. For example, HUD (Head-Up Display) technology or AR (Augmented Reality) technology is used to project the information into the field of view. Specifically, information such as road signs and traffic lights can be overlaid and displayed in the field of view. Step 4: The providing unit provides advice and warnings based on the information projected by the projection unit. It provides voice advice and warnings to the driver using bone conduction speakers. For example, it may notify the driver of changes in traffic signals or sudden braking by the vehicle ahead. It also provides visual advice and warnings using visual alerts. Specifically, warning messages can be displayed on the display.

[0060] (Example of form 2) The drive support agent according to an embodiment of the present invention is a glasses-type device that utilizes generative AI to enable drivers to enjoy safe and comfortable driving. This drive support agent uses generative AI to analyze data acquired from in-vehicle cameras and sensors, analyzing surrounding traffic conditions and road information in real time. The generative AI identifies important information and points of attention and projects them onto the glasses. This allows the driver to check the information without moving their eyes. Furthermore, the generative AI provides advice and warnings through bone conduction speakers. This allows the driver to receive information both visually and aurally, making it easier to maintain attention. This mechanism reduces the stress of long-distance driving and driving in unfamiliar areas, and improves safety. For example, even when fatigue and distraction tend to occur during long-distance driving, the generative AI supports the driver's attention by providing important information in real time. Also, when driving in unfamiliar areas, even when it is difficult to instantly grasp road signs and traffic conditions, the generative AI reduces the burden on the driver by providing the necessary information. In this way, the drive support agent can provide information that enables drivers to enjoy safe and comfortable driving.

[0061] The drive support agent according to this embodiment comprises an acquisition unit, an analysis unit, a projection unit, and a provision unit. The acquisition unit acquires data from in-vehicle cameras and sensors. For example, the acquisition unit can use an in-vehicle camera to photograph the surrounding traffic conditions and acquire image data. The acquisition unit can also use sensors to detect road information and vehicle status and acquire sensor data. For example, the acquisition unit can use a GPS sensor to acquire vehicle location information. Furthermore, the acquisition unit can use a weather sensor to acquire weather information. For example, the acquisition unit can use a temperature sensor to measure the temperature and acquire data. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit uses a generation AI to analyze the acquired data in real time and identify important information and points of caution. For example, the analysis unit can use an image analysis algorithm to analyze image data acquired from an in-vehicle camera and identify the location of vehicles and traffic lights ahead. Furthermore, the analysis unit can use data mining techniques to analyze sensor data and identify road conditions and traffic conditions. For example, the analysis unit can use data mining techniques to analyze road congestion and identify traffic congestion information. The projection unit projects information identified by the analysis unit onto the eyeglasses. The projection unit can display information on the lenses of the eyeglasses using display technology. For example, the projection unit can project information into the field of view using HUD (Head-Up Display) technology. The projection unit can also overlay information onto the real-world scenery using AR (Augmented Reality) technology. For example, the projection unit can overlay information on road signs and traffic lights onto the field of view using AR technology. The information provider unit provides advice and warnings based on the information projected by the projection unit. The information provider unit can provide advice and warnings to the driver via voice using bone conduction speakers. For example, the information provider unit can use bone conduction speakers to inform the driver of changes in traffic signals or sudden braking by vehicles ahead. The information provider unit can also provide advice and warnings to the driver visually using visual alerts. For example, the information provider unit can display warning messages on a display.This enables the drive support agent according to the embodiment to provide information that allows the driver to enjoy driving safely and comfortably.

[0062] The data acquisition unit acquires data from in-vehicle cameras and sensors. For example, the data acquisition unit can use an in-vehicle camera to photograph the surrounding traffic conditions and acquire image data. Specifically, the in-vehicle camera can capture high-resolution video in real time, allowing for detailed information on vehicles, pedestrians, traffic lights, road signs, and other objects ahead. The data acquisition unit can also use sensors to detect road information and vehicle status and acquire sensor data. For example, a LiDAR sensor mounted on the vehicle can accurately measure the distance to surrounding objects and understand the position and movement of obstacles. Furthermore, the data acquisition unit can acquire vehicle location information using a GPS sensor. A GPS sensor receives signals from satellites and can pinpoint the vehicle's current location with high accuracy. This allows the data acquisition unit to understand information such as the vehicle's travel path, speed, and direction of travel in real time. In addition, the data acquisition unit can acquire weather information using weather sensors. For example, a temperature sensor can measure the temperature, a humidity sensor can detect humidity, and a rain sensor can measure rainfall. This allows the data acquisition unit to understand changes in weather and road conditions in real time and provide appropriate information to the driver. This data is transmitted to the central control unit within the vehicle and used for processing in the analysis unit. The acquisition unit integrates data from these various sensors, enabling it to comprehensively understand the vehicle's surrounding environment. This allows the acquisition unit to provide the driver with fundamental information to support safe and comfortable driving.

[0063] The analysis unit analyzes the data acquired by the acquisition unit. Using generative AI, the analysis unit analyzes the acquired data in real time to identify important information and points of caution. Specifically, it can use image analysis algorithms to analyze image data acquired from in-vehicle cameras and identify the positions of vehicles and traffic lights ahead. The generative AI utilizes deep learning technology and learns from a vast dataset to achieve highly accurate image recognition. For example, the generative AI can analyze the shape, color, and movement patterns of vehicles to identify the type, distance, and speed of vehicles ahead. It can also analyze the color and illumination status of traffic lights to detect changes in signals in real time. Furthermore, the analysis unit can use data mining techniques to analyze sensor data and identify road conditions and traffic situations. For example, it can use data mining techniques to analyze road congestion and identify traffic jam information. This allows the analysis unit to suggest the optimal route to the driver and provide information to avoid traffic jams. In addition, the analysis unit can monitor the vehicle's condition and detect abnormalities. For example, it can monitor engine temperature, oil condition, tire pressure, etc., and issue a warning to the driver if an abnormality is detected. This allows the analysis unit to ensure vehicle safety and support the prevention and early response to malfunctions. By integrating this diverse data and performing comprehensive analysis, the analysis unit can provide comprehensive support to drivers.

[0064] The projection unit projects information identified by the analysis unit onto the eyeglasses. The projection unit can display information on the lenses of the eyeglasses using display technology. Specifically, it can project information into the driver's field of view using HUD (Head-Up Display) technology. HUD technology uses a transparent display to show information without obstructing the driver's view. For example, the projection unit can project information such as the position and speed of vehicles ahead, traffic light status, and road sign information into the driver's field of view, providing important information to the driver in real time. Furthermore, the projection unit can also overlay information onto the real-world landscape using AR (Augmented Reality) technology. AR technology overlays digital information onto the real-world landscape, enabling intuitive and easy-to-understand information delivery. For example, the projection unit can overlay information on road signs and traffic lights onto the driver's field of view using AR technology. This allows the driver to simultaneously view the real-world landscape and digital information, obtaining necessary information while maintaining attention while driving. Additionally, the projection unit allows for customization of how information is displayed. For example, the type, position, and size of the displayed information can be adjusted according to the driver's preference. This allows the projection unit to provide optimal information to the driver, supporting safe and comfortable driving. By utilizing these technologies, the projection unit can provide visually easy-to-understand information to the driver, delivering necessary information while maintaining their attention during driving.

[0065] The service provider provides advice and warnings based on information projected by the projection unit. The service provider can also provide voice advice and warnings to the driver using bone conduction speakers. Specifically, bone conduction speakers utilize technology to transmit sound through the skull, providing clear audio to the driver. For example, the service provider can notify the driver of changes in traffic signals or sudden braking by vehicles ahead. This allows the driver to obtain important information not only through visual information but also through auditory information. Furthermore, the service provider can provide visual advice and warnings to the driver using visual alerts. For example, the service provider can display warning messages on the display, allowing the driver to visually confirm important information. In addition, the service provider can monitor the driver's condition and provide advice and warnings at appropriate times. For example, it can detect driver fatigue and provide advice encouraging them to take a break. The service provider can also analyze the driver's driving style and provide advice for safe driving. This allows the service provider to provide comprehensive support to the driver, assisting in safe and comfortable driving. The service provider can utilize these technologies to provide drivers with appropriate advice and warnings, thereby improving safety while driving.

[0066] The analysis unit can analyze data including traffic conditions, road information, weather information, and vehicle status. For example, the analysis unit can analyze data related to traffic conditions and provide real-time traffic information. For instance, it can use a traffic flow analysis algorithm to analyze traffic flow and identify congestion information. The analysis unit can also analyze data related to road information and identify road conditions. For example, it can analyze road construction information and accident information and inform drivers. Furthermore, the analysis unit can analyze data related to weather information and identify meteorological conditions. For example, it can analyze data acquired from weather sensors and identify temperature and precipitation. The analysis unit can also analyze data related to vehicle status and identify vehicle status. For example, it can analyze engine status and tire pressure and inform drivers. This enables the provision of more accurate information by analyzing diverse data. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs data related to traffic conditions, road information, weather information, and vehicle status into the generation AI, which analyzes the data and identifies important information.

[0067] The system can provide advice and warnings, including those related to changes in traffic signals, sudden braking by vehicles ahead, speeding, and lane departures. For example, the system can detect changes in traffic signals and notify the driver. For example, it can detect changes in the color of traffic lights and notify the driver when the light turns red. The system can also detect sudden braking by vehicles ahead and warn the driver. For example, it can detect a sudden decrease in the distance between vehicles and notify the driver when the vehicle ahead has braked suddenly. Furthermore, the system can detect speeding and warn the driver. For example, it can warn the driver if the speed limit is exceeded. The system can also detect lane departures and warn the driver. For example, it can recognize lane markers and warn the driver when the vehicle deviates from its lane. This improves driver safety by providing specific advice and warnings. Some or all of the above processing in the system is performed using generative AI. For example, the data provider inputs information about changes in traffic signals, sudden braking by vehicles ahead, speeding, and lane departures into the generating AI, which then analyzes the data to provide advice and warnings.

[0068] The data acquisition unit can estimate the driver's emotions and adjust the timing of data acquisition based on the estimated emotions. For example, if the driver is tense, the unit can acquire data frequently to provide information in real time. For example, the unit can capture the driver's facial expressions with a camera and estimate the driver's emotions using an emotion estimation algorithm. The unit can also reduce the frequency of data acquisition and provide only necessary information if the driver is relaxed. For example, the unit can record the driver's voice and estimate the driver's emotions using voice analysis technology. Furthermore, if the driver is tired, the unit can prioritize acquiring important information and draw their attention. For example, the unit can collect the driver's biometric data (heart rate and skin electrical activity) with sensors and estimate the driver's emotions using an emotion estimation algorithm. This allows for the provision of appropriate information by adjusting the timing of data acquisition according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the acquisition unit is performed using a generating AI. For example, the acquisition unit inputs the driver's facial expression data into the generating AI, which estimates the driver's emotions and adjusts the timing of data acquisition.

[0069] The data acquisition unit can analyze the driver's past driving history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize acquiring data on routes that the driver has frequently traveled in the past. For example, the data acquisition unit can analyze the driver's driving history data and prioritize acquiring data related to a specific route. The data acquisition unit can also predict and acquire data necessary for a specific time period based on the driver's past driving history. For example, the data acquisition unit can analyze the driver's driving history data and prioritize acquiring traffic information for a specific time period. Furthermore, the data acquisition unit can analyze the driver's past driving patterns and select the optimal data acquisition timing. For example, the data acquisition unit can analyze the driver's driving pattern data and select the optimal data acquisition timing. This enables efficient information provision by selecting a data acquisition method based on past driving history. Some or all of the above processing in the data acquisition unit is performed using a generating AI. For example, the data acquisition unit inputs the driver's driving history data into the generating AI, which analyzes the data and selects the optimal data acquisition method.

[0070] The data acquisition unit can filter data based on the driver's current driving status and areas of interest. For example, if the driver is driving on a highway, the data acquisition unit will prioritize acquiring information related to the highway. For instance, it can prioritize acquiring highway traffic information and service area information. Furthermore, if the driver is heading to a specific destination, the data acquisition unit can acquire information related to that destination. For example, it can acquire parking information and tourist spot information around the destination. In addition, the data acquisition unit can prioritize acquiring and providing information that the driver has shown interest in. For example, it can prioritize acquiring information of interest based on information the driver has previously searched for or shared on social media. This allows for the priority provision of necessary information through data filtering based on driving status and areas of interest. Some or all of the above processing in the data acquisition unit is performed using a generating AI. For example, the data acquisition unit inputs data on the driver's driving status and areas of interest into the generating AI, which then analyzes and filters the data.

[0071] The data acquisition unit can estimate the driver's emotions and determine the priority of data to acquire based on the estimated emotions. For example, if the driver is tense, the unit will prioritize acquiring important traffic information. For example, the unit can capture the driver's facial expressions with a camera and estimate the driver's emotions using an emotion estimation algorithm. Also, if the driver is relaxed, the unit can prioritize acquiring general road information. For example, the unit can record the driver's voice and estimate the driver's emotions using voice analysis technology. Furthermore, if the driver is tired, the unit can prioritize acquiring information that requires attention. For example, the unit can collect the driver's biometric data (heart rate and skin electrical activity) with sensors and estimate the driver's emotions using an emotion estimation algorithm. This allows for the priority provision of important information by prioritizing data according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the acquisition unit is performed using a generating AI. For example, the acquisition unit inputs the driver's facial expression data into the generating AI, which estimates the driver's emotions and determines the priority of the data.

[0072] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the driver's geographical location information when acquiring data. For example, if the driver is in a specific area, the data acquisition unit can prioritize the acquisition of traffic information for that area. For example, the data acquisition unit can analyze the driver's GPS data and prioritize the acquisition of traffic information around the current location. Also, if the driver is approaching a destination, the data acquisition unit can prioritize the acquisition of information around the destination. For example, the data acquisition unit can acquire information on parking lots and tourist attractions around the destination. Furthermore, if the driver is traveling on a specific route, the data acquisition unit can prioritize the acquisition of information related to that route. For example, the data acquisition unit can prioritize the acquisition of traffic information and road conditions related to a specific route. This allows for the provision of highly relevant information through data acquisition based on geographical location information. Some or all of the above processing in the data acquisition unit is performed using a generation AI. For example, the data acquisition unit inputs the driver's geographical location information into the generation AI, which analyzes the data and prioritizes the acquisition of highly relevant data.

[0073] The data acquisition unit can analyze the driver's social media activity and acquire relevant data when acquiring data. For example, the data acquisition unit can acquire information related to places shared by the driver on social media. For example, the data acquisition unit can analyze the driver's social media posts and acquire information about the shared places. The data acquisition unit can also acquire information related to events that the driver has shown interest in on social media. For example, the data acquisition unit can analyze the driver's social media activity and acquire information about events that the driver has shown interest in. Furthermore, the data acquisition unit can acquire information related to accounts that the driver follows on social media. For example, the data acquisition unit can analyze the content of posts from accounts that the driver follows and acquire relevant information. This allows for the provision of information tailored to the driver's interests through data acquisition based on social media activity. Some or all of the above processing in the data acquisition unit is performed using a generation AI. For example, the data acquisition unit inputs the driver's social media activity data into the generation AI, which analyzes the data and acquires relevant information.

[0074] The analysis unit can estimate the driver's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the driver is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, the analysis unit can capture the driver's facial expressions with a camera and estimate the driver's emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the driver is relaxed. For example, the analysis unit can record the driver's voice and estimate the driver's emotions using voice analysis technology. Furthermore, if the driver is in a hurry, the analysis unit can provide concise analysis results. For example, the analysis unit can collect the driver's biometric data (heart rate and skin electrical activity) with sensors and estimate the driver's emotions using an emotion estimation algorithm. This allows for the provision of appropriate information by adjusting the presentation of the analysis according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using a generating AI. For example, the analysis unit inputs the driver's facial expression data into the generating AI, which estimates the driver's emotions and adjusts the method of expression for the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis of important traffic information. For instance, it can use a traffic flow analysis algorithm to analyze traffic flow in detail. The analysis unit can also perform a simplified analysis of general road information. For example, it can simplify and analyze road information and provide it to drivers. Furthermore, the analysis unit can perform a rapid analysis of information that is of high urgency. For example, it can quickly analyze urgent traffic information and provide it to drivers. This enables efficient information provision by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs information about the importance of the data into the generation AI, and the generation AI analyzes the data and adjusts the level of detail.

[0076] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a traffic flow analysis algorithm to traffic information. For example, the analysis unit can analyze traffic flow using a traffic flow analysis algorithm. The analysis unit can also apply a meteorological analysis algorithm to weather information. For example, the analysis unit can analyze meteorological data using a meteorological analysis algorithm. Furthermore, the analysis unit can apply a vehicle diagnostic algorithm to vehicle status. For example, the analysis unit can analyze the status of a vehicle using a vehicle diagnostic algorithm. This enables the provision of accurate information by applying analysis algorithms according to the data category. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs information about the data category into the generation AI, and the generation AI analyzes the data and applies an appropriate algorithm.

[0077] The analysis unit can estimate the driver's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the driver is tense, the analysis unit can provide a short, concise analysis. For example, the analysis unit can capture the driver's facial expressions with a camera and estimate the driver's emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the driver is relaxed. For example, the analysis unit can record the driver's voice and estimate the driver's emotions using voice analysis technology. Furthermore, if the driver is in a hurry, the analysis unit can provide analysis results quickly. For example, the analysis unit can collect the driver's biometric data (heart rate and skin electrical activity) with sensors and estimate the driver's emotions using an emotion estimation algorithm. This allows for the provision of appropriate information by adjusting the length of the analysis according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using a generating AI. For example, the analysis unit inputs the driver's facial expression data into the generating AI, which estimates the driver's emotions and adjusts the length of the analysis.

[0078] The analysis unit can determine the priority of analysis based on the data acquisition time during analysis. For example, the analysis unit can prioritize the analysis of the latest traffic information. For example, the analysis unit can prioritize the analysis of real-time traffic data and provide the latest traffic information. The analysis unit can also predict the current situation by referring to past data. For example, the analysis unit can analyze past traffic data and predict the current traffic situation. Furthermore, the analysis unit can also prioritize the analysis of real-time data. For example, the analysis unit can prioritize the analysis of real-time weather data and provide the latest weather information. This enables efficient information provision by determining the priority of analysis based on the data acquisition time. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs information about the data acquisition time into the generation AI, and the generation AI analyzes the data and determines the priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of important traffic information. For instance, it can use a traffic flow analysis algorithm to prioritize the analysis of traffic flow. The analysis unit can also prioritize the analysis of highly relevant data. For example, it can prioritize the analysis of highly relevant road information and provide it to drivers. Furthermore, the analysis unit can adjust the order of analysis based on the driver's interests. For example, it can prioritize the analysis of specific information based on the driver's interests. This allows for efficient information provision by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs information about the relevance of the data into the generative AI, which then analyzes the data and adjusts the order.

[0080] The projection unit can estimate the driver's emotions and adjust the display method of the projection based on the estimated emotions of the driver. For example, if the driver is tense, the projection unit can provide a simple and highly visible display method. For example, the projection unit can capture the driver's facial expression with a camera and estimate the driver's emotions using an emotion estimation algorithm. Also, if the driver is relaxed, the projection unit can provide a display method that includes detailed information. For example, the projection unit can record the driver's voice and estimate the driver's emotions using voice analysis technology. Furthermore, if the driver is in a hurry, the projection unit can provide a concise display method. For example, the projection unit can collect the driver's biometric data (heart rate and skin electrical activity) with sensors and estimate the driver's emotions using an emotion estimation algorithm. This makes it possible to provide appropriate information by adjusting the display method of the projection according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the projection unit is performed using a generating AI. For example, the projection unit inputs the driver's facial expression data into the generating AI, which estimates the driver's emotions and adjusts the projection display method.

[0081] The projection unit can adjust the level of detail of the projection based on the importance of the information during projection. For example, the projection unit can display important traffic information in detail. For instance, it can use a traffic flow display algorithm to display traffic flow in detail. The projection unit can also display general road information in a simplified manner. For example, it can display simplified road information and provide it to drivers. Furthermore, the projection unit can quickly display information that is of high urgency. For example, it can quickly display urgent traffic information and provide it to drivers. This enables efficient information provision by adjusting the level of detail of the projection according to the importance of the information. Some or all of the above processing in the projection unit is performed using a generation AI. For example, the projection unit inputs information about the importance of the information into the generation AI, and the generation AI analyzes the data and adjusts the level of detail.

[0082] The projection unit can apply different projection algorithms depending on the category of information during projection. For example, the projection unit can apply a traffic flow display algorithm to traffic information. For example, the projection unit can display traffic flow using the traffic flow display algorithm. The projection unit can also apply a weather display algorithm to weather information. For example, the projection unit can display weather data using the weather display algorithm. Furthermore, the projection unit can apply a vehicle diagnostic display algorithm to vehicle status. For example, the projection unit can display the vehicle status using the vehicle diagnostic display algorithm. This enables the provision of accurate information by applying projection algorithms according to the category of information. Some or all of the above processing in the projection unit is performed using a generation AI. For example, the projection unit inputs information about the category of information into the generation AI, and the generation AI analyzes the data and applies an appropriate algorithm.

[0083] The projection unit can estimate the driver's emotions and adjust the length of the projection based on the estimated emotions. For example, if the driver is tense, the projection unit will display a short, concise message. For example, the projection unit can capture the driver's facial expression with a camera and estimate the driver's emotions using an emotion estimation algorithm. The projection unit can also display a detailed message if the driver is relaxed. For example, the projection unit can record the driver's voice and estimate the driver's emotions using voice analysis technology. Furthermore, the projection unit can display a message quickly if the driver is in a hurry. For example, the projection unit can collect the driver's biometric data (heart rate and skin electrical activity) with sensors and estimate the driver's emotions using an emotion estimation algorithm. This allows for the provision of appropriate information by adjusting the length of the projection according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the projection unit are performed using generative AI. For example, the projection unit inputs the driver's facial expression data into a generating AI, which estimates the driver's emotions and adjusts the projection length.

[0084] The projection unit can determine projection priorities based on the timing of information acquisition during projection. For example, the projection unit can prioritize the display of the latest traffic information. For instance, it can prioritize the display of real-time traffic data to provide the latest traffic information. The projection unit can also predict current conditions by referring to past data. For example, it can analyze past traffic data to predict current traffic conditions. Furthermore, the projection unit can prioritize the display of real-time data. For example, it can prioritize the display of real-time weather data to provide the latest weather information. This enables efficient information provision by determining projection priorities based on the timing of information acquisition. Some or all of the above processing in the projection unit is performed using a generation AI. For example, the projection unit inputs information about the timing of information acquisition into the generation AI, which analyzes the data and determines the priority.

[0085] The projection unit can adjust the order of projections based on the relevance of the information during projection. For example, the projection unit can prioritize the display of important traffic information. For instance, it can prioritize the display of traffic flow using a traffic flow display algorithm. The projection unit can also prioritize the display of highly relevant information. For example, it can prioritize the display of highly relevant road information and provide it to the driver. Furthermore, the projection unit can adjust the order of projections based on the driver's interests. For example, it can prioritize the display of specific information based on the driver's interests. This allows for efficient information provision by adjusting the order of projections based on the relevance of the information. Some or all of the above processing in the projection unit is performed using a generative AI. For example, the projection unit inputs information about the relevance of the information into the generative AI, which analyzes the data and adjusts the order.

[0086] The system can estimate the driver's emotions and adjust the way advice and warnings are delivered based on the estimated emotions. For example, if the driver is tense, the system can provide advice and warnings in a calm voice. For example, the system can capture the driver's facial expressions with a camera and estimate the driver's emotions using an emotion estimation algorithm. The system can also provide advice and warnings in a cheerful voice if the driver is relaxed. For example, the system can record the driver's voice and estimate the driver's emotions using voice analysis technology. Furthermore, if the driver is in a hurry, the system can provide quick and concise advice and warnings. For example, the system can collect the driver's biometric data (heart rate and skin electrical activity) with sensors and estimate the driver's emotions using an emotion estimation algorithm. This allows for the provision of appropriate information by adjusting the way advice and warnings are delivered according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service delivery unit is performed using a generating AI. For example, the service delivery unit inputs the driver's facial expression data into the generating AI, which estimates the driver's emotions and adjusts the way advice and warnings are expressed.

[0087] The information provider can adjust the level of detail provided based on the importance of the information when offering advice or warnings. For example, the information provider can provide detailed advice or warnings for important traffic information. For instance, it can use a traffic flow advice algorithm to provide detailed advice on traffic flow. The information provider can also provide simplified advice or warnings for general road information. For example, it can provide simplified road information to drivers. Furthermore, the information provider can provide advice or warnings quickly for urgent information. For example, it can quickly provide urgent traffic information and issue warnings to drivers. This enables efficient information provision by adjusting the level of detail of advice and warnings according to the importance of the information. Some or all of the above processing in the information provider is performed using a generative AI. For example, the information provider inputs information on the importance of the information into the generative AI, which analyzes the data and adjusts the level of detail.

[0088] The information provider can apply different information provision algorithms depending on the information category when providing advice or warnings. For example, the information provider can apply a traffic flow advice algorithm to traffic information. For example, the information provider can use the traffic flow advice algorithm to provide advice on traffic flow. The information provider can also apply a weather advice algorithm to weather information. For example, the information provider can use the weather advice algorithm to provide advice on weather data. Furthermore, the information provider can apply a vehicle diagnostic advice algorithm to vehicle status. For example, the information provider can use the vehicle diagnostic advice algorithm to provide advice on vehicle status. This enables accurate information provision by applying information provision algorithms according to the information category. Some or all of the above processing in the information provider is performed using a generation AI. For example, the information provider inputs information about the information category into the generation AI, which analyzes the data and applies an appropriate algorithm.

[0089] The system can estimate the driver's emotions and adjust the length of advice and warnings based on the estimated emotions. For example, if the driver is tense, the system can provide short, concise advice and warnings. For example, the system can capture the driver's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The system can also provide detailed advice and warnings if the driver is relaxed. For example, the system can record the driver's voice and estimate their emotions using voice analysis technology. Furthermore, if the driver is in a hurry, the system can provide quick advice and warnings. For example, the system can collect the driver's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of appropriate information by adjusting the length of advice and warnings according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service delivery unit is performed using a generating AI. For example, the service delivery unit inputs the driver's facial expression data into the generating AI, which estimates the driver's emotions and adjusts the length of advice and warnings accordingly.

[0090] The information delivery unit can prioritize the delivery of advice and warnings based on when the information was acquired. For example, the unit can prioritize the delivery of the latest traffic information. For example, the unit can prioritize the delivery of real-time traffic data to provide the latest traffic information. The unit can also predict current conditions by referring to past data. For example, the unit can analyze past traffic data to predict current traffic conditions. Furthermore, the unit can also prioritize the delivery of real-time data. For example, the unit can prioritize the delivery of real-time weather data to provide the latest weather information. This enables efficient information delivery by determining the priority of advice and warnings based on when the information was acquired. Some or all of the above processing in the information delivery unit is performed using a generative AI. For example, the unit inputs information about when the information was acquired into the generative AI, and the generative AI analyzes the data to determine the priority.

[0091] The information delivery unit can adjust the order in which information is delivered based on its relevance when providing advice and warnings. For example, the information delivery unit can prioritize the delivery of important traffic information. For example, the information delivery unit can use a traffic flow advice algorithm to prioritize the delivery of advice regarding traffic flow. The information delivery unit can also prioritize the delivery of highly relevant information. For example, the information delivery unit can prioritize the delivery of highly relevant road information and provide it to drivers. Furthermore, the information delivery unit can adjust the order in which information is delivered based on the driver's interests. For example, the information delivery unit can prioritize the delivery of specific information based on the driver's interests. This enables efficient information delivery by adjusting the order of advice and warnings based on the relevance of the information. Some or all of the above processing in the information delivery unit is performed using a generative AI. For example, the information delivery unit inputs information about the relevance of the information into the generative AI, and the generative AI analyzes the data and adjusts the order.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The information acquisition unit can learn the driver's driving style and provide individually optimized information. For example, if the driver frequently uses sudden braking, the unit can more closely monitor the distance to the vehicle ahead and issue warnings as needed. Also, if the driver frequently uses highways, the unit can prioritize acquiring and providing highway-related information. Furthermore, if the driver prefers night driving, the unit can prioritize acquiring night-specific information (e.g., highly visible signs and road lighting conditions). This enables the provision of information tailored to the driver's driving style, improving driving safety and comfort.

[0094] The analysis unit can suggest improvements to the driver's driving based on their driving history. For example, if a driver frequently brakes suddenly, the analysis unit can analyze the cause and suggest smoother braking techniques. Furthermore, if a driver frequently stops at a particular intersection, the analysis unit can analyze the traffic conditions at that intersection and suggest the optimal timing for passing through. Additionally, if a driver experiences poor fuel efficiency at a specific speed range, the analysis unit can suggest driving methods to avoid that speed range. This is expected to improve both the driver's driving skills and fuel efficiency.

[0095] The system can monitor the driver's health while driving and suggest breaks as needed. For example, it can monitor the driver's heart rate and blood pressure and suggest a break if an abnormality is detected. It can also estimate the driver's fatigue level and suggest a break when a certain level of fatigue is reached. Furthermore, it can monitor the driver's hydration and suggest hydration if there is a risk of dehydration. This helps maintain the driver's health and supports safe driving.

[0096] The system can estimate the driver's emotions and provide music or entertainment content based on those emotions. For example, if the driver is tense, the system can provide relaxing music. If the driver is tired, it can provide uplifting music. Furthermore, if the driver is bored, it can provide engaging entertainment content. By providing entertainment content tailored to the driver's emotions, this system can reduce stress while driving and support a comfortable driving experience.

[0097] The analysis unit can estimate the driver's emotions and adjust the content of driving advice based on those emotions. For example, if the driver is tense, the analysis unit can provide advice in a calm tone. If the driver is relaxed, the analysis unit can provide detailed advice. Furthermore, if the driver is in a hurry, the analysis unit can provide concise and quick advice. This enables appropriate support by providing driving advice tailored to the driver's emotions.

[0098] The system can estimate the driver's emotions and adjust in-driving communication based on those emotions. For example, if the driver is tense, the system can speak in a calm voice. If the driver is relaxed, it can speak in a cheerful voice. Furthermore, if the driver is in a hurry, the system can provide quick and concise communication. By adjusting communication according to the driver's emotions, it can reduce stress while driving and support a comfortable driving experience.

[0099] The sensor can estimate the driver's emotions and adjust the lighting during driving based on the estimated emotions. For example, if the driver is tense, the sensor can provide calming lighting. If the driver is relaxed, it can provide bright lighting. Furthermore, if the driver is tired, it can provide eye-friendly lighting. This allows for a comfortable driving environment by adjusting the lighting according to the driver's emotions.

[0100] The system can estimate the driver's emotions and adjust the navigation guidance during driving based on those emotions. For example, if the driver is tense, the system can provide navigation guidance in a calm voice. If the driver is relaxed, the system can provide detailed navigation guidance. Furthermore, if the driver is in a hurry, the system can provide concise and quick navigation guidance. This allows for appropriate support by adjusting navigation guidance according to the driver's emotions.

[0101] The data acquisition unit can learn the driver's behavior while driving and analyze driving patterns. For example, if the driver frequently stops at a particular intersection, the unit can analyze the traffic conditions at that intersection and suggest the optimal timing for passing through. Furthermore, if the driver's fuel efficiency deteriorates at a particular speed range, the unit can suggest driving methods to avoid that speed range. Additionally, if the driver frequently uses a particular route, the unit can prioritize acquiring and providing information related to that route. This enables the provision of information tailored to the driver's driving patterns, improving driving efficiency and comfort.

[0102] The analysis unit can analyze the driver's behavior while driving and suggest ways to improve driving. For example, if the driver frequently brakes suddenly, the analysis unit can analyze the cause and suggest smoother braking methods. Furthermore, if the driver frequently stops at a particular intersection, the analysis unit can analyze the traffic conditions at that intersection and suggest the optimal timing for passing through. In addition, if the driver's fuel efficiency deteriorates at a specific speed range, the analysis unit can suggest driving methods to avoid that speed range. This is expected to improve both the driver's driving skills and fuel efficiency.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The acquisition unit acquires data from in-vehicle cameras and sensors. For example, it uses an in-vehicle camera to photograph the surrounding traffic conditions and acquire image data. It also uses sensors to detect road information and vehicle status and acquire sensor data. Specifically, it can acquire vehicle location information using a GPS sensor and weather information using a weather sensor. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. Using a generation AI, it analyzes the acquired data in real time to identify important information and points of caution. For example, it uses an image analysis algorithm to analyze image data acquired from an in-vehicle camera to identify the location of vehicles and traffic lights ahead. It also uses data mining techniques to analyze sensor data to identify road conditions and traffic situations. Step 3: The projection unit projects the information identified by the analysis unit onto the glasses. Display technology is used to display the information on the lenses of the glasses. For example, HUD (Head-Up Display) technology or AR (Augmented Reality) technology is used to project the information into the field of view. Specifically, information such as road signs and traffic lights can be overlaid and displayed in the field of view. Step 4: The providing unit provides advice and warnings based on the information projected by the projection unit. It provides voice advice and warnings to the driver using bone conduction speakers. For example, it may notify the driver of changes in traffic signals or sudden braking by the vehicle ahead. It also provides visual advice and warnings using visual alerts. Specifically, warning messages can be displayed on the display.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the acquisition unit, analysis unit, projection unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires surrounding traffic conditions and road information using the camera 42 and sensors of the smart device 14. The analysis unit is implemented in real time by the identification processing unit 290 of the data processing unit 12. The projection unit projects information onto the glasses using the display 40A of the smart device 14. The provision unit provides voice advice and warnings using the bone conduction speaker of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the acquisition unit, analysis unit, projection unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires surrounding traffic conditions and road information using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the acquired data in real time. The projection unit projects information onto the glasses using the display of the smart glasses 214. The provision unit provides advice and warnings by voice using the bone conduction speaker of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the acquisition unit, analysis unit, projection unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires surrounding traffic conditions and road information using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the acquired data in real time. The projection unit projects information onto the glasses portion using the display 343 of the headset terminal 314. The provision unit provides advice and warnings by voice using the bone conduction speaker of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the acquisition unit, analysis unit, projection unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires surrounding traffic conditions and road information using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the acquired data in real time. The projection unit projects information onto the glasses using, for example, the display of the robot 414. The provision unit provides advice and warnings by voice using, for example, the bone conduction speaker of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) The acquisition unit acquires data from in-vehicle cameras and sensors, An analysis unit analyzes the data acquired by the acquisition unit, A projection unit that projects the information identified by the analysis unit onto the eyeglasses portion, The system includes a providing unit that provides advice and warnings based on the information projected by the projection unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze data including traffic conditions, road information, weather information, and vehicle status. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It provides advice and warnings including changes in traffic signals, sudden braking by vehicles ahead, speeding, and lane departure. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, The system estimates the driver's emotions and adjusts the timing of data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Analyze the driver's past driving history and select the optimal data acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, When acquiring data, filtering is performed based on the driver's current driving status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the driver's emotions and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring data, the system prioritizes the acquisition of highly relevant data, taking into account the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring data, the driver's social media activity is analyzed to obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The projection unit is It estimates the driver's emotions and adjusts how the projection is displayed based on the estimated emotions of the driver. The system described in Appendix 1, characterized by the features described herein. (Note 17) The projection unit is During projection, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The projection unit is During projection, different projection algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The projection unit is It estimates the driver's emotions and adjusts the projection length based on the estimated emotions of the driver. The system described in Appendix 1, characterized by the features described herein. (Note 20) The projection unit is During projection, the projection priority is determined based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 21) The projection unit is During projection, adjust the projection order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the driver's emotions and adjusts the way advice and warnings are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice or warnings, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice or warnings, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the driver's emotions and adjusts the length of advice and warnings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice or warnings, prioritize their delivery based on when the information was obtained. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice or warnings, adjust the order in which the information is presented based on its relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition unit acquires data from in-vehicle cameras and sensors, An analysis unit analyzes the data acquired by the acquisition unit, A projection unit that projects the information identified by the analysis unit onto the eyeglasses portion, The system includes a providing unit that provides advice and warnings based on the information projected by the projection unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze data including traffic conditions, road information, weather information, and vehicle status. The system according to feature 1.

3. The aforementioned supply unit is, It provides advice and warnings including changes in traffic signals, sudden braking by vehicles ahead, speeding, and lane departure. The system according to feature 1.

4. The acquisition unit is, The system estimates the driver's emotions and adjusts the timing of data acquisition based on the estimated emotions. The system according to feature 1.

5. The acquisition unit is, Analyze the driver's past driving history and select the optimal data acquisition method. The system according to feature 1.

6. The acquisition unit is, When acquiring data, filtering is performed based on the driver's current driving status and areas of interest. The system according to feature 1.

7. The acquisition unit is, The system estimates the driver's emotions and prioritizes the data to be collected based on those estimated emotions. The system according to feature 1.

8. The acquisition unit is, When acquiring data, the system prioritizes the acquisition of highly relevant data, taking into account the driver's geographical location. The system according to feature 1.

9. The acquisition unit is, When acquiring data, the driver's social media activity is analyzed to obtain relevant data. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the driver's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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