System

Abstract

An object of a system according to an embodiment is to enable a driver to quickly and accurately understand information displayed on a window of a vehicle.SOLUTION: A system includes an information display unit, an analysis unit, and a warning unit. The information display unit displays information on a window of the vehicle. The analysis unit uses a generation AI for generating information to be displayed on the information display unit. The warning unit displays a warning based on the information generated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult for drivers to quickly and accurately understand the information displayed on the car windows, which can increase the risk of accidents.

[0005] The system according to the embodiment aims to enable the driver to quickly and accurately understand the information displayed on the car window. [Means for solving the problem]

[0006] The system according to the embodiment includes an information display unit, an analysis unit, and a warning unit. The information display unit displays information on the vehicle window. The analysis unit uses a generation AI to generate information to be displayed on the information display unit. The warning unit displays a warning based on the information generated by the analysis unit. [Effects of the Invention]

[0007] A system according to an embodiment can enable a driver to quickly and accurately understand information displayed on the vehicle window. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A driving assistance system according to an embodiment of the present invention is a system that makes information displayed on a vehicle window visually easy to understand, allowing the driver to instantly grasp the necessary information. This allows the driver to obtain the necessary information without taking their eyes off the road, thereby reducing the risk of accidents.

[0029] A driving assistance system according to an embodiment includes an information display unit, an analysis unit, and a warning unit. The information display unit displays information on a vehicle window. For example, it can display information such as navigation information, speed limits, and traffic signs. The information display unit also displays information in a visually easy-to-understand format so that the driver can obtain necessary information without taking their eyes off the road. The analysis unit generates information to be displayed on the information display unit using a generation AI. For example, the generation AI analyzes information required by the driver in real time and displays it on the window at an appropriate time. The generation AI can also monitor the driver's driving status in real time and generate information to display a warning when a dangerous situation is detected. The warning unit displays a warning based on the information generated by the analysis unit. For example, it displays a warning when the vehicle is getting too close to a vehicle ahead or when the vehicle is about to deviate from its lane. This allows the driving assistance system to obtain necessary information without taking the driver's eyes off the road, thereby reducing the risk of an accident.

[0030] The information display unit can display navigation information, speed limits, and traffic sign information. For example, the information display unit displays navigation information to allow the driver to confirm the route to the destination. It also displays speed limit information to allow the driver to understand the current road speed limit. It also displays traffic sign information to allow the driver to understand the road conditions. This allows the driver to obtain the necessary information without taking their eyes off the road.

[0031] The warning unit can display a warning when the distance to the vehicle ahead is too close or when the vehicle is about to deviate from its lane. For example, the warning unit displays a warning when the distance to the vehicle ahead is too close, urging the driver to slow down. Also, the warning unit displays a warning when the vehicle is about to deviate from its lane, urging the driver to stay in the lane. This allows the driver to detect dangerous situations in advance and reduce the risk of an accident.

[0032] The analysis unit analyzes the eye-tracking data and can dynamically change the display position of the information display unit according to eye movements. For example, the analysis unit uses generative AI to analyze the driver's eye-tracking data in real time and dynamically change the display position of navigation information, speed limits, etc. according to eye movements. For example, if the driver is looking to the left, information is displayed in the left window. Furthermore, based on the eye-tracking data, a system is built to display information in the most visible position for the driver. For example, if the driver is looking ahead, information is displayed in the front window so that the driver can obtain the information without taking their eyes off the road. Furthermore, the eye-tracking data is analyzed and the display position is adjusted so that the driver does not miss the information. For example, when the driver moves their eyes, information is automatically displayed in the direction of their line of sight. This allows the driver to obtain the necessary information without moving their eyes.

[0033] The analysis unit can learn driving history and display information optimized for each individual driver. For example, the analysis unit uses generation AI to learn the driver's past driving history and display optimized information based on the driver's frequently used routes and preferred information display format. For example, speed limit information can be highlighted on specific routes. In addition, a system can be built that predicts the information the driver will need based on past driving history and displays it at the appropriate time. For example, traffic light information can be displayed in advance at intersections that the driver frequently passes through. Furthermore, the generation AI can learn the driver's driving patterns and display information tailored to each individual driver. For example, if a driver frequently uses expressways, traffic congestion information will be displayed preferentially. This allows the driver to obtain information optimized based on their past driving history.

[0034] The information display unit can display information not only on the car windows, but also on the side mirrors and rearview mirror. For example, the information display unit uses generative AI to build a system that displays information such as navigation information and speed limits not only on the car windows, but also on the side mirrors and rearview mirror. For example, lane change instructions can be displayed on the side mirrors. In addition, by displaying information on the side mirrors and rearview mirrors, the driver can obtain information in all directions. For example, rear traffic conditions can be displayed on the rearview mirror so the driver can check without taking their eyes off the road. Furthermore, information can be displayed synchronously on the car windows, side mirrors, and rearview mirror so the driver can obtain the necessary information no matter which direction they look. For example, parking assistance information can be displayed on the side mirrors. This allows the driver to obtain information in all directions.

[0035] The information display unit can also synchronously display information on the dashboard and head-up display inside the vehicle. For example, the information display unit uses generation AI to build a system that synchronously displays information displayed on the car windows on the dashboard and head-up display. For example, navigation information is displayed on multiple displays simultaneously. Also, by synchronously displaying information on multiple displays inside the vehicle, the driver can obtain the necessary information regardless of which display they look at. For example, speed limit information is displayed on the dashboard and head-up display. Furthermore, a system is built in which generation AI integrates and manages the displays inside the vehicle and displays information on the optimal display. For example, important warning information is displayed preferentially on the head-up display. This allows the driver to obtain the necessary information regardless of which display they look at.

[0036] The analysis unit can analyze the driver's voice commands and display the necessary information on the information display unit based on the voice input. The analysis unit, for example, uses generation AI to analyze the driver's voice commands in real time and display necessary information such as navigation information and speed limits on the window based on the voice input. For example, information is displayed in response to a voice command such as "Turn right at the next intersection." A system can also be built that analyzes voice commands and instantly displays the information the driver needs. For example, in response to a voice command such as "Show nearby gas stations," the location of gas stations is displayed on the window. Furthermore, the generation AI analyzes the driver's voice commands and displays information at the appropriate time. For example, in response to a voice command such as "Turn left at the next traffic light," instructions to turn left before the traffic light are displayed on the window. This allows the driver to obtain the information they need through voice commands.

[0037] The analysis unit can analyze the driver's biometric data and display information according to their physical condition. For example, the analysis unit uses generation AI to analyze the driver's biometric data, such as heart rate and skin temperature, in real time, and build a system that displays information according to their physical condition. For example, if the heart rate is high, information with a relaxing effect is displayed. Information is also displayed based on the biometric data, tailored to the driver's physical condition. For example, if the skin temperature is high, information is displayed in colors that give a feeling of coolness. Furthermore, a system will be developed in which the generation AI analyzes the driver's biometric data and displays information according to their physical condition. For example, if the heart rate is low, warning information to draw attention is displayed. In this way, driving safety can be improved by displaying information according to the driver's physical condition.

[0038] The analysis unit can analyze weather and traffic conditions in real time and display corresponding information on the information display unit. The analysis unit, for example, uses generative AI to analyze weather data in real time and build a system that displays necessary information for the driver on the window. For example, a warning about slippery roads is displayed when it rains. It also analyzes traffic conditions in real time and displays appropriate information to the driver. For example, traffic congestion information is displayed on the window, allowing the driver to select a detour route. Furthermore, a system will be developed that displays information according to weather and traffic conditions in real time. For example, if there is fog, a warning about poor visibility is displayed on the window. This allows the driver to obtain information according to the weather and traffic conditions.

[0039] The analysis unit can integrate data from other vehicles and infrastructure and display coordinated information on the information display unit. The analysis unit, for example, uses generative AI to integrate data from other vehicles in real time and build a system that displays coordinated information. For example, if a vehicle in front brakes suddenly, that information can be displayed on the window. It also integrates data from infrastructure and displays information necessary for the driver. For example, traffic light status and road construction information can be displayed on the window to enable the driver to make appropriate decisions. Furthermore, a system will be developed that integrates data from other vehicles and infrastructure in real time and displays coordinated information. For example, traffic congestion information and accident information can be displayed on the window to enable the driver to respond quickly. This allows the driver to obtain information from other vehicles and infrastructure.

[0040] The analysis unit can learn driving styles and display warnings optimized for each individual driver in the warning unit. The analysis unit, for example, uses generation AI to learn a driver's driving style and build a system that displays warnings optimized for each individual driver. For example, it can display early warnings to drivers who frequently brake suddenly. It can also display warnings that the driver can respond to most effectively based on their driving style. For example, if a driver often uses expressways, it can highlight speeding warnings. Furthermore, a system will be developed in which generation AI learns a driver's driving patterns and displays warnings tailored to each individual driver. For example, it can warn of specific dangerous points in advance on routes that the driver frequently travels. This allows the driver to receive optimized warnings.

[0041] The analysis unit can integrate vehicle sensor data, detect multiple dangerous elements simultaneously, and display a warning in the warning unit. The analysis unit, for example, uses generation AI to integrate vehicle sensor data in real time and build a system that simultaneously detects multiple dangerous elements and displays a warning. For example, a warning is displayed if the distance to the vehicle ahead is too close or if the vehicle is about to deviate from its lane. In addition, based on sensor data, the system can simultaneously warn the driver of multiple dangerous elements. For example, if both a sharp curve and excessive speed are detected, a warning urging the driver to slow down is displayed. Furthermore, a system will be developed in which generation AI analyzes vehicle sensor data and simultaneously detects multiple dangerous elements and displays a warning. For example, it can simultaneously detect an obstacle ahead and an approaching vehicle behind, and display an appropriate warning to the driver. This allows the driver to simultaneously detect multiple dangerous elements and receive appropriate warnings.

[0042] The analysis unit can integrate data from other vehicles and infrastructure and display coordinated warnings in the warning unit. The analysis unit, for example, uses generative AI to integrate data from other vehicles in real time and build a system that displays coordinated warnings. For example, if a vehicle in front suddenly brakes, the system will warn the driver of that information. It will also integrate data from infrastructure and display coordinated warnings to the driver. For example, it will display appropriate warnings to the driver based on traffic light status and road construction information. Furthermore, we will develop a system that integrates data from other vehicles and infrastructure in real time and displays coordinated warnings. For example, it will display appropriate warnings to the driver based on traffic congestion information and accident information. This allows the driver to receive coordinated warnings based on information from other vehicles and infrastructure.

[0043] The analysis unit can analyze the driving history, predict specific dangerous patterns, and display a warning in the warning unit. The analysis unit, for example, uses generation AI to analyze the driver's past driving history and build a system that predicts specific dangerous patterns and displays warnings. For example, if the driver often exceeds the speed limit, a speeding warning will be displayed in advance. Furthermore, based on the past driving history, it predicts dangerous patterns that the driver is likely to encounter and displays appropriate warnings. For example, it warns the driver in advance of dangerous points on routes the driver frequently travels. Furthermore, a system will be developed in which the generation AI learns the driver's driving patterns and predicts specific dangerous patterns and displays warnings. For example, if the driver often brakes suddenly, a sudden braking warning will be displayed in advance. This allows the driver to predict specific dangerous patterns based on their past driving history and receive appropriate warnings.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The analysis unit can learn the driver's driving style and display information optimized for each individual driver. For example, the generation AI can learn the driver's past driving history and display optimized information based on the driver's frequently used routes and preferred information display format. For example, speed limit information can be highlighted on specific routes. In addition, a system can be built that predicts the information the driver will need based on past driving history and displays it at the appropriate time. For example, traffic light information can be displayed in advance at intersections that the driver frequently passes through. Furthermore, the generation AI can learn the driver's driving patterns and display information tailored to each individual driver. For example, if a driver frequently uses expressways, traffic congestion information will be displayed preferentially. This allows the driver to obtain optimized information based on their past driving history.

[0046] The analysis unit can analyze the driver's voice commands and display the necessary information on the information display unit based on the voice input. For example, a generation AI can be used to analyze the driver's voice commands in real time and display necessary information such as navigation information and speed limits on the window based on the voice input. For example, information can be displayed in response to a voice command such as "Turn right at the next intersection." A system can also be built that analyzes voice commands and instantly displays the information the driver needs. For example, in response to a voice command such as "Show nearby gas stations," the location of gas stations can be displayed on the window. Furthermore, the generation AI can analyze the driver's voice commands and display information at the appropriate time. For example, in response to a voice command such as "Turn left at the next traffic light," instructions to turn left before the traffic light can be displayed on the window. This allows the driver to obtain the information they need through voice commands.

[0047] The analysis unit can analyze the driver's biometric data and display information according to their physical condition. For example, we will use generation AI to analyze the driver's biometric data, such as heart rate and skin temperature, in real time and build a system that displays information according to their physical condition. For example, if the heart rate is high, information with a relaxing effect will be displayed. We will also display information based on the biometric data in accordance with the driver's physical condition. For example, if the skin temperature is high, information will be displayed in colors that give a feeling of coolness. We will also develop a system in which generation AI analyzes the driver's biometric data and displays information according to their physical condition. For example, if the heart rate is low, warning information to draw attention will be displayed. This will improve driving safety by displaying information according to the driver's physical condition.

[0048] The analysis unit can analyze weather and traffic conditions in real time and display corresponding information on the information display unit. For example, we will build a system that uses generative AI to analyze weather data in real time and display necessary information for the driver on the window. For example, a warning about slippery roads will be displayed when it rains. We will also analyze traffic conditions in real time and display appropriate information to the driver. For example, traffic congestion information will be displayed on the window, allowing the driver to select a detour route. We will also develop a system that displays information according to weather and traffic conditions in real time. For example, if there is fog, a warning about poor visibility will be displayed on the window. This will allow the driver to obtain information according to the weather and traffic conditions.

[0049] The analysis unit can integrate data from other vehicles and infrastructure and display coordinated information in the information display unit. For example, we will build a system that uses generative AI to integrate data from other vehicles in real time and display coordinated information. For example, if a vehicle in front brakes suddenly, that information will be displayed on the window. We will also integrate data from infrastructure and display the information necessary for the driver. For example, we will display the status of traffic lights and road construction information on the window to enable the driver to make appropriate decisions. We will also develop a system that integrates data from other vehicles and infrastructure in real time and displays coordinated information. For example, we will display traffic congestion information and accident information on the window to enable the driver to respond quickly. This will allow the driver to obtain information from other vehicles and infrastructure.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information display unit displays information on the car window. For example, it can display information such as navigation information, speed limits, and traffic signs. The information display unit also displays information in a visually easy-to-understand format so that the driver can obtain the necessary information without taking their eyes off the road. Step 2: The analysis unit uses the generation AI to generate information to be displayed on the information display unit. For example, the generation AI analyzes the information the driver needs in real time and displays it on the window at the appropriate time. The generation AI can also monitor the driver's driving status in real time and generate information to display a warning if it detects a dangerous situation. Step 3: The warning unit displays a warning based on the information generated by the analysis unit. For example, it displays a warning if the distance to the vehicle ahead is too close or if the vehicle is about to deviate from its lane. This allows the driving assistance system to provide the necessary information without the driver having to take their eyes off the road, reducing the risk of accidents.

[0052] (Example 2) A driving assistance system according to an embodiment of the present invention is a system that makes information displayed on a vehicle window visually easy to understand, allowing the driver to instantly grasp the necessary information. This allows the driver to obtain the necessary information without taking their eyes off the road, thereby reducing the risk of accidents.

[0053] A driving assistance system according to an embodiment includes an information display unit, an analysis unit, and a warning unit. The information display unit displays information on a vehicle window. For example, it can display information such as navigation information, speed limits, and traffic signs. The information display unit also displays information in a visually easy-to-understand format so that the driver can obtain necessary information without taking their eyes off the road. The analysis unit generates information to be displayed on the information display unit using a generation AI. For example, the generation AI analyzes information required by the driver in real time and displays it on the window at an appropriate time. The generation AI can also monitor the driver's driving status in real time and generate information to display a warning when a dangerous situation is detected. The warning unit displays a warning based on the information generated by the analysis unit. For example, it displays a warning when the vehicle is getting too close to a vehicle ahead or when the vehicle is about to deviate from its lane. This allows the driving assistance system to obtain necessary information without taking the driver's eyes off the road, thereby reducing the risk of an accident.

[0054] The information display unit can display navigation information, speed limits, and traffic sign information. For example, the information display unit displays navigation information to allow the driver to confirm the route to the destination. It also displays speed limit information to allow the driver to understand the current road speed limit. It also displays traffic sign information to allow the driver to understand the road conditions. This allows the driver to obtain the necessary information without taking their eyes off the road.

[0055] The warning unit can display a warning when the distance to the vehicle ahead is too close or when the vehicle is about to deviate from its lane. For example, the warning unit displays a warning when the distance to the vehicle ahead is too close, urging the driver to slow down. Also, the warning unit displays a warning when the vehicle is about to deviate from its lane, urging the driver to stay in the lane. This allows the driver to detect dangerous situations in advance and reduce the risk of an accident.

[0056] The analysis unit analyzes the eye-tracking data and can dynamically change the display position of the information display unit according to eye movements. For example, the analysis unit uses generative AI to analyze the driver's eye-tracking data in real time and dynamically change the display position of navigation information, speed limits, etc. according to eye movements. For example, if the driver is looking to the left, information is displayed in the left window. Furthermore, based on the eye-tracking data, a system is built to display information in the most visible position for the driver. For example, if the driver is looking ahead, information is displayed in the front window so that the driver can obtain the information without taking their eyes off the road. Furthermore, the eye-tracking data is analyzed and the display position is adjusted so that the driver does not miss the information. For example, when the driver moves their eyes, information is automatically displayed in the direction of their line of sight. This allows the driver to obtain the necessary information without moving their eyes.

[0057] The analysis unit can learn driving history and display information optimized for each individual driver. For example, the analysis unit uses generation AI to learn the driver's past driving history and display optimized information based on the driver's frequently used routes and preferred information display format. For example, speed limit information can be highlighted on specific routes. In addition, a system can be built that predicts the information the driver will need based on past driving history and displays it at the appropriate time. For example, traffic light information can be displayed in advance at intersections that the driver frequently passes through. Furthermore, the generation AI can learn the driver's driving patterns and display information tailored to each individual driver. For example, if a driver frequently uses expressways, traffic congestion information will be displayed preferentially. This allows the driver to obtain information optimized based on their past driving history.

[0058] The analysis unit can use the emotion estimation function to detect the driver's stress level and display information on the information display unit using colors and designs that have a relaxing effect. The analysis unit, for example, can use the emotion estimation function to detect the driver's stress level in real time and display navigation information using colors and designs that have a relaxing effect. For example, if the driver is highly stressed, the information is displayed in calming colors such as blue and green. In addition, a system is constructed that dynamically changes the design of the information display according to the driver's emotional state. For example, if the driver is relaxed, the information is displayed using bright colors and animations. Furthermore, information is displayed to reduce the driver's stress based on the emotion estimation data. For example, if the driver is highly stressed, the information is displayed in a simple, easy-to-read design to reduce the driver's burden. This reduces the driver's stress and allows them to drive in a relaxed state.

[0059] The information display unit can display information not only on the car windows, but also on the side mirrors and rearview mirror. For example, the information display unit uses generative AI to build a system that displays information such as navigation information and speed limits not only on the car windows, but also on the side mirrors and rearview mirror. For example, lane change instructions can be displayed on the side mirrors. In addition, by displaying information on the side mirrors and rearview mirrors, the driver can obtain information in all directions. For example, rear traffic conditions can be displayed on the rearview mirror so the driver can check without taking their eyes off the road. Furthermore, information can be displayed synchronously on the car windows, side mirrors, and rearview mirror so the driver can obtain the necessary information no matter which direction they look. For example, parking assistance information can be displayed on the side mirrors. This allows the driver to obtain information in all directions.

[0060] The information display unit can also synchronously display information on the dashboard and head-up display inside the vehicle. For example, the information display unit uses generation AI to build a system that synchronously displays information displayed on the car windows on the dashboard and head-up display. For example, navigation information is displayed on multiple displays simultaneously. Also, by synchronously displaying information on multiple displays inside the vehicle, the driver can obtain the necessary information regardless of which display they look at. For example, speed limit information is displayed on the dashboard and head-up display. Furthermore, a system is built in which generation AI integrates and manages the displays inside the vehicle and displays information on the optimal display. For example, important warning information is displayed preferentially on the head-up display. This allows the driver to obtain the necessary information regardless of which display they look at.

[0061] The analysis unit can use the emotion estimation function to customize information display according to the driver's emotional state. The analysis unit, for example, builds a system that uses the emotion estimation function to customize information display according to the driver's emotional state. For example, if the driver is relaxed, information is displayed using bright colors and animations. The emotional state of the driver is also analyzed in real time, and information is displayed to elicit positive emotions. For example, if the driver is feeling stressed, information is displayed in calming colors. Furthermore, a system is developed that displays information according to the driver's emotional state based on emotion estimation data. For example, if the driver is excited, information is displayed in a simple, easy-to-read design. This makes it possible to customize information display according to the driver's emotional state and elicit positive emotions.

[0062] The analysis unit can analyze the driver's voice commands and display the necessary information on the information display unit based on the voice input. The analysis unit, for example, uses generation AI to analyze the driver's voice commands in real time and display necessary information such as navigation information and speed limits on the window based on the voice input. For example, information is displayed in response to a voice command such as "Turn right at the next intersection." A system can also be built that analyzes voice commands and instantly displays the information the driver needs. For example, in response to a voice command such as "Show nearby gas stations," the location of gas stations is displayed on the window. Furthermore, the generation AI analyzes the driver's voice commands and displays information at the appropriate time. For example, in response to a voice command such as "Turn left at the next traffic light," instructions to turn left before the traffic light are displayed on the window. This allows the driver to obtain the information they need through voice commands.

[0063] The analysis unit can analyze the driver's biometric data and display information according to their physical condition. For example, the analysis unit uses generation AI to analyze the driver's biometric data, such as heart rate and skin temperature, in real time, and build a system that displays information according to their physical condition. For example, if the heart rate is high, information with a relaxing effect is displayed. Information is also displayed based on the biometric data, tailored to the driver's physical condition. For example, if the skin temperature is high, information is displayed in colors that give a feeling of coolness. Furthermore, a system will be developed in which the generation AI analyzes the driver's biometric data and displays information according to their physical condition. For example, if the heart rate is low, warning information to draw attention is displayed. In this way, driving safety can be improved by displaying information according to the driver's physical condition.

[0064] The analysis unit can dynamically change the priority of information according to the emotional state of the driver using the emotion estimation function. The analysis unit, for example, builds a system that dynamically changes the priority of information according to the emotional state of the driver using the emotion estimation function. For example, if the driver is feeling stressed, information that has a relaxing effect is displayed preferentially. The analysis unit also analyzes the driver's emotional state in real time and dynamically changes the priority of information. For example, if the driver is relaxed, entertainment information is displayed preferentially. Furthermore, a system is developed that dynamically changes the priority of information according to the emotional state of the driver based on emotion estimation data. For example, if the driver is excited, important warning information is displayed preferentially. This makes it possible to dynamically change the priority of information according to the emotional state of the driver and provide appropriate information.

[0065] The analysis unit can analyze weather and traffic conditions in real time and display corresponding information on the information display unit. The analysis unit, for example, uses generative AI to analyze weather data in real time and build a system that displays necessary information for the driver on the window. For example, a warning about slippery roads is displayed when it rains. It also analyzes traffic conditions in real time and displays appropriate information to the driver. For example, traffic congestion information is displayed on the window, allowing the driver to select a detour route. Furthermore, a system will be developed that displays information according to weather and traffic conditions in real time. For example, if there is fog, a warning about poor visibility is displayed on the window. This allows the driver to obtain information according to the weather and traffic conditions.

[0066] The analysis unit can integrate data from other vehicles and infrastructure and display coordinated information on the information display unit. The analysis unit, for example, uses generative AI to integrate data from other vehicles in real time and build a system that displays coordinated information. For example, if a vehicle in front brakes suddenly, that information can be displayed on the window. It also integrates data from infrastructure and displays information necessary for the driver. For example, traffic light status and road construction information can be displayed on the window to enable the driver to make appropriate decisions. Furthermore, a system will be developed that integrates data from other vehicles and infrastructure in real time and displays coordinated information. For example, traffic congestion information and accident information can be displayed on the window to enable the driver to respond quickly. This allows the driver to obtain information from other vehicles and infrastructure.

[0067] The analysis unit uses the emotion estimation function to display entertainment information on the information display unit according to the driver's emotional state, thereby promoting relaxation while driving. The analysis unit, for example, uses the emotion estimation function to analyze the driver's emotional state in real time and builds a system that displays entertainment information with a relaxing effect. For example, if the driver is feeling stressed, relaxing music or videos are displayed. The entertainment information is also dynamically changed according to the driver's emotional state. For example, if the driver is relaxed, enjoyable music or interesting information is displayed. Furthermore, a system is developed that provides entertainment information tailored to the driver's emotional state based on the emotion estimation data. For example, if the driver is excited, calming music or videos with a relaxing effect are displayed. This allows the driver to drive in a relaxed state.

[0068] The analysis unit can learn driving styles and display warnings optimized for each individual driver in the warning unit. The analysis unit, for example, uses generation AI to learn a driver's driving style and build a system that displays warnings optimized for each individual driver. For example, it can display early warnings to drivers who frequently brake suddenly. It can also display warnings that the driver can respond to most effectively based on their driving style. For example, if a driver often uses expressways, it can highlight speeding warnings. Furthermore, a system will be developed in which generation AI learns a driver's driving patterns and displays warnings tailored to each individual driver. For example, it can warn of specific dangerous points in advance on routes that the driver frequently travels. This allows the driver to receive optimized warnings.

[0069] The analysis unit can integrate vehicle sensor data, detect multiple dangerous elements simultaneously, and display a warning in the warning unit. The analysis unit, for example, uses generation AI to integrate vehicle sensor data in real time and build a system that simultaneously detects multiple dangerous elements and displays a warning. For example, a warning is displayed if the distance to the vehicle ahead is too close or if the vehicle is about to deviate from its lane. In addition, based on sensor data, the system can simultaneously warn the driver of multiple dangerous elements. For example, if both a sharp curve and excessive speed are detected, a warning urging the driver to slow down is displayed. Furthermore, a system will be developed in which generation AI analyzes vehicle sensor data and simultaneously detects multiple dangerous elements and displays a warning. For example, it can simultaneously detect an obstacle ahead and an approaching vehicle behind, and display an appropriate warning to the driver. This allows the driver to simultaneously detect multiple dangerous elements and receive appropriate warnings.

[0070] The analysis unit can use the emotion estimation function to detect the driver's state of tension and have the warning unit sound or display a warning that has a relaxing effect. The analysis unit, for example, uses the emotion estimation function to detect the driver's state of tension in real time and builds a system that sounds or displays a warning that has a relaxing effect. For example, if the driver is tense, a warning is displayed along with calm music. The warning sound or display is also dynamically changed according to the driver's state of tension. For example, if the driver is relaxed, a normal warning sound is used, and if the driver is tense, calm music is used. Furthermore, a system is developed that sounds or displays a warning that matches the driver's state of tension based on the emotion estimation data. For example, if the driver is excited, a warning is displayed along with calm music. This allows the driver to receive the warning in a relaxed state.

[0071] The analysis unit can integrate data from other vehicles and infrastructure and display coordinated warnings in the warning unit. The analysis unit, for example, uses generative AI to integrate data from other vehicles in real time and build a system that displays coordinated warnings. For example, if a vehicle in front suddenly brakes, the system will warn the driver of that information. It will also integrate data from infrastructure and display coordinated warnings to the driver. For example, it will display appropriate warnings to the driver based on traffic light status and road construction information. Furthermore, we will develop a system that integrates data from other vehicles and infrastructure in real time and displays coordinated warnings. For example, it will display appropriate warnings to the driver based on traffic congestion information and accident information. This allows the driver to receive coordinated warnings based on information from other vehicles and infrastructure.

[0072] The analysis unit can analyze the driving history, predict specific dangerous patterns, and display a warning in the warning unit. The analysis unit, for example, uses generation AI to analyze the driver's past driving history and build a system that predicts specific dangerous patterns and displays warnings. For example, if the driver often exceeds the speed limit, a speeding warning will be displayed in advance. Furthermore, based on the past driving history, it predicts dangerous patterns that the driver is likely to encounter and displays appropriate warnings. For example, it warns the driver in advance of dangerous points on routes the driver frequently travels. Furthermore, a system will be developed in which the generation AI learns the driver's driving patterns and predicts specific dangerous patterns and displays warnings. For example, if the driver often brakes suddenly, a sudden braking warning will be displayed in advance. This allows the driver to predict specific dangerous patterns based on their past driving history and receive appropriate warnings.

[0073] The analysis unit can dynamically change the intensity and format of the warning according to the emotional state of the driver using the emotion estimation function. The analysis unit, for example, builds a system that dynamically changes the intensity and format of the warning according to the emotional state of the driver using the emotion estimation function. For example, if the driver is relaxed, a normal warning sound is used, and if the driver is tense, calm music is used. The analysis unit also analyzes the driver's emotional state in real time and dynamically changes the intensity and format of the warning. For example, if the driver is feeling stressed, a warning is displayed along with calm music. Furthermore, a system is developed that dynamically changes the intensity and format of the warning according to the driver's emotional state based on the emotion estimation data. For example, if the driver is excited, a warning is displayed along with calm music. This makes it possible to dynamically change the intensity and format of the warning according to the driver's emotional state and provide appropriate warnings.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The analysis unit can learn the driver's driving style and display information optimized for each individual driver. For example, the generation AI can learn the driver's past driving history and display optimized information based on the driver's frequently used routes and preferred information display format. For example, speed limit information can be highlighted on specific routes. In addition, a system can be built that predicts the information the driver will need based on past driving history and displays it at the appropriate time. For example, traffic light information can be displayed in advance at intersections that the driver frequently passes through. Furthermore, the generation AI can learn the driver's driving patterns and display information tailored to each individual driver. For example, if a driver frequently uses expressways, traffic congestion information will be displayed preferentially. This allows the driver to obtain optimized information based on their past driving history.

[0076] The analysis unit can analyze the driver's voice commands and display the necessary information on the information display unit based on the voice input. For example, a generation AI can be used to analyze the driver's voice commands in real time and display necessary information such as navigation information and speed limits on the window based on the voice input. For example, information can be displayed in response to a voice command such as "Turn right at the next intersection." A system can also be built that analyzes voice commands and instantly displays the information the driver needs. For example, in response to a voice command such as "Show nearby gas stations," the location of gas stations can be displayed on the window. Furthermore, the generation AI can analyze the driver's voice commands and display information at the appropriate time. For example, in response to a voice command such as "Turn left at the next traffic light," instructions to turn left before the traffic light can be displayed on the window. This allows the driver to obtain the information they need through voice commands.

[0077] The analysis unit can analyze the driver's biometric data and display information according to their physical condition. For example, we will use generation AI to analyze the driver's biometric data, such as heart rate and skin temperature, in real time and build a system that displays information according to their physical condition. For example, if the heart rate is high, information with a relaxing effect will be displayed. We will also display information based on the biometric data in accordance with the driver's physical condition. For example, if the skin temperature is high, information will be displayed in colors that give a feeling of coolness. We will also develop a system in which generation AI analyzes the driver's biometric data and displays information according to their physical condition. For example, if the heart rate is low, warning information to draw attention will be displayed. This will improve driving safety by displaying information according to the driver's physical condition.

[0078] The analysis unit can analyze weather and traffic conditions in real time and display corresponding information on the information display unit. For example, we will build a system that uses generative AI to analyze weather data in real time and display necessary information for the driver on the window. For example, a warning about slippery roads will be displayed when it rains. We will also analyze traffic conditions in real time and display appropriate information to the driver. For example, traffic congestion information will be displayed on the window, allowing the driver to select a detour route. We will also develop a system that displays information according to weather and traffic conditions in real time. For example, if there is fog, a warning about poor visibility will be displayed on the window. This will allow the driver to obtain information according to the weather and traffic conditions.

[0079] The analysis unit can integrate data from other vehicles and infrastructure and display coordinated information in the information display unit. For example, we will build a system that uses generative AI to integrate data from other vehicles in real time and display coordinated information. For example, if a vehicle in front brakes suddenly, that information will be displayed on the window. We will also integrate data from infrastructure and display the information necessary for the driver. For example, we will display the status of traffic lights and road construction information on the window to enable the driver to make appropriate decisions. We will also develop a system that integrates data from other vehicles and infrastructure in real time and displays coordinated information. For example, we will display traffic congestion information and accident information on the window to enable the driver to respond quickly. This will allow the driver to obtain information from other vehicles and infrastructure.

[0080] The analysis unit can use the emotion estimation function to detect the driver's stress level and display information on the information display unit using colors and designs that have a relaxing effect. For example, the emotion estimation function can be used to detect the driver's stress level in real time and display navigation information using colors and designs that have a relaxing effect. For example, if the driver's stress level is high, information is displayed in calming colors such as blue and green. In addition, a system can be built that dynamically changes the design of the information display depending on the driver's emotional state. For example, if the driver is relaxed, information is displayed using bright colors and animations. Furthermore, information is displayed to reduce the driver's stress based on the emotion estimation data. For example, if stress is high, information is displayed in a simple, easy-to-read design to reduce the driver's burden. This reduces the driver's stress and allows them to drive in a relaxed state.

[0081] The analysis unit can use the emotion estimation function to customize information display according to the driver's emotional state. For example, we will build a system that uses the emotion estimation function to customize information display according to the driver's emotional state. For example, if the driver is relaxed, information will be displayed using bright colors and animations. We will also analyze the driver's emotional state in real time and display information to elicit positive emotions. For example, if the driver is feeling stressed, information will be displayed in calming colors. We will also develop a system that displays information according to the driver's emotional state based on emotion estimation data. For example, if the driver is excited, information will be displayed in a simple, easy-to-read design. This will allow us to customize information display according to the driver's emotional state and elicit positive emotions.

[0082] The analysis unit can dynamically change the priority of information according to the emotional state of the driver using the emotion estimation function. For example, a system is constructed that dynamically changes the priority of information according to the emotional state of the driver using the emotion estimation function. For example, if the driver is feeling stressed, information that has a relaxing effect is displayed preferentially. The driver's emotional state is also analyzed in real time and the priority of information is dynamically changed. For example, if the driver is relaxed, entertainment information is displayed preferentially. Furthermore, a system is developed that dynamically changes the priority of information according to the driver's emotional state based on emotion estimation data. For example, if the driver is excited, important warning information is displayed preferentially. This makes it possible to dynamically change the priority of information according to the driver's emotional state and provide appropriate information.

[0083] The analysis unit uses the emotion estimation function to display entertainment information on the information display unit according to the driver's emotional state, thereby promoting relaxation while driving. For example, we will build a system that uses the emotion estimation function to analyze the driver's emotional state in real time and display entertainment information that has a relaxing effect. For example, if the driver is feeling stressed, relaxing music and videos will be displayed. In addition, the entertainment information will be dynamically changed according to the driver's emotional state. For example, if the driver is relaxed, enjoyable music and interesting information will be displayed. Furthermore, we will develop a system that provides entertainment information tailored to the driver's emotional state based on the emotion estimation data. For example, if the driver is excited, calming music and videos with a relaxing effect will be displayed. This will allow the driver to drive in a relaxed state.

[0084] The analysis unit uses the emotion estimation function to detect the driver's state of tension and can have the warning unit sound or display a warning that has a relaxing effect. For example, we will build a system that uses the emotion estimation function to detect the driver's state of tension in real time and sound or display a warning that has a relaxing effect. For example, if the driver is tense, a warning will be displayed along with calm music. In addition, the warning sound or display will be dynamically changed according to the driver's state of tension. For example, if the driver is relaxed, a normal warning sound will be used, and if the driver is tense, calm music will be used. Furthermore, we will develop a system that sounds or displays a warning that matches the driver's state of tension based on the emotion estimation data. For example, if the driver is excited, a warning will be displayed along with calm music. This allows the driver to receive the warning in a relaxed state.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The information display unit displays information on the car window. For example, it can display information such as navigation information, speed limits, and traffic signs. The information display unit also displays information in a visually easy-to-understand format so that the driver can obtain the necessary information without taking their eyes off the road. Step 2: The analysis unit uses the generation AI to generate information to be displayed on the information display unit. For example, the generation AI analyzes the information the driver needs in real time and displays it on the window at the appropriate time. The generation AI can also monitor the driver's driving status in real time and generate information to display a warning if it detects a dangerous situation. Step 3: The warning unit displays a warning based on the information generated by the analysis unit. For example, it displays a warning if the distance to the vehicle ahead is too close or if the vehicle is about to deviate from its lane. This allows the driving assistance system to provide the necessary information without the driver having to take their eyes off the road, reducing the risk of accidents.

[0087] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0093] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0094] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0097] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0098] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0100] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0101] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0128] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0137] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0138] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0139] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0140] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0141] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0142] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0143] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0144] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0145] 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.

[0146] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0147] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0148] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0149] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0150] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0151] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0152] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information display unit that displays information on a car window; an analysis unit using a generation AI to generate information to be displayed on the information display unit; a warning unit that displays a warning based on the information generated by the analysis unit. A system characterized by:

2. The information display unit Display navigation information, speed limits and traffic sign information 2. The system of claim 1.

3. The warning unit Warns you if you get too close to the vehicle ahead or if you are about to leave your lane 2. The system of claim 1.

4. The analysis unit Analyzes eye-tracking data and dynamically changes the display position of the information display according to eye movements.

2. The system of claim 1.

5. The analysis unit Learns driving history and displays information optimized for each individual driver 2. The system of claim 1.

6. The analysis unit Detects the driver's stress level and displays information on the information display using colors and designs that have a relaxing effect.

2. The system of claim 1.

Images