system

Abstract

The system according to this embodiment aims to optimize traffic flow and alleviate congestion. [Solution] The system according to the embodiment comprises a vehicle agent, a traffic light agent, and an integrated agent. The vehicle agent communicates with vehicles to adjust speed, provide directions, and control traffic flow. The traffic light agent communicates with traffic lights to adjust signal timing and control intersections. The integrated agent manages the vehicle agent and the traffic light agent and performs overall optimization.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, optimizing traffic flow and eliminating congestion have not been fully achieved, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize traffic flow and eliminate congestion.

Means for Solving the Problems

[0007] The system according to this embodiment can optimize traffic flow and alleviate congestion. [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 48, 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) The traffic optimization system according to an embodiment of the present invention is a system that optimizes traffic and eliminates congestion using three AI agents. This traffic optimization system consists of three agents: a vehicle agent, a traffic light agent, and an integrated agent. First, the vehicle agent communicates with vehicles to adjust speed and provide directions, thereby controlling traffic flow. For example, if a particular road is congested, the vehicle agent guides vehicles to an alternative route, distributing traffic flow. It can also smooth the flow of traffic by adjusting the speed of vehicles. Next, the traffic light agent communicates with traffic lights to adjust signal timing and control traffic at intersections. For example, if a particular intersection is congested, the traffic light agent adjusts the signal timing to smooth the flow of traffic. The traffic light agent can also work in conjunction with the vehicle agent to optimize the flow of vehicles. Finally, the integrated agent oversees the vehicle agent and the traffic light agent, optimizing overall traffic. For example, it grasps the traffic situation for an entire area and issues instructions to the vehicle agent and the traffic light agent to optimize the flow of traffic. The integrated agent can also monitor traffic conditions in real time and make adjustments as needed. This can alleviate traffic congestion and reduce economic losses, environmental impacts, social impacts, and health impacts. For example, eliminating traffic congestion reduces wasted time, increased fuel costs, and increased logistics costs. It also reduces air pollution and noise, mitigating stress, accident risks, air pollution from exhaust fumes, and physical strain from long hours of driving. Thus, traffic optimization systems can alleviate traffic congestion and reduce economic losses, environmental impacts, social impacts, and health impacts.

[0029] The traffic optimization system according to this embodiment comprises a vehicle agent, a traffic light agent, and an integration agent. The vehicle agent communicates with vehicles to adjust speed and provide directions, thereby controlling traffic flow. For example, if a particular road is congested, the vehicle agent will guide vehicles to an alternative route. The vehicle agent can also adjust the speed of vehicles. For example, the vehicle agent can gradually adjust the speed of vehicles to smooth the flow of traffic. The traffic light agent communicates with traffic lights to adjust signal timing and control traffic at intersections. For example, if a particular intersection is congested, the traffic light agent will adjust the signal timing. The traffic light agent can also work in conjunction with the vehicle agent to optimize the flow of vehicles. For example, the traffic light agent adjusts the signal timing based on information from the vehicle agent to smooth the flow of traffic. The integration agent oversees the vehicle agent and the traffic light agent and optimizes overall traffic. For example, the integration agent grasps the traffic situation for an entire area and issues instructions to the vehicle agent and the traffic light agent. The integration agent can also monitor traffic conditions in real time and make adjustments as needed. For example, the integrated agent issues instructions to the vehicle agent and the traffic signal agent in response to changes in traffic conditions, optimizing the flow of traffic. As a result, the traffic optimization system according to this embodiment can optimize the flow of traffic and alleviate congestion.

[0030] Vehicle agents communicate with vehicles to adjust speed, provide directions, and control traffic flow. Specifically, vehicle agents acquire real-time data such as the vehicle's location, speed, and direction of travel via a communication module installed in the vehicle. This allows vehicle agents to guide vehicles to alternative routes if a particular road is congested. For example, a vehicle agent can analyze traffic congestion information, calculate the optimal detour route, and transmit it to the vehicle's navigation system. Vehicle agents can also adjust the vehicle's speed. For example, a vehicle agent can understand the traffic situation ahead and gradually adjust the vehicle's speed to prevent sudden braking and acceleration, thus smoothing the flow of traffic. Furthermore, vehicle agents can communicate with other vehicle agents to coordinate with surrounding vehicles. This allows vehicle agents to optimize overall traffic flow and suppress congestion. For example, a vehicle agent can consider the speed and position of the vehicle ahead and instruct following vehicles to adjust their speed and maintain an appropriate distance. Vehicle agents can also assist in giving priority to emergency vehicles. For example, if an emergency vehicle is approaching, the vehicle agent will instruct other vehicles to yield the right of way to facilitate the emergency vehicle's passage. This allows the vehicle agent to optimize traffic flow, alleviate congestion, and respond quickly to emergencies.

[0031] Traffic signal agents communicate with traffic signals to adjust signal timing and control traffic at intersections. Specifically, traffic signal agents communicate with the control devices of traffic signals installed at intersections and monitor the signal status in real time. This allows traffic signal agents to adjust signal timing when a particular intersection is congested. For example, traffic signal agents analyze traffic volume data at intersections and optimize the duration of green and red lights. Traffic signal agents can also work with vehicle agents to optimize vehicle flow. For example, traffic signal agents adjust signal timing based on information from vehicle agents to smooth traffic flow. Furthermore, traffic signal agents can also adjust pedestrian signal timing to ensure pedestrian safety. For example, traffic signal agents extend the duration of the green light for pedestrian signals, taking into account pedestrian traffic volume and speed. Traffic signal agents can also assist in prioritizing the passage of emergency vehicles. For example, if an emergency vehicle is approaching, traffic signal agents adjust the signals at the intersection to prioritize the passage of the emergency vehicle. This allows the traffic signal agent to efficiently control traffic at intersections, optimize traffic flow, and also ensure pedestrian safety and respond quickly to emergencies.

[0032] The integrated agent coordinates vehicle agents and traffic signal agents to optimize overall traffic. Specifically, the integrated agent understands the traffic conditions across a specific area and issues instructions to vehicle and traffic signal agents. For example, the integrated agent can collect and analyze traffic data across the entire area in real time to issue instructions for optimizing traffic flow. The integrated agent can also monitor traffic conditions in real time and make adjustments as needed. For example, the integrated agent can issue instructions to vehicle and traffic signal agents in response to changes in traffic conditions to optimize traffic flow. Furthermore, the integrated agent can utilize historical and forecast data to predict future traffic conditions and take preventative measures. For example, the integrated agent can predict increases in traffic volume during specific times or events, and adjust traffic signal timings in advance or guide vehicles to alternative routes. The integrated agent can also collaborate with other traffic management systems and infrastructure to share information. For example, the integrated agent can acquire information on public transport operations and road construction and use it to optimize traffic flow. This allows the integrated agent to efficiently manage overall traffic and alleviate congestion, as well as flexibly respond to future changes in traffic conditions.

[0033] Vehicle agents can guide vehicles to an alternative route if a particular road is congested. For example, a vehicle agent can guide a vehicle to an alternative route if a particular road is congested. Vehicle agents can guide vehicles to an alternative route based on the definition of congestion and the method of congestion detection. For example, a vehicle agent can detect congestion using traffic volume sensors or cameras and guide vehicles to an alternative route. Vehicle agents can also collect traffic information in real time and detect congestion. For example, a vehicle agent can detect congestion based on data from traffic information services and guide vehicles to an alternative route. This makes it possible to provide route guidance that avoids congestion.

[0034] Vehicle agents can adjust the speed of vehicles. For example, a vehicle agent can adjust the speed of a vehicle based on a speed range and timing of adjustment. For example, a vehicle agent can adjust the speed according to traffic conditions to smooth traffic flow. Furthermore, a vehicle agent can monitor vehicle speed in real time and adjust it as needed. For example, a vehicle agent can adjust the speed based on vehicle speed data to optimize traffic flow. This allows for smoother traffic flow by adjusting vehicle speed.

[0035] Traffic signal agents can adjust signal timing when a particular intersection is congested. For example, a traffic signal agent can adjust signal timing when a specific intersection is congested. The traffic signal agent can adjust signal timing based on the definition of congestion and the method of congestion detection. For example, a traffic signal agent can detect congestion using traffic volume sensors or cameras and adjust signal timing accordingly. Alternatively, a traffic signal agent can collect traffic information in real time and detect congestion. For example, a traffic signal agent can detect congestion based on data from traffic information services and adjust signal timing accordingly. This makes it possible to adjust signal timing to alleviate congestion at intersections.

[0036] Traffic signal agents can work in conjunction with vehicle agents to optimize traffic flow. For example, traffic signal agents can optimize traffic flow by adjusting signal timing based on information from vehicle agents. For instance, traffic signal agents can adjust signal timing based on traffic information from vehicle agents to smooth traffic flow. Furthermore, traffic signal agents can share information with vehicle agents in real time to optimize traffic flow. For example, traffic signal agents can adjust signal timing based on real-time information from vehicle agents to optimize traffic flow. In this way, by working in conjunction with vehicle agents, traffic flow can be optimized.

[0037] An integrated agent can understand traffic conditions across a specific area and issue instructions to vehicle agents and traffic signal agents. For example, an integrated agent can understand traffic conditions across a specific area and issue instructions to vehicle agents and traffic signal agents. The integrated agent can issue instructions to vehicle agents and traffic signal agents based on the scope and method of understanding traffic conditions. For example, an integrated agent can understand traffic conditions using traffic volume sensors and cameras and issue instructions to vehicle agents and traffic signal agents. Furthermore, an integrated agent can collect traffic information in real time and understand traffic conditions. For example, an integrated agent can understand traffic conditions based on data from traffic information services and issue instructions to vehicle agents and traffic signal agents. This allows for an understanding of traffic conditions across the entire area and the optimization of traffic flow by issuing appropriate instructions.

[0038] The integrated agent can monitor traffic conditions in real time and make adjustments as needed. For example, the integrated agent can monitor traffic conditions in real time and make adjustments as necessary. The integrated agent can monitor and adjust traffic conditions based on the target and method of monitoring. For example, the integrated agent can monitor traffic conditions using traffic volume sensors and cameras and make adjustments as needed. Furthermore, the integrated agent can collect traffic information in real time and monitor traffic conditions. For example, the integrated agent can monitor traffic conditions based on data from traffic information services and make adjustments as needed. This allows for the optimization of traffic flow by monitoring traffic conditions in real time and making appropriate adjustments.

[0039] The vehicle agent can monitor the vehicle's fuel consumption in real time and suggest the optimal speed. For example, if fuel consumption is high, the vehicle agent can reduce speed to improve fuel efficiency. If fuel consumption is low, the vehicle agent can maintain speed to continue efficient driving. The vehicle agent can also analyze fuel consumption data and suggest the optimal speed in real time. This enables optimal speed suggestions based on fuel consumption. Some or all of the above processing in the vehicle agent may be performed using AI, for example, or without AI. For example, the vehicle agent can input fuel consumption data into a generating AI and have the generating AI suggest the optimal speed.

[0040] The vehicle agent can adjust speed and provide route guidance while taking the vehicle's maintenance status into consideration. For example, if maintenance is required, the vehicle agent can reduce speed to encourage safe driving. If maintenance is complete, the vehicle agent can continue driving at the optimal speed. The vehicle agent can also suggest the optimal route based on the maintenance status. This enables speed adjustments and route guidance according to the maintenance status. Some or all of the above processes in the vehicle agent may be performed using AI, for example, or without AI. For example, the vehicle agent can input maintenance status data into a generating AI and have the generating AI perform speed adjustments and route guidance.

[0041] The vehicle agent can suggest the optimal speed and route, taking into account the type and weight of the vehicle's cargo. For example, if the vehicle is carrying heavy cargo, the agent may reduce speed to encourage safer driving. If the vehicle is carrying light cargo, the agent may continue driving at the optimal speed. The vehicle agent can also suggest the optimal route depending on the type of cargo. This enables the suggestion of the optimal speed and route according to the type and weight of the cargo. Some or all of the above processing in the vehicle agent may be performed using AI, for example, or not using AI. For example, the vehicle agent can input data on the type and weight of cargo into a generating AI and have the generating AI suggest the optimal speed and route.

[0042] The vehicle agent can analyze the vehicle's driving history and adjust the speed according to the driver's driving style. For example, if the driver frequently uses sudden braking, the vehicle agent can reduce the speed to encourage safer driving. If the driver drives smoothly, the vehicle agent can also continue driving at the optimal speed. The vehicle agent can analyze the driving history and adjust the speed according to the driving style. This enables speed adjustment based on driving history. Some or all of the above processing in the vehicle agent may be performed using AI, for example, or without AI. For example, the vehicle agent can input driving history data into a generating AI and have the generating AI perform speed adjustments according to the driving style.

[0043] The traffic signal agent can monitor traffic volume at an intersection in real time and set optimal signal timings. For example, if traffic volume is high, the traffic signal agent can shorten the waiting time for the signal. If traffic volume is low, the traffic signal agent can also extend the waiting time for the signal. The traffic signal agent can also set optimal signal timings based on real-time traffic data. This makes it possible to set signal timings according to real-time traffic volume. Some or all of the above processes in the traffic signal agent may be performed using AI, for example, or without AI. For example, the traffic signal agent can input real-time traffic data into a generating AI and have the generating AI set the optimal signal timings.

[0044] The traffic light agent can adjust the signal timing considering the flow of pedestrians. For example, if there are many pedestrians, the traffic light agent can shorten the waiting time for the signal. If there are few pedestrians, the traffic light agent can also extend the waiting time for the signal. The traffic light agent can also monitor the flow of pedestrians in real time and set the optimal signal timing. This makes it possible to adjust the signal timing according to the flow of pedestrians. Some or all of the above processes in the traffic light agent may be performed using AI, for example, or without AI. For example, the traffic light agent can input pedestrian flow data into a generating AI and have the generating AI perform the adjustment of the signal timing.

[0045] The traffic light agent can adjust signal timing considering the surrounding environment of the intersection. For example, the traffic light agent can shorten the waiting time for a signal near a school. It can also lengthen the waiting time for a signal near a hospital. The traffic light agent can also set the optimal signal timing considering the surrounding environment. This makes it possible to adjust signal timing according to the surrounding environment. Some or all of the above processing in the traffic light agent may be performed using AI, for example, or without AI. For example, the traffic light agent can input data on the surrounding environment into a generating AI and have the generating AI perform the adjustment of the signal timing.

[0046] The traffic light agent can adjust signal timings by taking weather information into consideration. For example, the traffic light agent can shorten the waiting time for signals during rainy weather. The traffic light agent can also maintain normal signal timings during sunny weather. The traffic light agent can also monitor weather information in real time and set optimal signal timings. This makes it possible to adjust signal timings according to weather information. Some or all of the above processes in the traffic light agent may be performed using AI, for example, or without AI. For example, the traffic light agent can input weather information data into a generating AI and have the generating AI perform the signal timing adjustments.

[0047] The integrated agent can collect traffic accident and construction information in real time and reflect it in the overall optimization. For example, the integrated agent can collect traffic accident information in real time and reflect it in the overall optimization. The integrated agent can also collect construction information in real time and reflect it in the overall optimization. The integrated agent can also propose the optimal route based on traffic accident and construction information. In this way, by collecting traffic accident and construction information in real time and reflecting it in the overall optimization, traffic flow can be optimized. Some or all of the above processing in the integrated agent may be performed using AI, for example, or without AI. For example, the integrated agent can input traffic accident and construction information data into a generating AI and have the generating AI execute the process of reflecting it in the overall optimization.

[0048] The integrated agent can perform overall optimization by taking into account the operating status of public transport. For example, the integrated agent can monitor the operating status of public transport in real time and reflect it in the overall optimization. The integrated agent can also propose the optimal route based on delay information of public transport. The integrated agent can also maintain the optimal traffic flow by taking into account the operating status of public transport. In this way, the overall traffic flow can be optimized by taking into account the operating status of public transport. Some or all of the above processes in the integrated agent may be performed using AI, for example, or without AI. For example, the integrated agent can input data on the operating status of public transport into a generating AI and have the generating AI perform the overall optimization process.

[0049] The integrated agent can perform overall optimization by taking local event information into consideration. For example, the integrated agent can collect local event information in real time and reflect it in the overall optimization. The integrated agent can also predict traffic volume during events and suggest the optimal route. The integrated agent can also maintain optimal traffic flow based on event information. In this way, the overall traffic flow can be optimized by taking local event information into consideration. Some or all of the above processes in the integrated agent may be performed using AI, for example, or not using AI. For example, the integrated agent can input local event information data into a generating AI and have the generating AI perform the overall optimization process.

[0050] The integrated agent can perform overall optimization to minimize environmental impact. For example, to minimize environmental impact, the integrated agent can monitor vehicle emissions in real time and suggest the optimal route. The integrated agent can also reduce the environmental impact of specific areas by distributing traffic volume. The integrated agent can also provide optimal transfer guidance to promote the use of public transport. This enables overall optimization to minimize environmental impact. Some or all of the above processes in the integrated agent may be performed using AI, for example, or not using AI. For example, the integrated agent can input environmental impact data into a generating AI and have the generating AI perform the overall optimization process.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The vehicle agent can monitor the driver's health and provide driving assistance tailored to their condition. For example, if the driver is feeling fatigued, the vehicle agent will guide them to the nearest rest stop to encourage them to take a break. If the driver complains of feeling unwell, the vehicle agent can also guide them to the nearest medical facility. Furthermore, if the driver is in good health, the vehicle agent can continue providing normal driving assistance. This enables driving assistance that is tailored to the driver's health condition.

[0053] Traffic light agents can detect the approach of emergency vehicles and adjust signal timing to allow them to pass smoothly. For example, if an ambulance is approaching, the traffic light agent will turn the signal in the direction the emergency vehicle is going green and the signals in other directions red. Similarly, if a fire truck is approaching, the signal timing can be adjusted to prioritize the passage of the emergency vehicle. Furthermore, if a police vehicle is pursuing a suspect, the traffic light agent can turn the signal in the direction the pursuing vehicle is going green to support a quick response. This enables signal timing adjustments that prioritize the passage of emergency vehicles.

[0054] The integrated agent can predict the occurrence of traffic accidents and adjust traffic flow based on the predicted accident risk. For example, if accidents frequently occur at a particular intersection, the integrated agent can adjust the signal timing at that intersection to reduce the accident risk. Furthermore, if accidents frequently occur during a specific time period, the integrated agent can reduce the accident risk by distributing traffic flow during that time. Additionally, if the accident risk increases during bad weather, the integrated agent can work with vehicle agents to adjust speeds and promote safer driving. This enables traffic flow adjustments tailored to the accident risk.

[0055] The vehicle agent can monitor the vehicle's energy consumption in real time and provide driving assistance to optimize energy efficiency. For example, if energy consumption is high, it can reduce speed to improve energy efficiency. If energy consumption is low, it can maintain speed to continue efficient driving. It can also analyze energy consumption data and suggest the optimal driving method. This enables optimal driving assistance tailored to energy consumption.

[0056] The traffic light agent can adjust signal timing considering the surrounding environment of the intersection. For example, it can shorten the waiting time at traffic lights near schools, and lengthen it near hospitals. It can also set the optimal signal timing considering the surrounding environment. This makes it possible to adjust signal timing according to the surrounding environment.

[0057] The integrated agent can optimize the overall system by taking into account the operating status of public transportation. For example, it can monitor the operating status of public transportation in real time and reflect this in the overall optimization. It can also suggest the optimal route based on public transportation delay information. It can also maintain an optimal traffic flow by taking the operating status of public transportation into consideration. In this way, the overall traffic flow can be optimized by taking the operating status of public transportation into consideration.

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

[0059] Step 1: The vehicle agent communicates with the vehicle to adjust speed, provide directions, and control traffic flow. For example, if a particular road is congested, it will guide the vehicle to an alternative route. The vehicle agent can also adjust the vehicle's speed, gradually slowing it down to smooth the flow of traffic. Step 2: The traffic light agent communicates with traffic lights to adjust signal timing and control traffic at intersections. For example, it adjusts signal timing if a particular intersection is congested. The traffic light agent can also work with vehicle agents to optimize vehicle flow. For example, it adjusts signal timing based on information from vehicle agents to smooth traffic flow. Step 3: The integrated agent coordinates the vehicle agent and traffic signal agent to optimize overall traffic. For example, it can understand the traffic situation for an entire area and issue instructions to the vehicle agent and traffic signal agent. The integrated agent can also monitor traffic conditions in real time and make adjustments as needed. For example, it can issue instructions to the vehicle agent and traffic signal agent in response to changes in traffic conditions to optimize traffic flow.

[0060] (Example of form 2) The traffic optimization system according to an embodiment of the present invention is a system that optimizes traffic and eliminates congestion using three AI agents. This traffic optimization system consists of three agents: a vehicle agent, a traffic light agent, and an integrated agent. First, the vehicle agent communicates with vehicles to adjust speed and provide directions, thereby controlling traffic flow. For example, if a particular road is congested, the vehicle agent guides vehicles to an alternative route, distributing traffic flow. It can also smooth the flow of traffic by adjusting the speed of vehicles. Next, the traffic light agent communicates with traffic lights to adjust signal timing and control traffic at intersections. For example, if a particular intersection is congested, the traffic light agent adjusts the signal timing to smooth the flow of traffic. The traffic light agent can also work in conjunction with the vehicle agent to optimize the flow of vehicles. Finally, the integrated agent oversees the vehicle agent and the traffic light agent, optimizing overall traffic. For example, it grasps the traffic situation for an entire area and issues instructions to the vehicle agent and the traffic light agent to optimize the flow of traffic. The integrated agent can also monitor traffic conditions in real time and make adjustments as needed. This can alleviate traffic congestion and reduce economic losses, environmental impacts, social impacts, and health impacts. For example, eliminating traffic congestion reduces wasted time, increased fuel costs, and increased logistics costs. It also reduces air pollution and noise, mitigating stress, accident risks, air pollution from exhaust fumes, and physical strain from long hours of driving. Thus, traffic optimization systems can alleviate traffic congestion and reduce economic losses, environmental impacts, social impacts, and health impacts.

[0061] The traffic optimization system according to this embodiment comprises a vehicle agent, a traffic light agent, and an integration agent. The vehicle agent communicates with vehicles to adjust speed and provide directions, thereby controlling traffic flow. For example, if a particular road is congested, the vehicle agent will guide vehicles to an alternative route. The vehicle agent can also adjust the speed of vehicles. For example, the vehicle agent can gradually adjust the speed of vehicles to smooth the flow of traffic. The traffic light agent communicates with traffic lights to adjust signal timing and control traffic at intersections. For example, if a particular intersection is congested, the traffic light agent will adjust the signal timing. The traffic light agent can also work in conjunction with the vehicle agent to optimize the flow of vehicles. For example, the traffic light agent adjusts the signal timing based on information from the vehicle agent to smooth the flow of traffic. The integration agent oversees the vehicle agent and the traffic light agent and optimizes overall traffic. For example, the integration agent grasps the traffic situation for an entire area and issues instructions to the vehicle agent and the traffic light agent. The integration agent can also monitor traffic conditions in real time and make adjustments as needed. For example, the integrated agent issues instructions to the vehicle agent and the traffic signal agent in response to changes in traffic conditions, optimizing the flow of traffic. As a result, the traffic optimization system according to this embodiment can optimize the flow of traffic and alleviate congestion.

[0062] Vehicle agents communicate with vehicles to adjust speed, provide directions, and control traffic flow. Specifically, vehicle agents acquire real-time data such as the vehicle's location, speed, and direction of travel via a communication module installed in the vehicle. This allows vehicle agents to guide vehicles to alternative routes if a particular road is congested. For example, a vehicle agent can analyze traffic congestion information, calculate the optimal detour route, and transmit it to the vehicle's navigation system. Vehicle agents can also adjust the vehicle's speed. For example, a vehicle agent can understand the traffic situation ahead and gradually adjust the vehicle's speed to prevent sudden braking and acceleration, thus smoothing the flow of traffic. Furthermore, vehicle agents can communicate with other vehicle agents to coordinate with surrounding vehicles. This allows vehicle agents to optimize overall traffic flow and suppress congestion. For example, a vehicle agent can consider the speed and position of the vehicle ahead and instruct following vehicles to adjust their speed and maintain an appropriate distance. Vehicle agents can also assist in giving priority to emergency vehicles. For example, if an emergency vehicle is approaching, the vehicle agent will instruct other vehicles to yield the right of way to facilitate the emergency vehicle's passage. This allows the vehicle agent to optimize traffic flow, alleviate congestion, and respond quickly to emergencies.

[0063] Traffic signal agents communicate with traffic signals to adjust signal timing and control traffic at intersections. Specifically, traffic signal agents communicate with the control devices of traffic signals installed at intersections and monitor the signal status in real time. This allows traffic signal agents to adjust signal timing when a particular intersection is congested. For example, traffic signal agents analyze traffic volume data at intersections and optimize the duration of green and red lights. Traffic signal agents can also work with vehicle agents to optimize vehicle flow. For example, traffic signal agents adjust signal timing based on information from vehicle agents to smooth traffic flow. Furthermore, traffic signal agents can also adjust pedestrian signal timing to ensure pedestrian safety. For example, traffic signal agents extend the duration of the green light for pedestrian signals, taking into account pedestrian traffic volume and speed. Traffic signal agents can also assist in prioritizing the passage of emergency vehicles. For example, if an emergency vehicle is approaching, traffic signal agents adjust the signals at the intersection to prioritize the passage of the emergency vehicle. This allows the traffic signal agent to efficiently control traffic at intersections, optimize traffic flow, and also ensure pedestrian safety and respond quickly to emergencies.

[0064] The integrated agent coordinates vehicle agents and traffic signal agents to optimize overall traffic. Specifically, the integrated agent understands the traffic conditions across a specific area and issues instructions to vehicle and traffic signal agents. For example, the integrated agent can collect and analyze traffic data across the entire area in real time to issue instructions for optimizing traffic flow. The integrated agent can also monitor traffic conditions in real time and make adjustments as needed. For example, the integrated agent can issue instructions to vehicle and traffic signal agents in response to changes in traffic conditions to optimize traffic flow. Furthermore, the integrated agent can utilize historical and forecast data to predict future traffic conditions and take preventative measures. For example, the integrated agent can predict increases in traffic volume during specific times or events, and adjust traffic signal timings in advance or guide vehicles to alternative routes. The integrated agent can also collaborate with other traffic management systems and infrastructure to share information. For example, the integrated agent can acquire information on public transport operations and road construction and use it to optimize traffic flow. This allows the integrated agent to efficiently manage overall traffic and alleviate congestion, as well as flexibly respond to future changes in traffic conditions.

[0065] Vehicle agents can guide vehicles to an alternative route if a particular road is congested. For example, a vehicle agent can guide a vehicle to an alternative route if a particular road is congested. Vehicle agents can guide vehicles to an alternative route based on the definition of congestion and the method of congestion detection. For example, a vehicle agent can detect congestion using traffic volume sensors or cameras and guide vehicles to an alternative route. Vehicle agents can also collect traffic information in real time and detect congestion. For example, a vehicle agent can detect congestion based on data from traffic information services and guide vehicles to an alternative route. This makes it possible to provide route guidance that avoids congestion.

[0066] Vehicle agents can adjust the speed of vehicles. For example, a vehicle agent can adjust the speed of a vehicle based on a speed range and timing of adjustment. For example, a vehicle agent can adjust the speed according to traffic conditions to smooth traffic flow. Furthermore, a vehicle agent can monitor vehicle speed in real time and adjust it as needed. For example, a vehicle agent can adjust the speed based on vehicle speed data to optimize traffic flow. This allows for smoother traffic flow by adjusting vehicle speed.

[0067] Traffic signal agents can adjust signal timing when a particular intersection is congested. For example, a traffic signal agent can adjust signal timing when a specific intersection is congested. The traffic signal agent can adjust signal timing based on the definition of congestion and the method of congestion detection. For example, a traffic signal agent can detect congestion using traffic volume sensors or cameras and adjust signal timing accordingly. Alternatively, a traffic signal agent can collect traffic information in real time and detect congestion. For example, a traffic signal agent can detect congestion based on data from traffic information services and adjust signal timing accordingly. This makes it possible to adjust signal timing to alleviate congestion at intersections.

[0068] Traffic signal agents can work in conjunction with vehicle agents to optimize traffic flow. For example, traffic signal agents can optimize traffic flow by adjusting signal timing based on information from vehicle agents. For instance, traffic signal agents can adjust signal timing based on traffic information from vehicle agents to smooth traffic flow. Furthermore, traffic signal agents can share information with vehicle agents in real time to optimize traffic flow. For example, traffic signal agents can adjust signal timing based on real-time information from vehicle agents to optimize traffic flow. In this way, by working in conjunction with vehicle agents, traffic flow can be optimized.

[0069] An integrated agent can understand traffic conditions across a specific area and issue instructions to vehicle agents and traffic signal agents. For example, an integrated agent can understand traffic conditions across a specific area and issue instructions to vehicle agents and traffic signal agents. The integrated agent can issue instructions to vehicle agents and traffic signal agents based on the scope and method of understanding traffic conditions. For example, an integrated agent can understand traffic conditions using traffic volume sensors and cameras and issue instructions to vehicle agents and traffic signal agents. Furthermore, an integrated agent can collect traffic information in real time and understand traffic conditions. For example, an integrated agent can understand traffic conditions based on data from traffic information services and issue instructions to vehicle agents and traffic signal agents. This allows for an understanding of traffic conditions across the entire area and the optimization of traffic flow by issuing appropriate instructions.

[0070] The integrated agent can monitor traffic conditions in real time and make adjustments as needed. For example, the integrated agent can monitor traffic conditions in real time and make adjustments as necessary. The integrated agent can monitor and adjust traffic conditions based on the target and method of monitoring. For example, the integrated agent can monitor traffic conditions using traffic volume sensors and cameras and make adjustments as needed. Furthermore, the integrated agent can collect traffic information in real time and monitor traffic conditions. For example, the integrated agent can monitor traffic conditions based on data from traffic information services and make adjustments as needed. This allows for the optimization of traffic flow by monitoring traffic conditions in real time and making appropriate adjustments.

[0071] The vehicle agent can estimate the user's emotions and adjust the timing of speed adjustments based on the estimated emotions. For example, if the user is stressed, the vehicle agent can gradually adjust the speed to provide a comfortable ride. If the user is relaxed, the vehicle agent can maintain a constant speed to ensure a smooth ride. If the user is in a hurry, the vehicle agent can quickly adjust the speed to reach the destination in the shortest possible time. This enables speed adjustments that are responsive to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 vehicle agent may be performed using AI or not. For example, the vehicle agent can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The vehicle agent can monitor the vehicle's fuel consumption in real time and suggest the optimal speed. For example, if fuel consumption is high, the vehicle agent can reduce speed to improve fuel efficiency. If fuel consumption is low, the vehicle agent can maintain speed to continue efficient driving. The vehicle agent can also analyze fuel consumption data and suggest the optimal speed in real time. This enables optimal speed suggestions based on fuel consumption. Some or all of the above processing in the vehicle agent may be performed using AI, for example, or without AI. For example, the vehicle agent can input fuel consumption data into a generating AI and have the generating AI suggest the optimal speed.

[0073] The vehicle agent can adjust speed and provide route guidance while taking the vehicle's maintenance status into consideration. For example, if maintenance is required, the vehicle agent can reduce speed to encourage safe driving. If maintenance is complete, the vehicle agent can continue driving at the optimal speed. The vehicle agent can also suggest the optimal route based on the maintenance status. This enables speed adjustments and route guidance according to the maintenance status. Some or all of the above processes in the vehicle agent may be performed using AI, for example, or without AI. For example, the vehicle agent can input maintenance status data into a generating AI and have the generating AI perform speed adjustments and route guidance.

[0074] The vehicle agent can estimate the user's emotions and determine route guidance priorities based on those emotions. For example, if the user is stressed, the vehicle agent will prioritize the most comfortable route. If the user is relaxed, the vehicle agent may also prioritize the shortest route. If the user is in a hurry, the vehicle agent may also prioritize the fastest route. This enables route guidance prioritization according to the user's emotions. 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 vehicle agent may be performed using AI or not. For example, the vehicle agent can input user emotion data into a generative AI and have the generative AI perform route guidance priority determination.

[0075] The vehicle agent can suggest the optimal speed and route, taking into account the type and weight of the vehicle's cargo. For example, if the vehicle is carrying heavy cargo, the agent may reduce speed to encourage safer driving. If the vehicle is carrying light cargo, the agent may continue driving at the optimal speed. The vehicle agent can also suggest the optimal route depending on the type of cargo. This enables the suggestion of the optimal speed and route according to the type and weight of the cargo. Some or all of the above processing in the vehicle agent may be performed using AI, for example, or not using AI. For example, the vehicle agent can input data on the type and weight of cargo into a generating AI and have the generating AI suggest the optimal speed and route.

[0076] The vehicle agent can analyze the vehicle's driving history and adjust the speed according to the driver's driving style. For example, if the driver frequently uses sudden braking, the vehicle agent can reduce the speed to encourage safer driving. If the driver drives smoothly, the vehicle agent can also continue driving at the optimal speed. The vehicle agent can analyze the driving history and adjust the speed according to the driving style. This enables speed adjustment based on driving history. Some or all of the above processing in the vehicle agent may be performed using AI, for example, or without AI. For example, the vehicle agent can input driving history data into a generating AI and have the generating AI perform speed adjustments according to the driving style.

[0077] The traffic light agent can estimate the user's emotions and adjust the signal timing based on the estimated emotions. For example, if the user is stressed, the traffic light agent can shorten the waiting time for the signal. If the user is relaxed, the traffic light agent can maintain the normal signal timing. If the user is in a hurry, the traffic light agent can minimize the waiting time for the signal. This allows for signal timing adjustments in accordance with 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 traffic light agent may be performed using AI or not using AI. For example, the traffic light agent can input user emotion data into a generative AI and have the generative AI perform the adjustment of the signal timing.

[0078] The traffic signal agent can monitor traffic volume at an intersection in real time and set optimal signal timings. For example, if traffic volume is high, the traffic signal agent can shorten the waiting time for the signal. If traffic volume is low, the traffic signal agent can also extend the waiting time for the signal. The traffic signal agent can also set optimal signal timings based on real-time traffic data. This makes it possible to set signal timings according to real-time traffic volume. Some or all of the above processes in the traffic signal agent may be performed using AI, for example, or without AI. For example, the traffic signal agent can input real-time traffic data into a generating AI and have the generating AI set the optimal signal timings.

[0079] The traffic light agent can adjust the signal timing considering the flow of pedestrians. For example, if there are many pedestrians, the traffic light agent can shorten the waiting time for the signal. If there are few pedestrians, the traffic light agent can also extend the waiting time for the signal. The traffic light agent can also monitor the flow of pedestrians in real time and set the optimal signal timing. This makes it possible to adjust the signal timing according to the flow of pedestrians. Some or all of the above processes in the traffic light agent may be performed using AI, for example, or without AI. For example, the traffic light agent can input pedestrian flow data into a generating AI and have the generating AI perform the adjustment of the signal timing.

[0080] The traffic light agent can estimate the user's emotions and adjust the way the traffic lights are displayed based on those emotions. For example, if the user is stressed, the traffic light agent can provide a highly visible display method. If the user is relaxed, the traffic light agent can maintain a normal display method. If the user is in a hurry, the traffic light agent can also display the traffic lights quickly. This allows for adjustment of the traffic light display method 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 traffic light agent may be performed using AI or not using AI. For example, the traffic light agent can input user emotion data into a generative AI and have the generative AI adjust the traffic light display method.

[0081] The traffic light agent can adjust signal timing considering the surrounding environment of the intersection. For example, the traffic light agent can shorten the waiting time for a signal near a school. It can also lengthen the waiting time for a signal near a hospital. The traffic light agent can also set the optimal signal timing considering the surrounding environment. This makes it possible to adjust signal timing according to the surrounding environment. Some or all of the above processing in the traffic light agent may be performed using AI, for example, or without AI. For example, the traffic light agent can input data on the surrounding environment into a generating AI and have the generating AI perform the adjustment of the signal timing.

[0082] The traffic light agent can adjust signal timings by taking weather information into consideration. For example, the traffic light agent can shorten the waiting time for signals during rainy weather. The traffic light agent can also maintain normal signal timings during sunny weather. The traffic light agent can also monitor weather information in real time and set optimal signal timings. This makes it possible to adjust signal timings according to weather information. Some or all of the above processes in the traffic light agent may be performed using AI, for example, or without AI. For example, the traffic light agent can input weather information data into a generating AI and have the generating AI perform the signal timing adjustments.

[0083] The integrated agent can estimate the user's emotions and adjust the overall optimization instructions based on the estimated emotions. For example, if the user is stressed, the integrated agent can quickly provide overall optimization instructions. If the user is relaxed, the integrated agent can also provide normal overall optimization instructions. If the user is in a hurry, the integrated agent can provide overall optimization instructions in the shortest possible time. This enables adjustment of overall optimization instructions 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 integrated agent may be performed using AI, for example, or not using AI. For example, the integrated agent can input user emotion data into a generative AI and have the generative AI perform the adjustment of overall optimization instructions.

[0084] The integrated agent can collect traffic accident and construction information in real time and reflect it in the overall optimization. For example, the integrated agent can collect traffic accident information in real time and reflect it in the overall optimization. The integrated agent can also collect construction information in real time and reflect it in the overall optimization. The integrated agent can also propose the optimal route based on traffic accident and construction information. In this way, by collecting traffic accident and construction information in real time and reflecting it in the overall optimization, traffic flow can be optimized. Some or all of the above processing in the integrated agent may be performed using AI, for example, or without AI. For example, the integrated agent can input traffic accident and construction information data into a generating AI and have the generating AI execute the process of reflecting it in the overall optimization.

[0085] The integrated agent can perform overall optimization by taking into account the operating status of public transport. For example, the integrated agent can monitor the operating status of public transport in real time and reflect it in the overall optimization. The integrated agent can also propose the optimal route based on delay information of public transport. The integrated agent can also maintain the optimal traffic flow by taking into account the operating status of public transport. In this way, the overall traffic flow can be optimized by taking into account the operating status of public transport. Some or all of the above processes in the integrated agent may be performed using AI, for example, or without AI. For example, the integrated agent can input data on the operating status of public transport into a generating AI and have the generating AI perform the overall optimization process.

[0086] The integrated agent can estimate the user's emotions and determine the overall optimization priorities based on the estimated emotions. For example, if the user is stressed, the integrated agent can quickly determine the overall optimization priorities. If the user is relaxed, the integrated agent can also determine the normal overall optimization priorities. If the user is in a hurry, the integrated agent can also determine the overall optimization priorities in the shortest possible time. This enables the determination of overall optimization priorities 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 integrated agent may be performed using AI, for example, or not using AI. For example, the integrated agent can input user emotion data into a generative AI and have the generative AI perform the determination of overall optimization priorities.

[0087] The integrated agent can perform overall optimization by taking local event information into consideration. For example, the integrated agent can collect local event information in real time and reflect it in the overall optimization. The integrated agent can also predict traffic volume during events and suggest the optimal route. The integrated agent can also maintain optimal traffic flow based on event information. In this way, the overall traffic flow can be optimized by taking local event information into consideration. Some or all of the above processes in the integrated agent may be performed using AI, for example, or not using AI. For example, the integrated agent can input local event information data into a generating AI and have the generating AI perform the overall optimization process.

[0088] The integrated agent can perform overall optimization to minimize environmental impact. For example, to minimize environmental impact, the integrated agent can monitor vehicle emissions in real time and suggest the optimal route. The integrated agent can also reduce the environmental impact of specific areas by distributing traffic volume. The integrated agent can also provide optimal transfer guidance to promote the use of public transport. This enables overall optimization to minimize environmental impact. Some or all of the above processes in the integrated agent may be performed using AI, for example, or not using AI. For example, the integrated agent can input environmental impact data into a generating AI and have the generating AI perform the overall optimization process.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The vehicle agent can monitor the driver's health and provide driving assistance tailored to their condition. For example, if the driver is feeling fatigued, the vehicle agent will guide them to the nearest rest stop to encourage them to take a break. If the driver complains of feeling unwell, the vehicle agent can also guide them to the nearest medical facility. Furthermore, if the driver is in good health, the vehicle agent can continue providing normal driving assistance. This enables driving assistance that is tailored to the driver's health condition.

[0091] Traffic light agents can detect the approach of emergency vehicles and adjust signal timing to allow them to pass smoothly. For example, if an ambulance is approaching, the traffic light agent will turn the signal in the direction the emergency vehicle is going green and the signals in other directions red. Similarly, if a fire truck is approaching, the signal timing can be adjusted to prioritize the passage of the emergency vehicle. Furthermore, if a police vehicle is pursuing a suspect, the traffic light agent can turn the signal in the direction the pursuing vehicle is going green to support a quick response. This enables signal timing adjustments that prioritize the passage of emergency vehicles.

[0092] The integrated agent can predict the occurrence of traffic accidents and adjust traffic flow based on the predicted accident risk. For example, if accidents frequently occur at a particular intersection, the integrated agent can adjust the signal timing at that intersection to reduce the accident risk. Furthermore, if accidents frequently occur during a specific time period, the integrated agent can reduce the accident risk by distributing traffic flow during that time. Additionally, if the accident risk increases during bad weather, the integrated agent can work with vehicle agents to adjust speeds and promote safer driving. This enables traffic flow adjustments tailored to the accident risk.

[0093] The vehicle agent can estimate the user's emotions and suggest music and entertainment based on those estimates. For example, if the user is stressed, it can suggest relaxing music. If the user is relaxed, it can suggest enjoyable music and entertainment. If the user is in a hurry, it can suggest music that helps them concentrate. This makes it possible to suggest music and entertainment that are tailored to the user's emotions.

[0094] The traffic light agent can estimate the user's emotions and adjust the traffic light color based on those emotions. For example, if the user is stressed, the traffic light color can be changed to a more visible color. If the user is relaxed, the normal traffic light color can be maintained. If the user is in a hurry, the traffic light color can be changed quickly, allowing for adjustments to the traffic light color in response to the user's emotions.

[0095] The integrated agent can estimate the user's emotions and adjust how traffic information is delivered based on those emotions. For example, if the user is stressed, it can provide concise and easy-to-understand traffic information. If the user is relaxed, it can provide more detailed traffic information. If the user is in a hurry, it can prioritize providing the most important traffic information. This allows for the delivery of traffic information to be tailored to the user's emotions.

[0096] The vehicle agent can monitor the vehicle's energy consumption in real time and provide driving assistance to optimize energy efficiency. For example, if energy consumption is high, it can reduce speed to improve energy efficiency. If energy consumption is low, it can maintain speed to continue efficient driving. It can also analyze energy consumption data and suggest the optimal driving method. This enables optimal driving assistance tailored to energy consumption.

[0097] The traffic light agent can adjust signal timing considering the surrounding environment of the intersection. For example, it can shorten the waiting time at traffic lights near schools, and lengthen it near hospitals. It can also set the optimal signal timing considering the surrounding environment. This makes it possible to adjust signal timing according to the surrounding environment.

[0098] The integrated agent can optimize the overall system by taking into account the operating status of public transportation. For example, it can monitor the operating status of public transportation in real time and reflect this in the overall optimization. It can also suggest the optimal route based on public transportation delay information. It can also maintain an optimal traffic flow by taking the operating status of public transportation into consideration. In this way, the overall traffic flow can be optimized by taking the operating status of public transportation into consideration.

[0099] The integrated agent can estimate the user's emotions and determine overall optimization priorities based on those emotions. For example, if the user is stressed, it can quickly determine overall optimization priorities. If the user is relaxed, it can determine normal overall optimization priorities. If the user is in a hurry, it can determine overall optimization priorities in the shortest possible time. This enables overall optimization prioritization to be determined in accordance with the user's emotions.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The vehicle agent communicates with the vehicle to adjust speed, provide directions, and control traffic flow. For example, if a particular road is congested, it will guide the vehicle to an alternative route. The vehicle agent can also adjust the vehicle's speed, gradually slowing it down to smooth the flow of traffic. Step 2: The traffic light agent communicates with traffic lights to adjust signal timing and control traffic at intersections. For example, it adjusts signal timing if a particular intersection is congested. The traffic light agent can also work with vehicle agents to optimize vehicle flow. For example, it adjusts signal timing based on information from vehicle agents to smooth traffic flow. Step 3: The integrated agent coordinates the vehicle agent and traffic signal agent to optimize overall traffic. For example, it can understand the traffic situation for an entire area and issue instructions to the vehicle agent and traffic signal agent. The integrated agent can also monitor traffic conditions in real time and make adjustments as needed. For example, it can issue instructions to the vehicle agent and traffic signal agent in response to changes in traffic conditions to optimize traffic flow.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the vehicle agent, traffic signal agent, and integrated agent, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the vehicle agent is implemented by the control unit 46A of the smart device 14 and communicates with vehicles to adjust speed and provide directions. The traffic signal agent is implemented by the specific processing unit 290 of the data processing device 12 and communicates with traffic signals to adjust signal timing. The integrated agent is implemented by the specific processing unit 290 of the data processing device 12 and coordinates the vehicle agent and traffic signal agent to optimize overall traffic. The correspondence between each part and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the vehicle agent, traffic signal agent, and integrated agent, is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the vehicle agent is implemented by the control unit 46A of the smart glasses 214 and communicates with vehicles to adjust speed and provide directions. The traffic signal agent is implemented by the specific processing unit 290 of the data processing device 12 and communicates with traffic signals to adjust signal timing. The integrated agent is implemented by the specific processing unit 290 of the data processing device 12 and coordinates the vehicle agent and traffic signal agent to optimize overall traffic. The correspondence between each part and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the vehicle agent, traffic signal agent, and integrated agent, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the vehicle agent is implemented by the control unit 46A of the headset terminal 314 and communicates with vehicles to adjust speed and provide directions. The traffic signal agent is implemented by the specific processing unit 290 of the data processing device 12 and communicates with traffic signals to adjust signal timing. The integrated agent is implemented by the specific processing unit 290 of the data processing device 12 and coordinates the vehicle agent and traffic signal agent to optimize overall traffic. The correspondence between each part and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the vehicle agent, traffic signal agent, and integration agent, is implemented in at least one of the robot 414 and the data processing device 12. For example, the vehicle agent is implemented by the control unit 46A of the robot 414 and communicates with vehicles to adjust speed and provide directions. The traffic signal agent is implemented by the specific processing unit 290 of the data processing device 12 and communicates with traffic signals to adjust signal timing. The integration agent is implemented by the specific processing unit 290 of the data processing device 12 and coordinates the vehicle agent and traffic signal agent to optimize overall traffic. The correspondence between each part and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A vehicle agent that communicates with the vehicle to adjust speed, provide directions, and control the flow rate, A traffic light agent communicates with traffic lights to adjust signal timing and control the intersection, The system comprises an integrated agent that manages the vehicle agent and the signal agent and performs overall optimization. A system characterized by the following features. (Note 2) The aforementioned vehicle agent, If a particular road is congested, guide the vehicle to an alternative route. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned vehicle agent, Adjust the vehicle speed The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned signal agent, If a particular intersection is congested, the timing of the traffic lights will be adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned signal agent, In cooperation with the aforementioned vehicle agent, the flow of vehicles is optimized. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned integration agent is The system grasps the traffic conditions across a specific area and issues instructions to the vehicle agent and the traffic signal agent. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned integration agent is We monitor traffic conditions in real time and make adjustments as needed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned vehicle agent, It estimates the user's emotions and adjusts the timing of speed adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned vehicle agent, It monitors the vehicle's fuel consumption in real time and suggests the optimal speed. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned vehicle agent, The vehicle's maintenance status is taken into consideration when adjusting speed and providing route guidance. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned vehicle agent, The system estimates the user's emotions and determines route guidance priorities based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned vehicle agent, We will suggest the optimal speed and route, taking into account the type and weight of the vehicle's cargo. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned vehicle agent, The system analyzes the vehicle's driving history and adjusts the speed according to the driver's driving style. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned signal agent, It estimates the user's emotions and adjusts the signal timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned signal agent, The system monitors traffic volume at intersections in real time and sets optimal signal timings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned signal agent, Adjust the traffic light timing to take pedestrian flow into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned signal agent, It estimates the user's emotions and adjusts how the signal is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned signal agent, The timing of traffic signals is adjusted considering the surrounding environment of the intersection. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned signal agent, We adjust the signal timing taking weather information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned integration agent is It estimates the user's emotions and adjusts the overall optimization instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned integration agent is We collect traffic accident and construction information in real time and use it to optimize the overall system. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned integration agent is We will optimize the overall system, taking into account the operating status of public transportation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned integration agent is It estimates user emotions and determines overall optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned integration agent is We will optimize the overall system by taking local event information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned integration agent is We will perform overall optimization to minimize environmental impact. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 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 vehicle agent that communicates with the vehicle to adjust speed, provide directions, and control the flow rate, A traffic light agent communicates with traffic lights to adjust signal timing and control the intersection, The system comprises an integrated agent that manages the vehicle agent and the signal agent and performs overall optimization. A system characterized by the following features.

2. The aforementioned vehicle agent, If a particular road is congested, guide the vehicle to an alternative route. The system according to feature 1.

3. The aforementioned vehicle agent, Adjust the vehicle speed The system according to feature 1.

4. The aforementioned signal agent, If a particular intersection is congested, the timing of the traffic lights will be adjusted. The system according to feature 1.

5. The aforementioned signal agent, In cooperation with the aforementioned vehicle agent, the flow of vehicles is optimized. The system according to feature 1.

6. The aforementioned integration agent is The system grasps the traffic conditions across a specific area and issues instructions to the vehicle agent and the traffic signal agent. The system according to feature 1.

7. The aforementioned integration agent is We monitor traffic conditions in real time and make adjustments as needed. The system according to feature 1.

8. The aforementioned vehicle agent, It estimates the user's emotions and adjusts the timing of speed adjustments based on those estimated emotions. The system according to feature 1.

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