System

Abstract

An object of the system according to the embodiment is to optimize the timing of a signal in accordance with the situation of a vehicle or a pedestrian.SOLUTION: A system according to an embodiment includes an imaging unit, an analysis unit, and an adjustment unit. In the imaging unit, a camera installed at each intersection or crosswalk images a situation of a vehicle or a pedestrian. The analysis unit analyzes the image captured by the imaging unit and recognizes the number, positions, and movements of vehicles and pedestrians. The adjustment unit adjusts the timing of the signal based on the information recognized 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] With conventional technology, traffic light timing was not optimized according to the actual situation of vehicles and pedestrians, resulting in unnecessary waiting times at traffic lights and increased energy loads.

[0005] The system according to the embodiment aims to optimize the timing of signals according to the situation of vehicles and pedestrians. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera unit, an analyzer, and an adjuster. The camera unit captures images of vehicles and pedestrians using cameras installed at intersections and crosswalks. The analyzer analyzes the images captured by the camera unit and recognizes the number, positions, and movements of vehicles and pedestrians. The adjuster adjusts the timing of traffic lights based on the information recognized by the analyzer. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the timing of signals depending on the situation of vehicles and pedestrians. [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 traffic light system according to an embodiment of the present invention replaces all traffic lights at intersections, crosswalks, and other locations nationwide with traffic lights capable of assessing traffic conditions through image recognition. The traffic light system reduces unnecessary wait times and optimizes traffic. For example, cameras installed at each intersection and crosswalk capture images of vehicles and pedestrians in real time. The captured images are then analyzed using AI to identify the number, location, and movement of vehicles and pedestrians. For example, if there are no vehicles or pedestrians at an intersection, the traffic light does not turn red, thereby reducing unnecessary wait times. Furthermore, the timing of traffic lights can be adjusted according to traffic congestion. For example, the traffic light on the congested side can be extended to alleviate congestion. Furthermore, if a single-lane intersection is blocked by a traffic jam of right-turning vehicles, the traffic light can be adjusted to accommodate the right-turning vehicle and prevent following vehicles from getting stuck. This reduces unnecessary wait times and optimizes traffic, resulting in smoother traffic flow. The traffic light system thus reduces unnecessary wait times and optimizes traffic. For example, when there are no vehicles on the road, the traffic light will not change to red, allowing vehicles to pass through smoothly. Also, by extending the traffic light on the congested side, congestion will be alleviated and traffic will flow more smoothly. Furthermore, when there are vehicles turning right, the traffic light will be adjusted to accommodate the vehicles turning right, allowing following vehicles to pass through smoothly. In this way, the traffic light system can reduce unnecessary waiting times and optimize traffic, resulting in smoother traffic flow.

[0029] A traffic light system according to an embodiment includes a camera unit, an analysis unit, and an adjustment unit. The camera unit captures images of vehicles and pedestrians using cameras installed at intersections and crosswalks. For example, the camera unit can capture a wide range of images by setting the camera's resolution high and using a wide-angle lens. The camera unit can also use an infrared camera or a waterproof camera to capture clear images even at night or in bad weather. The camera unit can also coordinate multiple cameras to optimize camera installation locations and minimize blind spots. The analysis unit uses AI to analyze the images captured by the camera unit and recognize the number, location, and movement of vehicles and pedestrians. For example, the analysis unit uses an image recognition algorithm to detect the outlines of vehicles and pedestrians and identify their locations based on a coordinate system. The analysis unit can also learn movement patterns and detect abnormal movement. The analysis unit can also improve the accuracy of the analysis by referring to past traffic data. The adjustment unit adjusts the timing of traffic signals based on the information recognized by the analysis unit. For example, the adjustment unit aims to alleviate traffic congestion by extending the traffic light on the congested side. In addition, when there is a backlog of right-turning vehicles, the adjustment unit can adjust the traffic light for right-turning vehicles to prevent following vehicles from getting stuck. Furthermore, when there are no vehicles or pedestrians, the adjustment unit can also not turn the traffic light red, thereby reducing unnecessary waiting time at the traffic light. This enables the traffic light system according to the embodiment to reduce unnecessary waiting time at the traffic light and optimize traffic. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can adjust the timing of the traffic light using an AI model that receives information recognized by the analysis unit as input and outputs the timing of the traffic light.

[0030] The adjustment unit can alleviate the congestion by extending the signal on the congested side. A specific method for extending the signal on the congested side is, for example, to set an extension time for the normal signal time. The adjustment unit detects congestion based on, for example, vehicle retention time and vehicle density, and determines the signal extension time. The adjustment unit can also monitor the congestion situation in real time and adjust the signal extension time as needed. Furthermore, the adjustment unit can also cooperate with signals at other intersections to alleviate the congestion. As a result, the congestion can be alleviated by extending the signal on the congested side. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can adjust the timing of the signal using an AI model that inputs the congestion situation and outputs the signal extension time.

[0031] When right-turning vehicles are blocked, the adjustment unit can adjust the traffic light for the right-turning vehicles to prevent following vehicles from getting stuck. Specific circumstances in which right-turning vehicles are blocked include, for example, the number of vehicles in the right-turn lane and the waiting time for a right turn. The adjustment unit, for example, monitors the number of vehicles in the right-turn lane and adjusts the traffic light when a certain number of vehicles or more are blocked. The adjustment unit can also monitor the waiting time for a right turn and adjust the traffic light when the waiting time exceeds a certain period of time. Furthermore, when adjusting the traffic light for right-turning vehicles, the adjustment unit can also adjust the timing of the traffic light so that following vehicles can pass smoothly. This makes it possible to adjust the traffic light for right-turning vehicles when right-turning vehicles are blocked, so that following vehicles do not get stuck. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can adjust the timing of the traffic light using an AI model that inputs the number of vehicles in the right-turn lane and the waiting time for a right turn and outputs the timing of the traffic light.

[0032] The analysis unit can analyze video in real time to recognize the number, location, and movement of vehicles and pedestrians. Specific methods for performing real-time analysis include, for example, minimizing data processing speed and delay time. The analysis unit can recognize the number, location, and movement of vehicles and pedestrians in real time, for example, using a high-speed image recognition algorithm. The analysis unit can also use high-performance hardware to process video data in real time. Furthermore, the analysis unit can instantly adjust traffic light timing based on the real-time analysis results. This allows the analysis unit to analyze video in real time to recognize the number, location, and movement of vehicles and pedestrians. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform real-time analysis using an AI model that inputs video data and outputs the number, location, and movement of vehicles and pedestrians.

[0033] The camera unit can capture images of vehicles and pedestrians using cameras installed at each intersection and crosswalk. For example, the camera unit can set the camera's resolution high and capture a wide area using a wide-angle lens. The camera unit can also use an infrared camera or a waterproof camera to capture clear images even at night or in bad weather. Furthermore, the camera unit can coordinate multiple cameras to optimize the camera installation location and minimize blind spots. This allows the camera unit to accurately capture images of vehicles and pedestrians using cameras installed at each intersection and crosswalk. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without AI. For example, the camera unit can capture images using an AI model that inputs camera image data and outputs the status of vehicles and pedestrians.

[0034] The adjustment unit can reduce unnecessary waiting time by not changing the signal to red when there are no vehicles or pedestrians. A specific method for reducing unnecessary waiting time at a traffic light is, for example, adjusting the timing of a traffic light based on the time of day when there are no vehicles or pedestrians and the waiting time at the traffic light. For example, the adjustment unit can extend the duration of the green signal without changing the signal to red when there are no vehicles or pedestrians. The adjustment unit can also monitor the status of vehicles and pedestrians in real time and adjust the timing of the signal as needed. Furthermore, the adjustment unit can reduce unnecessary waiting time at a traffic light by coordinating with signals at other intersections. This allows the adjustment unit to reduce unnecessary waiting time at a traffic light by not changing the signal to red when there are no vehicles or pedestrians. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can adjust the timing of the signal using an AI model that inputs the status of vehicles and pedestrians and outputs the timing of the signal.

[0035] The imaging unit can automatically adjust the camera's exposure and sensitivity during shooting according to the weather and time of day. For example, when shooting at night, the imaging unit increases the camera's sensitivity to capture clear images even in dark places. The imaging unit can also adjust the exposure during rainy weather to prevent raindrops from affecting the image. Furthermore, the imaging unit can lower the exposure during strong sunlight during the day to prevent the image from being overexposed. This enables the imaging unit to capture optimal images according to the weather and time of day. Some or all of the above-mentioned processing in the imaging unit may be performed using, or without, AI, for example. For example, the imaging unit can input data on the weather and time of day into the generation AI and have the generation AI adjust the exposure and sensitivity.

[0036] The image capturing unit can add a focus function for tracking a specific vehicle or pedestrian when capturing an image. For example, the image capturing unit automatically adjusts the focus of the camera to continue tracking a specific vehicle. The image capturing unit can also focus on a pedestrian when the pedestrian crosses an intersection and capture the image. Furthermore, the image capturing unit can focus on a right-turning vehicle when the vehicle enters an intersection and capture the image. This enables the image capturing unit to capture an image while tracking a specific target. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit can input data of a specific vehicle or pedestrian into the generation AI and have the generation AI adjust the focus.

[0037] The photographing unit can automatically adjust the position of the camera during photographing to minimize blind spots. For example, the photographing unit can move the position of the camera up, down, left, and right to minimize blind spots. The photographing unit can also adjust the photographing range so that there are no blind spots by linking multiple cameras. Furthermore, the photographing unit can adjust the height of the camera to reduce blind spots for vehicles and pedestrians. This enables the photographing unit to photograph with minimal blind spots. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input camera position data to the generation AI and have the generation AI adjust the position.

[0038] The photographing unit can take into account surrounding buildings and obstacles when photographing in order to optimize the camera installation location. The photographing unit, for example, determines the camera installation location by considering the height and position of surrounding buildings. The photographing unit can also install the camera by selecting a location with few obstacles. Furthermore, the photographing unit can periodically review the camera installation location and relocate it to an optimal position. This enables the photographing unit to select the optimal camera installation location. Some or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input data on surrounding buildings and obstacles into the generating AI and cause the generating AI to optimize the installation location.

[0039] The photographing unit can share the camera footage with other traffic management systems in real time when photographing. For example, the photographing unit transmits the camera footage to a traffic management center in real time. The photographing unit can also share the camera footage in cooperation with other traffic management systems. Furthermore, the photographing unit can upload the camera footage to the cloud and share it with multiple systems. This enables the photographing unit to share footage in real time. Some or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the camera footage data to a generation AI and have the generation AI share the data with other traffic management systems.

[0040] The imaging unit can perform preprocessing for analyzing the camera image when capturing an image. The imaging unit can perform preprocessing to remove noise from the image, for example. The imaging unit can also perform preprocessing to adjust the resolution of the image. Furthermore, the imaging unit can also perform preprocessing to correct the color tone of the image. This enables the imaging unit to perform preprocessing for image analysis. Some or all of the above-mentioned processing in the imaging unit can be performed using, for example, AI, or can be performed without using AI. For example, the imaging unit can input the image data to a generation AI and have the generation AI perform preprocessing.

[0041] During analysis, the analysis unit learns patterns of vehicle and pedestrian movement and can detect abnormal movement. For example, the analysis unit detects an abnormality when a vehicle moves differently from its normal movement. The analysis unit can also detect an abnormality when a pedestrian moves differently from its normal movement. Furthermore, the analysis unit can learn patterns of vehicle and pedestrian movement and detect abnormal movement in real time. This enables the analysis unit to detect abnormal movement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on vehicle and pedestrian movement to the generation AI and cause the generation AI to detect abnormal movement.

[0042] The analysis unit can add a function to recognize the vehicle type and license plate during analysis. The analysis unit recognizes, for example, the vehicle type (passenger car, truck, bus, etc.). The analysis unit can also recognize and record the vehicle license plate. Furthermore, the analysis unit can recognize the vehicle type and license plate in real time. This enables the analysis unit to recognize the vehicle type and license plate. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data of the vehicle to the generation AI and have the generation AI recognize the type and license plate.

[0043] The analysis unit can add a function to estimate the age and gender of a pedestrian during analysis. The analysis unit, for example, analyzes the face of a pedestrian to estimate the age. The analysis unit can also analyze the body shape and clothing of a pedestrian to estimate the gender. Furthermore, the analysis unit can estimate the age and gender of a pedestrian in real time. This enables the analysis unit to estimate the age and gender of a pedestrian. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data of a pedestrian to a generation AI and have the generation AI estimate the age and gender.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past traffic data. The analysis unit, for example, analyzes current traffic conditions based on past traffic data. The analysis unit can also detect abnormal traffic conditions by referring to past traffic data. Furthermore, the analysis unit can improve the accuracy of the analysis by utilizing past traffic data. This enables the analysis unit to improve the accuracy of the analysis by utilizing past traffic data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past traffic data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] The analysis unit can integrate data from other sensors during analysis. For example, the analysis unit can integrate data from an acoustic sensor to analyze traffic conditions. The analysis unit can also integrate data from a temperature sensor to analyze traffic conditions. Furthermore, the analysis unit can integrate data from other sensors to improve the accuracy of the analysis. In this way, the analysis unit improves the accuracy of the analysis by integrating data from other sensors. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other sensors into the generation AI and have the generation AI integrate the data.

[0046] During analysis, the analysis unit can share the analysis results with other traffic management systems. For example, the analysis unit transmits the analysis results to a traffic management center in real time. The analysis unit can also cooperate with other traffic management systems to share the analysis results. Furthermore, the analysis unit can upload the analysis results to the cloud and share them with multiple systems. In this way, the analysis unit can share the analysis results with other traffic management systems, thereby improving the efficiency of traffic management. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI and have the generation AI share the results with other traffic management systems.

[0047] During adjustment, the adjustment unit can change the timing of the traffic light in response to a specific event. For example, when an emergency vehicle passes, the adjustment unit changes the traffic light to green to allow it to pass quickly. The adjustment unit can also adjust the timing of the traffic light to match the time when people are arriving and leaving school. Furthermore, the adjustment unit can change the timing of the traffic light in real time when a specific event occurs. This enables the adjustment unit to change the timing of the traffic light in response to a specific event. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input specific event data to the generation AI and cause the generation AI to change the timing of the traffic light.

[0048] During adjustment, the adjustment unit can optimize traffic flow in cooperation with signals at other intersections. The adjustment unit, for example, adjusts the timing of signals in cooperation with neighboring intersections. The adjustment unit can also smooth traffic flow in cooperation with signals at other intersections. Furthermore, the adjustment unit can coordinate signals at multiple intersections to optimize traffic. This enables the adjustment unit to optimize traffic flow by coordinating with signals at other intersections. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input signal data at other intersections to the generation AI and cause the generation AI to adjust the timing of the signals.

[0049] During adjustment, the adjustment unit can improve visibility by changing the color or blinking pattern of the traffic light. For example, the adjustment unit can improve visibility by changing the color of the traffic light. The adjustment unit can also improve visibility by changing the blinking pattern of the traffic light. Furthermore, the adjustment unit can change the color or blinking pattern of the traffic light in real time depending on a specific situation. This allows the adjustment unit to improve the visibility of the traffic light. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input data on the color or blinking pattern of the traffic light into the generation AI and cause the generation AI to improve visibility.

[0050] During adjustment, the adjustment unit can optimize the signal timing by referring to traffic volume prediction data. The adjustment unit adjusts the signal timing based on, for example, traffic volume prediction data. The adjustment unit can also set signal timing corresponding to peak traffic volume by referring to the prediction data. Furthermore, the adjustment unit can also adjust the signal timing in real time according to fluctuations in traffic volume by utilizing the prediction data. This enables the adjustment unit to optimize the signal timing by utilizing the traffic volume prediction data. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input traffic volume prediction data to the generation AI and cause the generation AI to optimize the signal timing.

[0051] During adjustment, the adjustment unit can adjust the signal timing in cooperation with other traffic management systems. The adjustment unit, for example, adjusts the signal timing in cooperation with other traffic management systems. The adjustment unit can also adjust the signal timing in real time in cooperation with a traffic management center. Furthermore, the adjustment unit can optimize the signal timing based on data from other systems. This enables the adjustment unit to adjust the signal timing in cooperation with other traffic management systems. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from other traffic management systems to the generation AI and cause the generation AI to adjust the signal timing.

[0052] The adjustment unit can add a remote control function for changing the signal timing in real time during adjustment. The adjustment unit changes the signal timing in real time using, for example, the remote control function. The adjustment unit can also adjust the signal timing remotely from a traffic management center. Furthermore, the adjustment unit can also quickly change the signal timing using the remote control function in an emergency. This enables the adjustment unit to change the signal timing using the remote control function. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input remote control data to the generation AI and cause the generation AI to change the signal timing.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The traffic light system may further include a prediction unit. The prediction unit can predict future traffic conditions based on past traffic data and weather data. For example, the prediction unit analyzes past traffic data and learns traffic volume fluctuation patterns during specific time periods and days of the week. The prediction unit can also refer to weather data to predict traffic volume fluctuations on rainy or snowy days. Furthermore, the prediction unit can predict increases in traffic volume based on specific events (e.g., sporting events or concerts). This allows the traffic light system to predict future traffic conditions using the prediction unit and adjust signal timing in advance.

[0055] The traffic light system may further include a notification unit. The notification unit can provide real-time notifications to drivers and pedestrians according to traffic conditions. For example, if there is congestion, the notification unit may suggest a detour route to the driver. The notification unit may also notify pedestrians when the traffic light will change before they cross the street. Furthermore, the notification unit may also notify drivers to pay attention if an emergency vehicle is approaching. This allows the traffic light system to provide appropriate information according to traffic conditions via the notification unit, thereby ensuring smooth traffic flow.

[0056] The signal system can further include an energy management unit. The energy management unit can optimize the energy consumption of the traffic lights. For example, the energy management unit can use solar panels to supply power to the traffic lights. The energy management unit can also adjust the brightness of the traffic light's LED lights to reduce energy consumption. Furthermore, the energy management unit can reduce the operation of the traffic lights at night or during times of low traffic volume to save energy. In this way, the signal system can optimize energy consumption using the energy management unit and operate in an environmentally friendly manner.

[0057] The traffic light system can further include an emergency response unit. The emergency response unit can respond quickly when an emergency occurs. For example, in the event of a traffic accident, the emergency response unit can adjust the timing of traffic lights to allow emergency vehicles to arrive at the scene quickly. In the event of a natural disaster, the emergency response unit can also adjust the timing of traffic lights to secure evacuation routes. Furthermore, in the event of a terrorist attack or crime, the emergency response unit can quickly notify the police and fire department and adjust the timing of traffic lights to respond. This allows the traffic light system to quickly respond to emergencies through the emergency response unit and minimize damage.

[0058] The signal system can further be equipped with an environmental monitoring unit, which can collect and analyze not only traffic volume but also environmental data. For example, the environmental monitoring unit can measure carbon dioxide concentrations and PM2.5 levels in the atmosphere. The environmental monitoring unit can also measure noise levels and analyze their correlation with traffic volume. Furthermore, the environmental monitoring unit can collect meteorological data such as temperature and humidity and analyze their correlation with traffic conditions. This allows the signal system to understand the interrelationship between traffic and the environment through the environmental monitoring unit, enabling environmentally friendly traffic management.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The camera unit uses cameras installed at each intersection and crosswalk to capture images of vehicles and pedestrians. The camera unit can set the camera's resolution to high and use a wide-angle lens to capture a wide area. Infrared cameras and waterproof cameras can also be used to capture clear images at night or in bad weather. Furthermore, multiple cameras can be linked together to optimize camera installation locations and minimize blind spots. Step 2: The analysis unit analyzes the video captured by the camera unit and recognizes the number, location, and movement of vehicles and pedestrians. The analysis unit uses an AI-based image recognition algorithm to detect the outlines of vehicles and pedestrians and identify their location based on a coordinate system. It can also learn movement patterns and detect abnormal movements. It can also improve the accuracy of the analysis by referencing past traffic data. Step 3: The adjustment unit adjusts the timing of the traffic lights based on the information recognized by the analysis unit. The adjustment unit aims to resolve the traffic jam by making the traffic light on the congested side longer. Also, if there are right-turning vehicles stuck, the adjustment unit can adjust the traffic light for the right-turning vehicles so that following vehicles do not get stuck. Furthermore, if there are no vehicles or pedestrians, the traffic light can be left red, reducing unnecessary waiting time. This makes it possible to reduce unnecessary waiting time at traffic lights and optimize traffic. The processing in the adjustment unit may be performed using AI or without AI.

[0061] (Example 2) A traffic light system according to an embodiment of the present invention replaces all traffic lights at intersections, crosswalks, and other locations nationwide with traffic lights capable of assessing traffic conditions through image recognition. The traffic light system reduces unnecessary wait times and optimizes traffic. For example, cameras installed at each intersection and crosswalk capture images of vehicles and pedestrians in real time. The captured images are then analyzed using AI to identify the number, location, and movement of vehicles and pedestrians. For example, if there are no vehicles or pedestrians at an intersection, the traffic light does not turn red, thereby reducing unnecessary wait times. Furthermore, the timing of traffic lights can be adjusted according to traffic congestion. For example, the traffic light on the congested side can be extended to alleviate congestion. Furthermore, if a single-lane intersection is blocked by a traffic jam of right-turning vehicles, the traffic light can be adjusted to accommodate the right-turning vehicle and prevent following vehicles from getting stuck. This reduces unnecessary wait times and optimizes traffic, resulting in smoother traffic flow. The traffic light system thus reduces unnecessary wait times and optimizes traffic. For example, when there are no vehicles on the road, the traffic light will not change to red, allowing vehicles to pass through smoothly. Also, by extending the traffic light on the congested side, congestion will be alleviated and traffic will flow more smoothly. Furthermore, when there are vehicles turning right, the traffic light will be adjusted to accommodate the vehicles turning right, allowing following vehicles to pass through smoothly. In this way, the traffic light system can reduce unnecessary waiting times and optimize traffic, resulting in smoother traffic flow.

[0062] A traffic light system according to an embodiment includes a camera unit, an analysis unit, and an adjustment unit. The camera unit captures images of vehicles and pedestrians using cameras installed at intersections and crosswalks. For example, the camera unit can capture a wide range of images by setting the camera's resolution high and using a wide-angle lens. The camera unit can also use an infrared camera or a waterproof camera to capture clear images even at night or in bad weather. The camera unit can also coordinate multiple cameras to optimize camera installation locations and minimize blind spots. The analysis unit uses AI to analyze the images captured by the camera unit and recognize the number, location, and movement of vehicles and pedestrians. For example, the analysis unit uses an image recognition algorithm to detect the outlines of vehicles and pedestrians and identify their locations based on a coordinate system. The analysis unit can also learn movement patterns and detect abnormal movement. The analysis unit can also improve the accuracy of the analysis by referring to past traffic data. The adjustment unit adjusts the timing of traffic signals based on the information recognized by the analysis unit. For example, the adjustment unit aims to alleviate traffic congestion by extending the traffic light on the congested side. In addition, when there is a backlog of right-turning vehicles, the adjustment unit can adjust the traffic light for right-turning vehicles to prevent following vehicles from getting stuck. Furthermore, when there are no vehicles or pedestrians, the adjustment unit can also not turn the traffic light red, thereby reducing unnecessary waiting time at the traffic light. This enables the traffic light system according to the embodiment to reduce unnecessary waiting time at the traffic light and optimize traffic. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can adjust the timing of the traffic light using an AI model that receives information recognized by the analysis unit as input and outputs the timing of the traffic light.

[0063] The adjustment unit can alleviate the congestion by extending the signal on the congested side. A specific method for extending the signal on the congested side is, for example, to set an extension time for the normal signal time. The adjustment unit detects congestion based on, for example, vehicle retention time and vehicle density, and determines the signal extension time. The adjustment unit can also monitor the congestion situation in real time and adjust the signal extension time as needed. Furthermore, the adjustment unit can also cooperate with signals at other intersections to alleviate the congestion. As a result, the congestion can be alleviated by extending the signal on the congested side. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can adjust the timing of the signal using an AI model that inputs the congestion situation and outputs the signal extension time.

[0064] When right-turning vehicles are blocked, the adjustment unit can adjust the traffic light for the right-turning vehicles to prevent following vehicles from getting stuck. Specific circumstances in which right-turning vehicles are blocked include, for example, the number of vehicles in the right-turn lane and the waiting time for a right turn. The adjustment unit, for example, monitors the number of vehicles in the right-turn lane and adjusts the traffic light when a certain number of vehicles or more are blocked. The adjustment unit can also monitor the waiting time for a right turn and adjust the traffic light when the waiting time exceeds a certain period of time. Furthermore, when adjusting the traffic light for right-turning vehicles, the adjustment unit can also adjust the timing of the traffic light so that following vehicles can pass smoothly. This makes it possible to adjust the traffic light for right-turning vehicles when right-turning vehicles are blocked, so that following vehicles do not get stuck. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can adjust the timing of the traffic light using an AI model that inputs the number of vehicles in the right-turn lane and the waiting time for a right turn and outputs the timing of the traffic light.

[0065] The analysis unit can analyze video in real time to recognize the number, location, and movement of vehicles and pedestrians. Specific methods for performing real-time analysis include, for example, minimizing data processing speed and delay time. The analysis unit can recognize the number, location, and movement of vehicles and pedestrians in real time, for example, using a high-speed image recognition algorithm. The analysis unit can also use high-performance hardware to process video data in real time. Furthermore, the analysis unit can instantly adjust traffic light timing based on the real-time analysis results. This allows the analysis unit to analyze video in real time to recognize the number, location, and movement of vehicles and pedestrians. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform real-time analysis using an AI model that inputs video data and outputs the number, location, and movement of vehicles and pedestrians.

[0066] The camera unit can capture images of vehicles and pedestrians using cameras installed at each intersection and crosswalk. For example, the camera unit can set the camera's resolution high and capture a wide area using a wide-angle lens. The camera unit can also use an infrared camera or a waterproof camera to capture clear images even at night or in bad weather. Furthermore, the camera unit can coordinate multiple cameras to optimize the camera installation location and minimize blind spots. This allows the camera unit to accurately capture images of vehicles and pedestrians using cameras installed at each intersection and crosswalk. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without AI. For example, the camera unit can capture images using an AI model that inputs camera image data and outputs the status of vehicles and pedestrians.

[0067] The adjustment unit can reduce unnecessary waiting time by not changing the signal to red when there are no vehicles or pedestrians. A specific method for reducing unnecessary waiting time at a traffic light is, for example, adjusting the timing of a traffic light based on the time of day when there are no vehicles or pedestrians and the waiting time at the traffic light. For example, the adjustment unit can extend the duration of the green signal without changing the signal to red when there are no vehicles or pedestrians. The adjustment unit can also monitor the status of vehicles and pedestrians in real time and adjust the timing of the signal as needed. Furthermore, the adjustment unit can reduce unnecessary waiting time at a traffic light by coordinating with signals at other intersections. This allows the adjustment unit to reduce unnecessary waiting time at a traffic light by not changing the signal to red when there are no vehicles or pedestrians. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can adjust the timing of the signal using an AI model that inputs the status of vehicles and pedestrians and outputs the timing of the signal.

[0068] The camera unit can estimate the user's emotions and adjust the camera's shooting angle and zoom based on the estimated user's emotions. For example, if the user is nervous, the camera unit can adjust the camera's shooting angle to provide a wide-angle view of the entire scene. If the user is relaxed, the camera unit can zoom in on a specific subject to provide detailed footage. If the user is in a hurry, the camera unit can also capture a close-up of only the important parts. This enables the camera unit to capture footage according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the camera unit may be performed using AI, or may be performed without AI. For example, the camera unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] The imaging unit can automatically adjust the camera's exposure and sensitivity during shooting according to the weather and time of day. For example, when shooting at night, the imaging unit increases the camera's sensitivity to capture clear images even in dark places. The imaging unit can also adjust the exposure during rainy weather to prevent raindrops from affecting the image. Furthermore, the imaging unit can lower the exposure during strong sunlight during the day to prevent the image from being overexposed. This enables the imaging unit to capture optimal images according to the weather and time of day. Some or all of the above-mentioned processing in the imaging unit may be performed using, or without, AI, for example. For example, the imaging unit can input data on the weather and time of day into the generation AI and have the generation AI adjust the exposure and sensitivity.

[0070] The image capturing unit can add a focus function for tracking a specific vehicle or pedestrian when capturing an image. For example, the image capturing unit automatically adjusts the focus of the camera to continue tracking a specific vehicle. The image capturing unit can also focus on a pedestrian when the pedestrian crosses an intersection and capture the image. Furthermore, the image capturing unit can focus on a right-turning vehicle when the vehicle enters an intersection and capture the image. This enables the image capturing unit to capture an image while tracking a specific target. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit can input data of a specific vehicle or pedestrian into the generation AI and have the generation AI adjust the focus.

[0071] The photographing unit can automatically adjust the position of the camera during photographing to minimize blind spots. For example, the photographing unit can move the position of the camera up, down, left, and right to minimize blind spots. The photographing unit can also adjust the photographing range so that there are no blind spots by linking multiple cameras. Furthermore, the photographing unit can adjust the height of the camera to reduce blind spots for vehicles and pedestrians. This enables the photographing unit to photograph with minimal blind spots. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input camera position data to the generation AI and have the generation AI adjust the position.

[0072] The image capture unit can estimate the user's emotions and adjust the storage period of the captured video based on the estimated user emotions. For example, if the user is nervous, the image capture unit can shorten the storage period of the video. Furthermore, if the user is relaxed, the image capture unit can also lengthen the storage period of the video. Furthermore, if the user is in a hurry, the image capture unit can set the storage period to save only important parts. This enables the image capture unit to adjust the storage period of the video according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image capture unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the image capture unit can input the user's emotion data into the generation AI and have the generation AI adjust the storage period.

[0073] The photographing unit can take into account surrounding buildings and obstacles when photographing in order to optimize the camera installation location. The photographing unit, for example, determines the camera installation location by considering the height and position of surrounding buildings. The photographing unit can also install the camera by selecting a location with few obstacles. Furthermore, the photographing unit can periodically review the camera installation location and relocate it to an optimal position. This enables the photographing unit to select the optimal camera installation location. Some or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input data on surrounding buildings and obstacles into the generating AI and cause the generating AI to optimize the installation location.

[0074] The photographing unit can share the camera footage with other traffic management systems in real time when photographing. For example, the photographing unit transmits the camera footage to a traffic management center in real time. The photographing unit can also share the camera footage in cooperation with other traffic management systems. Furthermore, the photographing unit can upload the camera footage to the cloud and share it with multiple systems. This enables the photographing unit to share footage in real time. Some or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the camera footage data to a generation AI and have the generation AI share the data with other traffic management systems.

[0075] The imaging unit can perform preprocessing for analyzing the camera image when capturing an image. The imaging unit can perform preprocessing to remove noise from the image, for example. The imaging unit can also perform preprocessing to adjust the resolution of the image. Furthermore, the imaging unit can also perform preprocessing to correct the color tone of the image. This enables the imaging unit to perform preprocessing for image analysis. Some or all of the above-mentioned processing in the imaging unit can be performed using, for example, AI, or can be performed without using AI. For example, the imaging unit can input the image data to a generation AI and have the generation AI perform preprocessing.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This enables the analysis unit to display analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0077] During analysis, the analysis unit learns patterns of vehicle and pedestrian movement and can detect abnormal movement. For example, the analysis unit detects an abnormality when a vehicle moves differently from its normal movement. The analysis unit can also detect an abnormality when a pedestrian moves differently from its normal movement. Furthermore, the analysis unit can learn patterns of vehicle and pedestrian movement and detect abnormal movement in real time. This enables the analysis unit to detect abnormal movement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on vehicle and pedestrian movement to the generation AI and cause the generation AI to detect abnormal movement.

[0078] The analysis unit can add a function to recognize the vehicle type and license plate during analysis. The analysis unit recognizes, for example, the vehicle type (passenger car, truck, bus, etc.). The analysis unit can also recognize and record the vehicle license plate. Furthermore, the analysis unit can recognize the vehicle type and license plate in real time. This enables the analysis unit to recognize the vehicle type and license plate. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data of the vehicle to the generation AI and have the generation AI recognize the type and license plate.

[0079] The analysis unit can add a function to estimate the age and gender of a pedestrian during analysis. The analysis unit, for example, analyzes the face of a pedestrian to estimate the age. The analysis unit can also analyze the body shape and clothing of a pedestrian to estimate the gender. Furthermore, the analysis unit can estimate the age and gender of a pedestrian in real time. This enables the analysis unit to estimate the age and gender of a pedestrian. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data of a pedestrian to a generation AI and have the generation AI estimate the age and gender.

[0080] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize displaying important analysis results. Furthermore, if the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. This enables the analysis unit to prioritize the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past traffic data. The analysis unit, for example, analyzes current traffic conditions based on past traffic data. The analysis unit can also detect abnormal traffic conditions by referring to past traffic data. Furthermore, the analysis unit can improve the accuracy of the analysis by utilizing past traffic data. This enables the analysis unit to improve the accuracy of the analysis by utilizing past traffic data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past traffic data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0082] The analysis unit can integrate data from other sensors during analysis. For example, the analysis unit can integrate data from an acoustic sensor to analyze traffic conditions. The analysis unit can also integrate data from a temperature sensor to analyze traffic conditions. Furthermore, the analysis unit can integrate data from other sensors to improve the accuracy of the analysis. In this way, the analysis unit improves the accuracy of the analysis by integrating data from other sensors. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other sensors into the generation AI and have the generation AI integrate the data.

[0083] During analysis, the analysis unit can share the analysis results with other traffic management systems. For example, the analysis unit transmits the analysis results to a traffic management center in real time. The analysis unit can also cooperate with other traffic management systems to share the analysis results. Furthermore, the analysis unit can upload the analysis results to the cloud and share them with multiple systems. In this way, the analysis unit can share the analysis results with other traffic management systems, thereby improving the efficiency of traffic management. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI and have the generation AI share the results with other traffic management systems.

[0084] The adjustment unit can estimate the user's emotion and adjust the timing of the signal based on the estimated user's emotion. For example, if the user is nervous, the adjustment unit can shorten the timing of the signal. Furthermore, if the user is relaxed, the adjustment unit can also lengthen the timing of the signal. Furthermore, if the user is in a hurry, the adjustment unit can quickly adjust the timing of the signal. This enables the adjustment unit to adjust the timing of the signal according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using an AI, for example, or without an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of the signal.

[0085] During adjustment, the adjustment unit can change the timing of the traffic light in response to a specific event. For example, when an emergency vehicle passes, the adjustment unit changes the traffic light to green to allow it to pass quickly. The adjustment unit can also adjust the timing of the traffic light to match the time when people are arriving and leaving school. Furthermore, the adjustment unit can change the timing of the traffic light in real time when a specific event occurs. This enables the adjustment unit to change the timing of the traffic light in response to a specific event. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input specific event data to the generation AI and cause the generation AI to change the timing of the traffic light.

[0086] During adjustment, the adjustment unit can optimize traffic flow in cooperation with signals at other intersections. The adjustment unit, for example, adjusts the timing of signals in cooperation with neighboring intersections. The adjustment unit can also smooth traffic flow in cooperation with signals at other intersections. Furthermore, the adjustment unit can coordinate signals at multiple intersections to optimize traffic. This enables the adjustment unit to optimize traffic flow by coordinating with signals at other intersections. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input signal data at other intersections to the generation AI and cause the generation AI to adjust the timing of the signals.

[0087] During adjustment, the adjustment unit can improve visibility by changing the color or blinking pattern of the traffic light. For example, the adjustment unit can improve visibility by changing the color of the traffic light. The adjustment unit can also improve visibility by changing the blinking pattern of the traffic light. Furthermore, the adjustment unit can change the color or blinking pattern of the traffic light in real time depending on a specific situation. This allows the adjustment unit to improve the visibility of the traffic light. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input data on the color or blinking pattern of the traffic light into the generation AI and cause the generation AI to improve visibility.

[0088] The adjustment unit can estimate the user's emotions and determine the priority of traffic signals based on the estimated user's emotions. For example, the adjustment unit can set a high priority for traffic signals when the user is nervous. The adjustment unit can also set a low priority for traffic signals when the user is relaxed. Furthermore, the adjustment unit can quickly determine the priority of traffic signals when the user is in a hurry. This enables the adjustment unit to determine the priority of traffic signals according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using an AI, for example, or without an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of traffic signals.

[0089] During adjustment, the adjustment unit can optimize the signal timing by referring to traffic volume prediction data. The adjustment unit adjusts the signal timing based on, for example, traffic volume prediction data. The adjustment unit can also set signal timing corresponding to peak traffic volume by referring to the prediction data. Furthermore, the adjustment unit can also adjust the signal timing in real time according to fluctuations in traffic volume by utilizing the prediction data. This enables the adjustment unit to optimize the signal timing by utilizing the traffic volume prediction data. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input traffic volume prediction data to the generation AI and cause the generation AI to optimize the signal timing.

[0090] During adjustment, the adjustment unit can adjust the signal timing in cooperation with other traffic management systems. The adjustment unit, for example, adjusts the signal timing in cooperation with other traffic management systems. The adjustment unit can also adjust the signal timing in real time in cooperation with a traffic management center. Furthermore, the adjustment unit can optimize the signal timing based on data from other systems. This enables the adjustment unit to adjust the signal timing in cooperation with other traffic management systems. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from other traffic management systems to the generation AI and cause the generation AI to adjust the signal timing.

[0091] The adjustment unit can add a remote control function for changing the signal timing in real time during adjustment. The adjustment unit changes the signal timing in real time using, for example, the remote control function. The adjustment unit can also adjust the signal timing remotely from a traffic management center. Furthermore, the adjustment unit can also quickly change the signal timing using the remote control function in an emergency. This enables the adjustment unit to change the signal timing using the remote control function. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input remote control data to the generation AI and cause the generation AI to change the signal timing. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described image capturing unit, analysis unit, and adjustment unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the image capturing unit can capture images of vehicles and pedestrians using the camera 42 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured images to recognize the number, positions, and movements of vehicles and pedestrians. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the timing of traffic signals based on the analyzed information. The adjustment unit may also be realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described image capturing unit, analysis unit, and adjustment unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image capturing unit can capture images of vehicles and pedestrians using the camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured images to recognize the number, positions, and movements of vehicles and pedestrians. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the timing of signals based on the analyzed information. The adjustment unit may also be realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned image capturing unit, analysis unit, and adjustment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the image capturing unit can capture images of vehicles and pedestrians using the camera 42 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the captured images to recognize the number, positions, and movements of vehicles and pedestrians. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the timing of signals based on the analyzed information. The adjustment unit may also be realized by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit can photograph the situation of vehicles and pedestrians using the camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photographed video to recognize the number, positions, and movements of vehicles and pedestrians. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the timing of signals based on the analyzed information. The adjustment unit may also be realized by the control unit 46A of the robot 414.

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

[0093] The traffic light system may further include a prediction unit. The prediction unit can predict future traffic conditions based on past traffic data and weather data. For example, the prediction unit analyzes past traffic data and learns traffic volume fluctuation patterns during specific time periods and days of the week. The prediction unit can also refer to weather data to predict traffic volume fluctuations on rainy or snowy days. Furthermore, the prediction unit can predict increases in traffic volume based on specific events (e.g., sporting events or concerts). This allows the traffic light system to predict future traffic conditions using the prediction unit and adjust signal timing in advance.

[0094] The traffic light system may further include a notification unit. The notification unit can provide real-time notifications to drivers and pedestrians according to traffic conditions. For example, if there is congestion, the notification unit may suggest a detour route to the driver. The notification unit may also notify pedestrians when the traffic light will change before they cross the street. Furthermore, the notification unit may also notify drivers to pay attention if an emergency vehicle is approaching. This allows the traffic light system to provide appropriate information according to traffic conditions via the notification unit, thereby ensuring smooth traffic flow.

[0095] The signal system can further include an energy management unit. The energy management unit can optimize the energy consumption of the traffic lights. For example, the energy management unit can use solar panels to supply power to the traffic lights. The energy management unit can also adjust the brightness of the traffic light's LED lights to reduce energy consumption. Furthermore, the energy management unit can reduce the operation of the traffic lights at night or during times of low traffic volume to save energy. In this way, the signal system can optimize energy consumption using the energy management unit and operate in an environmentally friendly manner.

[0096] The traffic light system can further include an emergency response unit. The emergency response unit can respond quickly when an emergency occurs. For example, in the event of a traffic accident, the emergency response unit can adjust the timing of traffic lights to allow emergency vehicles to arrive at the scene quickly. In the event of a natural disaster, the emergency response unit can also adjust the timing of traffic lights to secure evacuation routes. Furthermore, in the event of a terrorist attack or crime, the emergency response unit can quickly notify the police and fire department and adjust the timing of traffic lights to respond. This allows the traffic light system to quickly respond to emergencies through the emergency response unit and minimize damage.

[0097] The signal system can further be equipped with an environmental monitoring unit, which can collect and analyze not only traffic volume but also environmental data. For example, the environmental monitoring unit can measure carbon dioxide concentrations and PM2.5 levels in the atmosphere. The environmental monitoring unit can also measure noise levels and analyze their correlation with traffic volume. Furthermore, the environmental monitoring unit can collect meteorological data such as temperature and humidity and analyze their correlation with traffic conditions. This allows the signal system to understand the interrelationship between traffic and the environment through the environmental monitoring unit, enabling environmentally friendly traffic management.

[0098] The traffic light system may further include an emotion feedback unit. The emotion feedback unit may estimate the user's emotion and provide feedback based on the estimated emotion. For example, if the user is feeling stressed, the emotion feedback unit may provide music or a message to help the user relax. If the user is happy, the emotion feedback unit may also provide positive feedback. If the user is tired, the emotion feedback unit may also provide a message encouraging the user to take a break. In this way, the traffic light system may provide appropriate feedback according to the user's emotion via the emotion feedback unit, thereby improving user satisfaction.

[0099] The traffic light system can further include an emotion analysis unit. The emotion analysis unit can estimate the user's emotion and analyze the traffic situation based on the estimated emotion. For example, if the user is irritated, the emotion analysis unit can determine that there is a high possibility of traffic congestion. Alternatively, if the user is relaxed, the emotion analysis unit can determine that traffic is flowing smoothly. Furthermore, the emotion analysis unit can accumulate user emotion data and use it for long-term traffic situation analysis. This allows the traffic light system to analyze traffic situations based on the user's emotion using the emotion analysis unit, thereby achieving more accurate traffic management.

[0100] The traffic light system can further include an emotion prediction unit. The emotion prediction unit can predict future emotions based on the user's past emotion data. For example, the emotion prediction unit can predict that the user is likely to feel stressed during a specific time period or day of the week. The emotion prediction unit can also predict how the user will feel under specific weather conditions. Furthermore, the emotion prediction unit can predict how the user will feel in response to a specific event (for example, long driving hours or traffic jams). This allows the traffic light system to predict the user's future emotions using the emotion prediction unit and take appropriate measures in advance.

[0101] The traffic light system may further include an emotion adjustment unit. The emotion adjustment unit may estimate the user's emotion and adjust the timing of the traffic light based on the estimated emotion. For example, the emotion adjustment unit may shorten the waiting time for the traffic light if the user is feeling stressed. The emotion adjustment unit may also lengthen the waiting time for the traffic light if the user is relaxed. Furthermore, the emotion adjustment unit may quickly adjust the timing of the traffic light if the user is in a hurry. This allows the traffic light system to adjust the timing of the traffic light according to the user's emotion using the emotion adjustment unit, thereby reducing user stress.

[0102] The traffic light system may further include an emotion notification unit. The emotion notification unit may estimate the user's emotion and provide a notification based on the estimated emotion. For example, if the user is feeling stressed, the emotion notification unit may provide advice to relax. If the user is happy, the emotion notification unit may also provide a positive message. Furthermore, if the user is tired, the emotion notification unit may provide a notification encouraging the user to take a break. In this way, the traffic light system may provide an appropriate notification according to the user's emotion using the emotion notification unit, thereby improving user satisfaction.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The camera unit uses cameras installed at each intersection and crosswalk to capture images of vehicles and pedestrians. The camera unit can set the camera's resolution to high and use a wide-angle lens to capture a wide area. Infrared cameras and waterproof cameras can also be used to capture clear images at night or in bad weather. Furthermore, multiple cameras can be linked together to optimize camera installation locations and minimize blind spots. Step 2: The analysis unit analyzes the video captured by the camera unit and recognizes the number, location, and movement of vehicles and pedestrians. The analysis unit uses an AI-based image recognition algorithm to detect the outlines of vehicles and pedestrians and identify their location based on a coordinate system. It can also learn movement patterns and detect abnormal movements. It can also improve the accuracy of the analysis by referencing past traffic data. Step 3: The adjustment unit adjusts the timing of the traffic lights based on the information recognized by the analysis unit. The adjustment unit aims to resolve the traffic jam by making the traffic light on the congested side longer. Also, if there are right-turning vehicles stuck, the adjustment unit can adjust the traffic light for the right-turning vehicles so that following vehicles do not get stuck. Furthermore, if there are no vehicles or pedestrians, the traffic light can be left red, reducing unnecessary waiting time. This makes it possible to reduce unnecessary waiting time at traffic lights and optimize traffic. The processing in the adjustment unit may be performed using AI or without AI.

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

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

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

[0122] 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.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

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

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0143] The data processing device 12 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0155] 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.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. The camera unit, which is installed at each intersection and crosswalk, captures the situation of vehicles and pedestrians. an analysis unit that analyzes the video captured by the imaging unit and recognizes the number, positions, and movements of vehicles and pedestrians; an adjustment unit that adjusts the timing of the signal based on the information recognized by the analysis unit. A system characterized by:

2. The adjustment unit The traffic lights on the congested side will be made longer to alleviate the congestion.

2. The system of claim 1.

3. The adjustment unit If there is a traffic jam of right-turning vehicles, adjust the traffic lights to accommodate the right-turning vehicles and prevent following vehicles from getting stuck.

2. The system of claim 1.

4. The analysis unit Analyze video footage in real time to recognize the number, location, and movement of vehicles and pedestrians 2. The system of claim 1.

5. The imaging unit is Cameras installed at each intersection and crosswalk are used to capture images of vehicles and pedestrians.

2. The system of claim 1.

6. The adjustment unit If there are no vehicles or pedestrians, the traffic light will not turn red, reducing unnecessary waiting time.

2. The system of claim 1.

7. The imaging unit is Estimate the user's emotions and adjust the camera angle and zoom based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is When shooting, the camera automatically adjusts exposure and sensitivity according to the weather and time of day.

2. The system of claim 1.

Images