system

Abstract

The system according to this embodiment aims to monitor and analyze traffic conditions throughout a city in real time and to provide integrated management of traffic signals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a management unit. The collection unit collects camera information. The analysis unit analyzes the camera information collected by the collection unit and monitors, analyzes, and predicts traffic volume. The management unit integrates and manages traffic signals in real time based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the traffic conditions of the entire city have not been sufficiently monitored and analyzed in real time, and the traffic lights have not been integratedly managed, leaving room for improvement.

[0005] The system according to the embodiment aims to monitor and analyze the traffic conditions of the entire city in real time and integrally manage the traffic lights.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a management unit. The collection unit collects camera information. The analysis unit analyzes the camera information collected by the collection unit and monitors, analyzes, and predicts the traffic volume. The management unit integrally manages the traffic lights in real time based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor and analyze traffic conditions throughout the city in real time and manage traffic signals in an integrated manner. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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 4A, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that monitors the traffic conditions of an entire city and centrally manages all traffic signals in real time. By monitoring the traffic conditions of an entire city and centrally managing all traffic signals in real time, this AI agent system optimizes traffic flow, reduces congestion, and lowers environmental impact. For example, the AI ​​agent system installs multiple cameras to monitor the traffic conditions of an entire city and monitors traffic conditions in real time. For example, cameras are installed at major intersections and roads with heavy traffic to monitor traffic flow. This camera information is transmitted to the AI ​​agent. Next, the AI ​​agent analyzes the camera information to monitor, analyze, and predict traffic volume. Based on the camera information, the AI ​​agent grasps the current traffic conditions and predicts future traffic volume. For example, if an increase in traffic volume is predicted at a particular intersection, the timing of the traffic signals is adjusted to optimize traffic flow. Furthermore, based on the analysis results, all traffic signals are centrally managed in real time. The AI ​​agent adjusts the timing of each traffic signal to optimize traffic flow. For example, the timing of traffic signals at major intersections is adjusted to smooth traffic flow. This reduces congestion and lowers environmental impact. This system manages the entire city's transportation ecosystem, resulting in a sustainable transportation system. Specifically, it achieves congestion reduction, a decrease in traffic accidents, and a significant reduction in fuel consumption and CO2 emissions. For example, by having AI agents predict traffic volume and adjust the timing of traffic lights, traffic flow becomes smoother and congestion is alleviated. In addition, the risk of traffic accidents is reduced, and fuel consumption and CO2 emissions are lowered. Thus, the AI ​​agent system can monitor the traffic situation throughout the city and centrally manage traffic lights in real time, optimizing traffic flow, reducing congestion, and lowering the environmental impact.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and a management unit. The collection unit collects camera information. The collection unit collects information from cameras installed, for example, at major intersections and on busy roads. The collection unit collects camera information in real time and transmits it to the AI ​​agent. The collection unit periodically collects camera information and monitors traffic conditions. The collection unit converts camera information into a format that is easy to analyze. The analysis unit analyzes the camera information collected by the collection unit and monitors, analyzes, and predicts traffic volume. The analysis unit grasps the current traffic situation based on the camera information. The analysis unit predicts future traffic volume based on the camera information. The analysis unit analyzes traffic flow based on the camera information. The analysis unit identifies traffic bottlenecks based on the camera information. The management unit integrates and manages traffic signals in real time based on the analysis results obtained by the analysis unit. The management unit adjusts the timing of each traffic signal. The management unit adjusts the timing of traffic signals at major intersections. The management department adjusts the timing of traffic lights to optimize traffic flow, for example. The management department adjusts the timing of traffic lights to reduce congestion, for example. The management department adjusts the timing of traffic lights to reduce environmental impact, for example. As a result, the AI ​​agent system according to the embodiment can optimize traffic flow, reduce congestion, and reduce environmental impact by monitoring traffic conditions throughout the city and integrating and managing traffic lights in real time.

[0030] The data collection unit collects camera information. For example, it collects information from cameras installed at major intersections and busy roads. Specifically, these cameras are high-resolution and can provide clear images day and night. The cameras use wide-angle lenses to cover a wide area and capture traffic conditions in detail. The data collection unit acquires video data from these cameras in real time and transmits it to an AI agent. For example, the data collection unit periodically collects camera information to monitor traffic conditions. This allows for continuous understanding of traffic flow and congestion. The data collection unit converts camera information into a format that is easy to analyze. Specifically, it compresses video data and extracts and transmits only the necessary parts, reducing the amount of data and enabling efficient data processing. Furthermore, by optimizing the camera placement and angles, the data collection unit minimizes blind spots and collects more accurate information. This allows the data collection unit to monitor traffic conditions throughout the city in detail and provide information in real time.

[0031] The analysis unit analyzes camera information collected by the collection unit to monitor, analyze, and predict traffic volume. For example, the analysis unit uses camera information to understand the current traffic situation. Specifically, it uses AI to analyze video data and identify the number, speed, and direction of travel of vehicles. This allows for real-time understanding of current traffic volume and congestion. For example, the analysis unit predicts future traffic volume based on camera information. The AI ​​uses an algorithm that predicts future traffic volume based on past data and current conditions. This allows for advance prediction of congestion at specific times and locations, enabling appropriate countermeasures to be taken. For example, the analysis unit analyzes traffic flow based on camera information. Specifically, it tracks vehicle movements and visualizes traffic flow to identify bottlenecks and causes of congestion. For example, the analysis unit identifies traffic bottlenecks based on camera information. The AI ​​analyzes traffic flow and identifies the causes of congestion at specific intersections and roads. This allows the analysis unit to optimize traffic flow and propose specific measures to reduce congestion. Furthermore, the analysis unit uses anomaly detection algorithms to detect unusual traffic patterns and abnormal situations early and respond quickly. This allows the analysis unit to monitor traffic conditions in real time, predict future risks, and take appropriate measures.

[0032] The management department centrally manages traffic signals in real time based on the analysis results obtained by the analysis department. For example, the management department adjusts the timing of each traffic signal. Specifically, it uses algorithms that optimize the lighting time and order of traffic signals based on traffic condition data provided by the analysis department. This makes traffic flow smoother and reduces congestion. For example, the management department adjusts the timing of traffic signals at major intersections. Especially at intersections with heavy traffic, fine-tuning the timing of traffic signals can optimize traffic flow. For example, the management department adjusts the timing of traffic signals to optimize traffic flow. Specifically, it adjusts the lighting time of traffic signals to smooth the flow of vehicles and minimize waiting times at intersections. For example, the management department adjusts the timing of traffic signals to reduce congestion. By predicting congestion at specific times and locations and adjusting the timing of traffic signals, congestion can be prevented in advance. For example, the management department adjusts the timing of traffic signals to reduce environmental impact. By reducing vehicle idling time and decreasing fuel consumption and emissions, the environmental burden can be reduced. Furthermore, the management department adjusts the timing of traffic signals in real time in response to changes in traffic conditions, ensuring optimal traffic management at all times. This allows the management department to efficiently manage traffic conditions throughout the city, optimize traffic flow, reduce congestion, and lower environmental impact.

[0033] The data collection unit can collect information from cameras installed at major intersections and busy roads. For example, the data collection unit collects information from cameras installed at major intersections. For example, the data collection unit collects information from cameras installed on busy roads. For example, the data collection unit collects information in real time from cameras installed at major intersections and busy roads. This allows for an accurate understanding of traffic conditions by collecting information from major intersections and busy roads. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information acquired from cameras installed at major intersections and busy roads into a generating AI and have the generating AI perform the analysis of the information.

[0034] The analysis unit can grasp the current traffic situation and predict future traffic volume based on camera information. For example, the analysis unit grasps the current traffic situation based on camera information. For example, the analysis unit predicts future traffic volume based on camera information. For example, the analysis unit analyzes traffic flow based on camera information. For example, the analysis unit identifies traffic bottlenecks based on camera information. By doing so, the timing of traffic signals can be appropriately adjusted by grasping the current traffic situation and predicting future traffic volume. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input camera information into a generating AI and have the generating AI perform the task of grasping the current traffic situation and predicting future traffic volume.

[0035] The management unit can adjust the timing of each traffic light to optimize traffic flow. The management unit can, for example, adjust the timing of each traffic light. The management unit can, for example, adjust the timing of traffic lights at major intersections. The management unit can, for example, adjust the timing of traffic lights to optimize traffic flow. The management unit can, for example, adjust the timing of traffic lights to reduce congestion. The management unit can, for example, adjust the timing of traffic lights to reduce environmental impact. In this way, by adjusting the timing of traffic lights, traffic flow can be optimized and congestion can be reduced. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can have a generating AI perform the adjustment of traffic light timing.

[0036] The management department can reduce traffic congestion by adjusting the timing of traffic lights. For example, the management department can reduce traffic congestion by adjusting the timing of traffic lights. For example, the management department can reduce traffic congestion by adjusting the timing of traffic lights at major intersections. For example, the management department can reduce traffic congestion by adjusting the timing of traffic lights to optimize traffic flow. For example, the management department can reduce traffic congestion by adjusting the timing of traffic lights during peak traffic hours. In this way, traffic congestion can be reduced by adjusting the timing of traffic lights. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can have a generating AI perform the adjustment of traffic light timing.

[0037] The management unit can reduce the environmental impact by adjusting the timing of traffic lights. For example, the management unit can reduce the environmental impact by adjusting the timing of traffic lights. For example, the management unit can reduce the environmental impact by adjusting the timing of traffic lights at major intersections. For example, the management unit can reduce the environmental impact by adjusting the timing of traffic lights to optimize traffic flow. For example, the management unit can reduce the environmental impact by adjusting the timing of traffic lights during peak traffic hours. In this way, the environmental impact can be reduced by adjusting the timing of traffic lights. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can have a generating AI perform the adjustment of the timing of traffic lights.

[0038] The data collection unit can detect the occurrence of traffic accidents in real time and collect information preferentially when an accident occurs. For example, when a traffic accident occurs, the data collection unit can preferentially collect camera information from the surrounding area to understand the details of the accident. For example, when the data collection unit detects the occurrence of a traffic accident, it can collect camera information from the accident scene in real time to enable a rapid response. For example, the data collection unit can preferentially collect camera information from the area where the traffic accident occurred to provide data for optimizing traffic flow. This enables a rapid response by detecting the occurrence of traffic accidents in real time and collecting information preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for detecting the occurrence of traffic accidents into a generating AI and have the generating AI perform information collection when an accident occurs.

[0039] The data collection unit can automatically adjust the camera's collection range and resolution according to the weather and time of day. For example, in rainy weather, the collection unit widens the camera's collection range to compensate for poor visibility. For example, at night, the collection unit increases the camera's resolution to improve visibility in dark places. For example, during times of heavy traffic, the collection unit narrows the camera's collection range to collect more detailed information. This allows for an accurate understanding of traffic conditions by automatically adjusting the camera's collection range and resolution according to the weather and time of day. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform camera adjustments according to the weather and time of day.

[0040] The data collection unit can predict peak traffic hours and adjust the collection frequency when collecting camera information. For example, during commuting hours, the collection unit increases the frequency of camera information collection to gain a detailed understanding of traffic conditions. For example, during late-night hours when traffic is light, the collection unit decreases the frequency of camera information collection to reduce the system load. For example, on weekends and holidays, the collection unit predicts fluctuations in traffic volume and adjusts the frequency of camera information collection. This allows for a detailed understanding of traffic conditions by predicting peak traffic hours and adjusting the collection frequency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for predicting peak traffic hours into a generating AI and have the generating AI adjust the collection frequency.

[0041] The data collection unit can prioritize monitoring areas with a high risk of traffic accidents when collecting camera information. For example, the data collection unit can prioritize collecting camera information from intersections where traffic accidents frequently occur. For example, the data collection unit can monitor camera information from areas with a high risk of traffic accidents in real time. For example, the data collection unit can prioritize collecting camera information from relevant areas during times when the risk of traffic accidents is high. By prioritizing the monitoring of areas with a high risk of traffic accidents, accidents can be prevented. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data to identify areas with a high risk of traffic accidents into a generating AI and have the generating AI perform the monitoring.

[0042] The analysis unit can improve the accuracy of its analysis by referring to past traffic data when analyzing traffic volume. For example, the analysis unit can analyze the current traffic situation based on past traffic data to improve accuracy. For example, the analysis unit can predict traffic volume for specific time periods or days of the week by referring to past traffic data. For example, the analysis unit can analyze past traffic data to understand traffic patterns and improve analysis accuracy. Thus, the accuracy of the analysis is improved by referring to past traffic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past traffic data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0043] The analysis unit can consider the impact of specific events when analyzing traffic volume. For example, the analysis unit can predict traffic volume during a sporting event and reflect it in the analysis. For example, the analysis unit can predict traffic volume during a concert and reflect it in the analysis. For example, the analysis unit can adjust the analysis results by considering fluctuations in traffic volume due to specific events. This improves the accuracy of the analysis results by considering the impact of specific events. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data to consider the impact of specific events into a generating AI and have the generating AI perform the analysis.

[0044] The analysis unit can improve the accuracy of its analysis by referring to weather data when analyzing traffic volume. For example, the analysis unit predicts traffic volume during rainy weather and reflects it in the analysis. For example, the analysis unit predicts traffic volume during sunny weather and reflects it in the analysis. For example, the analysis unit performs an analysis that takes into account fluctuations in traffic volume by referring to weather data. This improves the accuracy of the analysis by referring to weather data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input weather data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0045] The analysis unit can perform analyses to predict the risk of traffic accidents when analyzing traffic volume. For example, the analysis unit predicts the risk of traffic accidents based on past traffic accident data. For example, the analysis unit analyzes the risk of traffic accidents considering fluctuations in traffic volume. For example, the analysis unit predicts the risk of traffic accidents and reflects it in the analysis results. In this way, accidents can be prevented by predicting the risk of traffic accidents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for predicting the risk of traffic accidents into a generating AI and have the generating AI perform the analysis.

[0046] The control unit can adjust the timing of traffic lights in real time in response to fluctuations in traffic volume. For example, if traffic volume increases, the control unit adjusts the timing of traffic lights to smooth the flow of traffic. For example, if traffic volume decreases, the control unit adjusts the timing of traffic lights to optimize the flow of traffic. The control unit adjusts the timing of traffic lights in real time in response to fluctuations in traffic volume. This allows for the optimization of traffic flow by adjusting the timing of traffic lights in real time in response to fluctuations in traffic volume. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input traffic volume fluctuation data into a generating AI and have the generating AI perform the adjustment of the timing of traffic lights.

[0047] The control unit can automatically adjust the timing of traffic lights to respond to specific traffic conditions. For example, the control unit can adjust the timing of traffic lights to allow emergency vehicles to pass quickly. For example, the control unit automatically adjusts the timing of traffic lights when it detects the passage of an emergency vehicle. For example, the control unit adjusts the timing of traffic lights in real time to respond to the passage of an emergency vehicle. This enables a quick response by automatically adjusting the timing of traffic lights to respond to specific traffic conditions. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data to respond to specific traffic conditions into a generating AI and have the generating AI perform the adjustment of the timing of traffic lights.

[0048] The management department can adjust the timing of traffic lights to reduce the risk of traffic accidents. For example, the management department can adjust the timing of traffic lights at intersections with a high risk of traffic accidents to reduce the risk. For example, the management department can adjust the timing of traffic lights during times when the risk of traffic accidents is high to reduce the risk. For example, the management department can predict the risk of traffic accidents and adjust the timing of traffic lights to reduce the risk. In this way, the risk of traffic accidents can be reduced by adjusting the timing of traffic lights. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input traffic accident risk data into a generating AI and have the generating AI perform the adjustment of traffic light timing.

[0049] The management department can consider the operating schedule of public transport when adjusting the timing of traffic lights. For example, the management department can adjust the timing of traffic lights to match the operating schedule of public transport. For example, the management department can adjust the timing of traffic lights considering delay information of public transport. For example, the management department can adjust the timing of traffic lights by referring to the operating schedule of public transport in real time. This allows for the optimization of traffic flow by considering the operating schedule of public transport. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input public transport operating schedule data into a generating AI and have the generating AI perform the adjustment of the timing of traffic lights.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analysis unit can incorporate real-time weather data into its traffic condition analysis. For example, since traffic volume is expected to increase during rainy weather, the analysis unit can adjust the timing of traffic lights based on weather data. Also, since the risk of traffic accidents increases during adverse weather conditions such as snow and fog, the analysis unit can optimize traffic flow by taking this data into consideration. Furthermore, since traffic volume can also change due to temperature fluctuations, the analysis unit can predict traffic conditions based on temperature data and adjust the timing of traffic lights. This enables flexible traffic management in accordance with weather conditions, resulting in smoother traffic flow.

[0052] The management department can adjust traffic light timings based on past accident data to reduce the risk of traffic accidents. For example, if a particular intersection has a history of many accidents, the timing of the traffic lights at that intersection can be adjusted to reduce the risk of accidents. Also, if accidents frequently occur during specific time periods, the timing of traffic lights can be adjusted during those periods to optimize traffic flow. Furthermore, by identifying areas with a high risk of traffic accidents and adjusting the timing of traffic lights in those areas, the risk of accidents can be reduced. This makes it possible to prevent traffic accidents from occurring and create a safer traffic environment.

[0053] The data collection department can utilize drones for monitoring traffic conditions. For example, drones can be flown over major intersections and busy roads to monitor traffic conditions in real time. Drones can also quickly arrive at the scene when congestion or accidents occur and collect detailed information. Furthermore, because drones can monitor traffic conditions over a wide area at once, it is possible to efficiently grasp the traffic situation across the entire city. As a result, utilizing drones improves the accuracy of traffic condition monitoring and enables a quicker response.

[0054] The analysis unit can utilize social media data in traffic volume analysis. For example, it can collect traffic information posted on social media and incorporate it into the analysis. Furthermore, it can grasp the occurrence of traffic accidents and congestion in real time from user posts on social media. In addition, it can predict traffic volume fluctuations caused by specific events based on event information on social media and incorporate this into the analysis. As a result, utilizing social media data improves the accuracy of traffic condition analysis and enables faster responses.

[0055] The management department can consider public transport schedules when adjusting traffic light timings. For example, by adjusting traffic light timings to match bus and train schedules, the operation of public transport can be made smoother. Furthermore, by adjusting traffic light timings based on public transport delay information, delays can be minimized. In addition, by referencing public transport schedules in real time and adjusting traffic light timings accordingly, traffic flow can be optimized. In this way, by considering public transport schedules, traffic flow can be made smoother and the convenience of public transport users can be improved.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects camera information. For example, it collects information from cameras installed at major intersections and busy roads, collects camera information in real time, and transmits it to the AI ​​agent. It also periodically collects camera information to monitor traffic conditions and converts it into a format that is easy to analyze. Step 2: The analysis unit analyzes the camera information collected by the collection unit to monitor, analyze, and predict traffic volume. For example, it uses camera information to understand the current traffic situation, predict future traffic volume, analyze traffic flow, and identify traffic bottlenecks. Step 3: The management unit integrates and manages the traffic signals in real time based on the analysis results obtained by the analysis unit. For example, it adjusts the timing of each traffic signal, adjusts the timing of traffic signals at major intersections, optimizes traffic flow, reduces congestion, and adjusts the timing of traffic signals to reduce environmental impact.

[0058] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that monitors the traffic conditions of an entire city and centrally manages all traffic signals in real time. By monitoring the traffic conditions of an entire city and centrally managing all traffic signals in real time, this AI agent system optimizes traffic flow, reduces congestion, and lowers environmental impact. For example, the AI ​​agent system installs multiple cameras to monitor the traffic conditions of an entire city and monitors traffic conditions in real time. For example, cameras are installed at major intersections and roads with heavy traffic to monitor traffic flow. This camera information is transmitted to the AI ​​agent. Next, the AI ​​agent analyzes the camera information to monitor, analyze, and predict traffic volume. Based on the camera information, the AI ​​agent grasps the current traffic conditions and predicts future traffic volume. For example, if an increase in traffic volume is predicted at a particular intersection, the timing of the traffic signals is adjusted to optimize traffic flow. Furthermore, based on the analysis results, all traffic signals are centrally managed in real time. The AI ​​agent adjusts the timing of each traffic signal to optimize traffic flow. For example, the timing of traffic signals at major intersections is adjusted to smooth traffic flow. This reduces congestion and lowers environmental impact. This system manages the entire city's transportation ecosystem, resulting in a sustainable transportation system. Specifically, it achieves congestion reduction, a decrease in traffic accidents, and a significant reduction in fuel consumption and CO2 emissions. For example, by having AI agents predict traffic volume and adjust the timing of traffic lights, traffic flow becomes smoother and congestion is alleviated. In addition, the risk of traffic accidents is reduced, and fuel consumption and CO2 emissions are lowered. Thus, the AI ​​agent system can monitor the traffic situation throughout the city and centrally manage traffic lights in real time, optimizing traffic flow, reducing congestion, and lowering the environmental impact.

[0059] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and a management unit. The collection unit collects camera information. The collection unit collects information from cameras installed, for example, at major intersections and on busy roads. The collection unit collects camera information in real time and transmits it to the AI ​​agent. The collection unit periodically collects camera information and monitors traffic conditions. The collection unit converts camera information into a format that is easy to analyze. The analysis unit analyzes the camera information collected by the collection unit and monitors, analyzes, and predicts traffic volume. The analysis unit grasps the current traffic situation based on the camera information. The analysis unit predicts future traffic volume based on the camera information. The analysis unit analyzes traffic flow based on the camera information. The analysis unit identifies traffic bottlenecks based on the camera information. The management unit integrates and manages traffic signals in real time based on the analysis results obtained by the analysis unit. The management unit adjusts the timing of each traffic signal. The management unit adjusts the timing of traffic signals at major intersections. The management department adjusts the timing of traffic lights to optimize traffic flow, for example. The management department adjusts the timing of traffic lights to reduce congestion, for example. The management department adjusts the timing of traffic lights to reduce environmental impact, for example. As a result, the AI ​​agent system according to the embodiment can optimize traffic flow, reduce congestion, and reduce environmental impact by monitoring traffic conditions throughout the city and integrating and managing traffic lights in real time.

[0060] The data collection unit collects camera information. For example, it collects information from cameras installed at major intersections and busy roads. Specifically, these cameras are high-resolution and can provide clear images day and night. The cameras use wide-angle lenses to cover a wide area and capture traffic conditions in detail. The data collection unit acquires video data from these cameras in real time and transmits it to an AI agent. For example, the data collection unit periodically collects camera information to monitor traffic conditions. This allows for continuous understanding of traffic flow and congestion. The data collection unit converts camera information into a format that is easy to analyze. Specifically, it compresses video data and extracts and transmits only the necessary parts, reducing the amount of data and enabling efficient data processing. Furthermore, by optimizing the camera placement and angles, the data collection unit minimizes blind spots and collects more accurate information. This allows the data collection unit to monitor traffic conditions throughout the city in detail and provide information in real time.

[0061] The analysis unit analyzes camera information collected by the collection unit to monitor, analyze, and predict traffic volume. For example, the analysis unit uses camera information to understand the current traffic situation. Specifically, it uses AI to analyze video data and identify the number, speed, and direction of travel of vehicles. This allows for real-time understanding of current traffic volume and congestion. For example, the analysis unit predicts future traffic volume based on camera information. The AI ​​uses an algorithm that predicts future traffic volume based on past data and current conditions. This allows for advance prediction of congestion at specific times and locations, enabling appropriate countermeasures to be taken. For example, the analysis unit analyzes traffic flow based on camera information. Specifically, it tracks vehicle movements and visualizes traffic flow to identify bottlenecks and causes of congestion. For example, the analysis unit identifies traffic bottlenecks based on camera information. The AI ​​analyzes traffic flow and identifies the causes of congestion at specific intersections and roads. This allows the analysis unit to optimize traffic flow and propose specific measures to reduce congestion. Furthermore, the analysis unit uses anomaly detection algorithms to detect unusual traffic patterns and abnormal situations early and respond quickly. This allows the analysis unit to monitor traffic conditions in real time, predict future risks, and take appropriate measures.

[0062] The management department centrally manages traffic signals in real time based on the analysis results obtained by the analysis department. For example, the management department adjusts the timing of each traffic signal. Specifically, it uses algorithms that optimize the lighting time and order of traffic signals based on traffic condition data provided by the analysis department. This makes traffic flow smoother and reduces congestion. For example, the management department adjusts the timing of traffic signals at major intersections. Especially at intersections with heavy traffic, fine-tuning the timing of traffic signals can optimize traffic flow. For example, the management department adjusts the timing of traffic signals to optimize traffic flow. Specifically, it adjusts the lighting time of traffic signals to smooth the flow of vehicles and minimize waiting times at intersections. For example, the management department adjusts the timing of traffic signals to reduce congestion. By predicting congestion at specific times and locations and adjusting the timing of traffic signals, congestion can be prevented in advance. For example, the management department adjusts the timing of traffic signals to reduce environmental impact. By reducing vehicle idling time and decreasing fuel consumption and emissions, the environmental burden can be reduced. Furthermore, the management department adjusts the timing of traffic signals in real time in response to changes in traffic conditions, ensuring optimal traffic management at all times. This allows the management department to efficiently manage traffic conditions throughout the city, optimize traffic flow, reduce congestion, and lower environmental impact.

[0063] The data collection unit can collect information from cameras installed at major intersections and busy roads. For example, the data collection unit collects information from cameras installed at major intersections. For example, the data collection unit collects information from cameras installed on busy roads. For example, the data collection unit collects information in real time from cameras installed at major intersections and busy roads. This allows for an accurate understanding of traffic conditions by collecting information from major intersections and busy roads. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information acquired from cameras installed at major intersections and busy roads into a generating AI and have the generating AI perform the analysis of the information.

[0064] The analysis unit can grasp the current traffic situation and predict future traffic volume based on camera information. For example, the analysis unit grasps the current traffic situation based on camera information. For example, the analysis unit predicts future traffic volume based on camera information. For example, the analysis unit analyzes traffic flow based on camera information. For example, the analysis unit identifies traffic bottlenecks based on camera information. By doing so, the timing of traffic signals can be appropriately adjusted by grasping the current traffic situation and predicting future traffic volume. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input camera information into a generating AI and have the generating AI perform the task of grasping the current traffic situation and predicting future traffic volume.

[0065] The management unit can adjust the timing of each traffic light to optimize traffic flow. The management unit can, for example, adjust the timing of each traffic light. The management unit can, for example, adjust the timing of traffic lights at major intersections. The management unit can, for example, adjust the timing of traffic lights to optimize traffic flow. The management unit can, for example, adjust the timing of traffic lights to reduce congestion. The management unit can, for example, adjust the timing of traffic lights to reduce environmental impact. In this way, by adjusting the timing of traffic lights, traffic flow can be optimized and congestion can be reduced. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can have a generating AI perform the adjustment of traffic light timing.

[0066] The management department can reduce traffic congestion by adjusting the timing of traffic lights. For example, the management department can reduce traffic congestion by adjusting the timing of traffic lights. For example, the management department can reduce traffic congestion by adjusting the timing of traffic lights at major intersections. For example, the management department can reduce traffic congestion by adjusting the timing of traffic lights to optimize traffic flow. For example, the management department can reduce traffic congestion by adjusting the timing of traffic lights during peak traffic hours. In this way, traffic congestion can be reduced by adjusting the timing of traffic lights. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can have a generating AI perform the adjustment of traffic light timing.

[0067] The management unit can reduce the environmental impact by adjusting the timing of traffic lights. For example, the management unit can reduce the environmental impact by adjusting the timing of traffic lights. For example, the management unit can reduce the environmental impact by adjusting the timing of traffic lights at major intersections. For example, the management unit can reduce the environmental impact by adjusting the timing of traffic lights to optimize traffic flow. For example, the management unit can reduce the environmental impact by adjusting the timing of traffic lights during peak traffic hours. In this way, the environmental impact can be reduced by adjusting the timing of traffic lights. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can have a generating AI perform the adjustment of the timing of traffic lights.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of camera data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit increases the frequency of camera data collection to respond quickly to changes in traffic conditions. For example, if the user is relaxed, the data collection unit decreases the frequency of camera data collection to reduce the system load. For example, if the user is in a hurry, the data collection unit shortens the timing of camera data collection to enhance real-time understanding of traffic conditions. This allows for a quick response to changes in traffic conditions by adjusting the timing of camera data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0069] The data collection unit can detect the occurrence of traffic accidents in real time and collect information preferentially when an accident occurs. For example, when a traffic accident occurs, the data collection unit can preferentially collect camera information from the surrounding area to understand the details of the accident. For example, when the data collection unit detects the occurrence of a traffic accident, it can collect camera information from the accident scene in real time to enable a rapid response. For example, the data collection unit can preferentially collect camera information from the area where the traffic accident occurred to provide data for optimizing traffic flow. This enables a rapid response by detecting the occurrence of traffic accidents in real time and collecting information preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for detecting the occurrence of traffic accidents into a generating AI and have the generating AI perform information collection when an accident occurs.

[0070] The data collection unit can automatically adjust the camera's collection range and resolution according to the weather and time of day. For example, in rainy weather, the collection unit widens the camera's collection range to compensate for poor visibility. For example, at night, the collection unit increases the camera's resolution to improve visibility in dark places. For example, during times of heavy traffic, the collection unit narrows the camera's collection range to collect more detailed information. This allows for an accurate understanding of traffic conditions by automatically adjusting the camera's collection range and resolution according to the weather and time of day. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform camera adjustments according to the weather and time of day.

[0071] The data collection unit can estimate the user's emotions and determine the priority of camera information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting camera information from major intersections. If the user is relaxed, the data collection unit will prioritize collecting camera information from areas with low traffic volume. If the user is in a hurry, the data collection unit will prioritize collecting camera information from areas prone to traffic congestion. By prioritizing camera information according to the user's emotions, important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The data collection unit can predict peak traffic hours and adjust the collection frequency when collecting camera information. For example, during commuting hours, the collection unit increases the frequency of camera information collection to gain a detailed understanding of traffic conditions. For example, during late-night hours when traffic is light, the collection unit decreases the frequency of camera information collection to reduce the system load. For example, on weekends and holidays, the collection unit predicts fluctuations in traffic volume and adjusts the frequency of camera information collection. This allows for a detailed understanding of traffic conditions by predicting peak traffic hours and adjusting the collection frequency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for predicting peak traffic hours into a generating AI and have the generating AI adjust the collection frequency.

[0073] The data collection unit can prioritize monitoring areas with a high risk of traffic accidents when collecting camera information. For example, the data collection unit can prioritize collecting camera information from intersections where traffic accidents frequently occur. For example, the data collection unit can monitor camera information from areas with a high risk of traffic accidents in real time. For example, the data collection unit can prioritize collecting camera information from relevant areas during times when the risk of traffic accidents is high. By prioritizing the monitoring of areas with a high risk of traffic accidents, accidents can be prevented. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data to identify areas with a high risk of traffic accidents into a generating AI and have the generating AI perform the monitoring.

[0074] 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 tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The analysis unit can improve the accuracy of its analysis by referring to past traffic data when analyzing traffic volume. For example, the analysis unit can analyze the current traffic situation based on past traffic data to improve accuracy. For example, the analysis unit can predict traffic volume for specific time periods or days of the week by referring to past traffic data. For example, the analysis unit can analyze past traffic data to understand traffic patterns and improve analysis accuracy. Thus, the accuracy of the analysis is improved by referring to past traffic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past traffic data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0076] The analysis unit can consider the impact of specific events when analyzing traffic volume. For example, the analysis unit can predict traffic volume during a sporting event and reflect it in the analysis. For example, the analysis unit can predict traffic volume during a concert and reflect it in the analysis. For example, the analysis unit can adjust the analysis results by considering fluctuations in traffic volume due to specific events. This improves the accuracy of the analysis results by considering the impact of specific events. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data to consider the impact of specific events into a generating AI and have the generating AI perform the analysis.

[0077] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit will display detailed analysis results. For example, if the user is in a hurry, the analysis unit will prioritize displaying concise analysis results. In this way, by prioritizing analysis results according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The analysis unit can improve the accuracy of its analysis by referring to weather data when analyzing traffic volume. For example, the analysis unit predicts traffic volume during rainy weather and reflects it in the analysis. For example, the analysis unit predicts traffic volume during sunny weather and reflects it in the analysis. For example, the analysis unit performs an analysis that takes into account fluctuations in traffic volume by referring to weather data. This improves the accuracy of the analysis by referring to weather data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input weather data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0079] The analysis unit can perform analyses to predict the risk of traffic accidents when analyzing traffic volume. For example, the analysis unit predicts the risk of traffic accidents based on past traffic accident data. For example, the analysis unit analyzes the risk of traffic accidents considering fluctuations in traffic volume. For example, the analysis unit predicts the risk of traffic accidents and reflects it in the analysis results. In this way, accidents can be prevented by predicting the risk of traffic accidents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for predicting the risk of traffic accidents into a generating AI and have the generating AI perform the analysis.

[0080] The control unit can estimate the user's emotions and adjust the timing of traffic lights based on the estimated emotions. For example, if the user is stressed, the control unit can adjust the timing of traffic lights to smooth the flow of traffic. For example, if the user is relaxed, the control unit can adjust the timing of traffic lights to optimize the flow of traffic. For example, if the user is in a hurry, the control unit can adjust the timing of traffic lights to enable faster travel. In this way, traffic flow can be made smoother by adjusting the timing of traffic lights according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or not using AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The control unit can adjust the timing of traffic lights in real time in response to fluctuations in traffic volume. For example, if traffic volume increases, the control unit adjusts the timing of traffic lights to smooth the flow of traffic. For example, if traffic volume decreases, the control unit adjusts the timing of traffic lights to optimize the flow of traffic. The control unit adjusts the timing of traffic lights in real time in response to fluctuations in traffic volume. This allows for the optimization of traffic flow by adjusting the timing of traffic lights in real time in response to fluctuations in traffic volume. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input traffic volume fluctuation data into a generating AI and have the generating AI perform the adjustment of the timing of traffic lights.

[0082] The control unit can automatically adjust the timing of traffic lights to respond to specific traffic conditions. For example, the control unit can adjust the timing of traffic lights to allow emergency vehicles to pass quickly. For example, the control unit automatically adjusts the timing of traffic lights when it detects the passage of an emergency vehicle. For example, the control unit adjusts the timing of traffic lights in real time to respond to the passage of an emergency vehicle. This enables a quick response by automatically adjusting the timing of traffic lights to respond to specific traffic conditions. Some or all of the above processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data to respond to specific traffic conditions into a generating AI and have the generating AI perform the adjustment of the timing of traffic lights.

[0083] The management department can adjust the timing of traffic lights to reduce the risk of traffic accidents. For example, the management department can adjust the timing of traffic lights at intersections with a high risk of traffic accidents to reduce the risk. For example, the management department can adjust the timing of traffic lights during times when the risk of traffic accidents is high to reduce the risk. For example, the management department can predict the risk of traffic accidents and adjust the timing of traffic lights to reduce the risk. In this way, the risk of traffic accidents can be reduced by adjusting the timing of traffic lights. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input traffic accident risk data into a generating AI and have the generating AI perform the adjustment of traffic light timing.

[0084] The management department can consider the operating schedule of public transport when adjusting the timing of traffic lights. For example, the management department can adjust the timing of traffic lights to match the operating schedule of public transport. For example, the management department can adjust the timing of traffic lights considering delay information of public transport. For example, the management department can adjust the timing of traffic lights by referring to the operating schedule of public transport in real time. This allows for the optimization of traffic flow by considering the operating schedule of public transport. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input public transport operating schedule data into a generating AI and have the generating AI perform the adjustment of the timing of traffic lights.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The analysis unit can incorporate real-time weather data into its traffic condition analysis. For example, since traffic volume is expected to increase during rainy weather, the analysis unit can adjust the timing of traffic lights based on weather data. Also, since the risk of traffic accidents increases during adverse weather conditions such as snow and fog, the analysis unit can optimize traffic flow by taking this data into consideration. Furthermore, since traffic volume can also change due to temperature fluctuations, the analysis unit can predict traffic conditions based on temperature data and adjust the timing of traffic lights. This enables flexible traffic management in accordance with weather conditions, resulting in smoother traffic flow.

[0087] The management department can adjust traffic light timings based on past accident data to reduce the risk of traffic accidents. For example, if a particular intersection has a history of many accidents, the timing of the traffic lights at that intersection can be adjusted to reduce the risk of accidents. Also, if accidents frequently occur during specific time periods, the timing of traffic lights can be adjusted during those periods to optimize traffic flow. Furthermore, by identifying areas with a high risk of traffic accidents and adjusting the timing of traffic lights in those areas, the risk of accidents can be reduced. This makes it possible to prevent traffic accidents from occurring and create a safer traffic environment.

[0088] The data collection department can utilize drones for monitoring traffic conditions. For example, drones can be flown over major intersections and busy roads to monitor traffic conditions in real time. Drones can also quickly arrive at the scene when congestion or accidents occur and collect detailed information. Furthermore, because drones can monitor traffic conditions over a wide area at once, it is possible to efficiently grasp the traffic situation across the entire city. As a result, utilizing drones improves the accuracy of traffic condition monitoring and enables a quicker response.

[0089] The analysis unit can utilize social media data in traffic volume analysis. For example, it can collect traffic information posted on social media and incorporate it into the analysis. Furthermore, it can grasp the occurrence of traffic accidents and congestion in real time from user posts on social media. In addition, it can predict traffic volume fluctuations caused by specific events based on event information on social media and incorporate this into the analysis. As a result, utilizing social media data improves the accuracy of traffic condition analysis and enables faster responses.

[0090] The management department can consider public transport schedules when adjusting traffic light timings. For example, by adjusting traffic light timings to match bus and train schedules, the operation of public transport can be made smoother. Furthermore, by adjusting traffic light timings based on public transport delay information, delays can be minimized. In addition, by referencing public transport schedules in real time and adjusting traffic light timings accordingly, traffic flow can be optimized. In this way, by considering public transport schedules, traffic flow can be made smoother and the convenience of public transport users can be improved.

[0091] The data collection unit can estimate the user's emotions and adjust the timing of camera data collection based on those emotions. For example, if the user is stressed, the frequency of camera data collection increases to respond quickly to changes in traffic conditions. If the user is relaxed, the frequency of camera data collection decreases to reduce the system load. If the user is in a hurry, the camera data collection interval is shortened to enhance real-time understanding of traffic conditions. This allows for a quick response to changes in traffic conditions by adjusting the timing of camera data collection according to the user's emotions.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that gets straight to the point. In this way, visibility is improved by adjusting the display method of the analysis results according to the user's emotions.

[0093] The management department can estimate user emotions and adjust traffic light timing based on those emotions. For example, if a user is stressed, the traffic light timing can be adjusted to smooth traffic flow. If a user is relaxed, the traffic light timing can be adjusted to optimize traffic flow. If a user is in a hurry, the traffic light timing can be adjusted to enable faster travel. In this way, traffic flow can be made smoother by adjusting traffic light timing according to user emotions.

[0094] The data collection unit can estimate the user's emotions and determine the priority of camera information to collect based on those emotions. For example, if the user is stressed, camera information from major intersections will be prioritized. If the user is relaxed, camera information from areas with less traffic will be prioritized. If the user is in a hurry, camera information from areas prone to traffic congestion will be prioritized. By prioritizing camera information according to the user's emotions, important information can be collected preferentially.

[0095] The analysis unit can estimate the user's emotions and prioritize the analysis results based on those emotions. For example, if the user is stressed, important analysis results will be displayed first. If the user is relaxed, detailed analysis results will be displayed. If the user is in a hurry, concise analysis results will be displayed first. In this way, by prioritizing analysis results according to the user's emotions, important information can be displayed preferentially.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The collection unit collects camera information. For example, it collects information from cameras installed at major intersections and busy roads, collects camera information in real time, and transmits it to the AI ​​agent. It also periodically collects camera information to monitor traffic conditions and converts it into a format that is easy to analyze. Step 2: The analysis unit analyzes the camera information collected by the collection unit to monitor, analyze, and predict traffic volume. For example, it uses camera information to understand the current traffic situation, predict future traffic volume, analyze traffic flow, and identify traffic bottlenecks. Step 3: The management unit integrates and manages the traffic signals in real time based on the analysis results obtained by the analysis unit. For example, it adjusts the timing of each traffic signal, adjusts the timing of traffic signals at major intersections, optimizes traffic flow, reduces congestion, and adjusts the timing of traffic signals to reduce environmental impact.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements described above, including the data collection unit, analysis unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the smart device 14 to collect information on major intersections and busy roads, and the control unit 46A collects the camera information in real time and transmits it to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and grasps the current traffic situation based on the camera information and predicts future traffic volume. The management unit is implemented in the specific processing unit 290 of the data processing unit 12, and adjusts the timing of traffic signals based on the analysis results to optimize traffic flow. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the smart glasses 214 to collect information on major intersections and busy roads, and the control unit 46A collects the camera information in real time and transmits it to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and grasps the current traffic situation based on the camera information and predicts future traffic volume. The management unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and adjusts the timing of traffic signals based on the analysis results to optimize traffic flow. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the headset terminal 314 to collect information on major intersections and busy roads, and the control unit 46A collects the camera information in real time and transmits it to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to grasp the current traffic situation based on the camera information and predict future traffic volume. The management unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to adjust the timing of traffic signals based on the analysis results and optimize the flow of traffic. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, and management unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the robot 414 to collect information on major intersections and busy roads, and the control unit 46A collects the camera information in real time and transmits it to the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and grasps the current traffic situation based on the camera information and predicts future traffic volume. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and adjusts the timing of traffic signals based on the analysis results to optimize traffic flow. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A collection unit that collects camera information, The camera information collected by the aforementioned collection unit is analyzed by the analysis unit, which monitors, analyzes, and predicts traffic volume. The system includes a management unit that integrates and manages traffic signals in real time based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Information is collected from cameras installed at major intersections and on busy roads. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on camera data, we can understand the current traffic situation and predict future traffic volume. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Adjusting the timing of each traffic light optimizes traffic flow. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Adjusting the timing of traffic lights will reduce traffic congestion. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Adjusting the timing of traffic lights reduces environmental impact. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of camera data collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system detects traffic accidents in real time and prioritizes collecting information when an accident occurs. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The camera automatically adjusts its collection range and resolution according to the weather and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of camera information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting camera data, the collection frequency is adjusted by predicting the times of day when traffic volume is high. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting camera data, prioritize monitoring areas with a high risk of traffic accidents. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing traffic volume, historical traffic data is used to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing traffic volume, consider the impact of specific events. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing traffic volume, weather data is used to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing traffic volume, we perform analyses to predict the risk of traffic accidents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, The system estimates the user's emotions and adjusts the timing of traffic lights based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, The timing of traffic lights is adjusted in real time according to fluctuations in traffic volume. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, Automatically adjusts the timing of traffic lights to accommodate specific traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, When adjusting the timing of traffic lights, adjustments are made to reduce the risk of traffic accidents. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, When adjusting the timing of traffic lights, the operating schedule of public transportation should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects camera information, The camera information collected by the aforementioned collection unit is analyzed by the analysis unit, which monitors, analyzes, and predicts traffic volume. The system includes a management unit that integrates and manages traffic signals in real time based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Information is collected from cameras installed at major intersections and on busy roads. The system according to feature 1.

3. The aforementioned analysis unit, Based on camera data, we can understand the current traffic situation and predict future traffic volume. The system according to feature 1.

4. The aforementioned management department, Adjusting the timing of each traffic light optimizes traffic flow. The system according to feature 1.

5. The aforementioned management department, Adjusting the timing of traffic lights will reduce traffic congestion. The system according to feature 1.

6. The aforementioned management department, Adjusting the timing of traffic lights reduces environmental impact. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of camera data collection based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is The system detects traffic accidents in real time and prioritizes collecting information when an accident occurs. The system according to feature 1.

9. The aforementioned collection unit is The camera automatically adjusts its collection range and resolution according to the weather and time of day. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of camera information to collect based on the estimated user emotions. The system according to feature 1.

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