system

Abstract

We provide the system. [Solution] A means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, A means for predicting traffic volume and traffic flow patterns using an artificial intelligence model in order to analyze received traffic information, A means for generating control instructions to the traffic control device based on prediction results and dynamically adjusting the timing of the signal device, A means of providing collected data and predictive information to the user's terminal via a communication device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Traffic congestion in urban areas is a factor causing economic losses and environmental pollution, and is also a serious problem that increases the risk of traffic accidents. Since conventional signal control systems switch signals according to preset timings, it is difficult to respond to real-time traffic situations, and there is a limit to efficiently managing the movements of vehicles and pedestrians at intersections. In addition, due to the lack of prediction ability, it is also impossible to flexibly respond to temporary congestion or traffic flows during event holding. Therefore, there is a need for a new system that optimizes the traffic flow in real time and makes the movements of vehicles and pedestrians smoother and safer.

Means for Solving the Problems

[0005] In this invention, traffic information is received from multiple monitoring devices installed to acquire traffic conditions, and based on this information, an artificial intelligence model is used to predict traffic volume and traffic flow patterns. Based on these prediction results, control instructions are generated for the traffic control device, and the timing of the signal devices is dynamically adjusted to optimize traffic flow. Furthermore, the collected data and predicted information are also provided to the user's terminal via a communication device, allowing for real-time sharing of traffic information. This makes it possible to effectively alleviate congestion at intersections and improve the flow of traffic in urban areas.

[0006] "Traffic conditions" refer to the state of things on the road, such as the flow of vehicles and pedestrians, the degree of congestion, and the speed of movement.

[0007] "Monitoring equipment" refers to devices such as cameras and sensors that are installed to collect traffic information.

[0008] "Traffic information" refers to data that indicates the flow of traffic, such as the number, location, and speed of vehicles and pedestrians, acquired by monitoring devices.

[0009] An "artificial intelligence model" refers to an algorithm that uses machine learning techniques to analyze traffic information and predict future traffic patterns.

[0010] "Traffic volume" refers to the number of vehicles or pedestrians passing through a particular section of road at a specific time.

[0011] "Flow patterns" refer to the characteristics and tendencies of vehicle and pedestrian movement depending on the time and situation.

[0012] A "traffic control device" refers to equipment used to dynamically change the display of traffic signals and signs.

[0013] A "traffic signal system" refers to traffic signals installed at intersections and other locations, and its function is to manage the flow of traffic by controlling the timing of these signals.

[0014] The "communication device" refers to the entire infrastructure, hardware, and software used for transmitting and receiving data.

[0015] The "user terminal" refers to devices such as smartphones and in-vehicle navigation systems used for receiving traffic information.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. 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).

[0023] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] The 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.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] To implement this invention, it is first necessary to install multiple monitoring devices in the city's traffic infrastructure and collect traffic information in real time through these devices. The monitoring devices include cameras and sensors installed at major intersections and major roads. These devices are used to understand traffic volume, speed, and congestion levels.

[0038] The server collects real-time traffic data from these monitoring devices and stores it in a database. This traffic data includes the current number, location, and speed of vehicles. The server also stores historical traffic datasets and uses them as training data to make future predictions.

[0039] The terminal receives collected real-time traffic data and analyzes it using an artificial intelligence model. Specifically, the terminal predicts traffic flow patterns and executes algorithms to estimate planned traffic volume. Based on this analysis, it calculates the optimal timing for switching traffic signals and generates instructions to smooth traffic flow.

[0040] The traffic signal system receives instructions from terminals and dynamically adjusts the display of traffic lights at intersections. This adjustment is based on predicted traffic patterns and can improve traffic flow, for example, by extending the green light time for a particular lane.

[0041] Users receive real-time traffic information transmitted from a server via their smartphones or in-car navigation devices. This information includes suggestions for the most efficient routes and congestion information, allowing users to optimize their travel paths based on this information.

[0042] As a concrete example, during the morning rush hour, a server detects an increase in traffic volume at a specific intersection, and a terminal analyzes this data to instruct the traffic signal system on when to switch signals. As a result, traffic flow becomes smoother, travel time is shortened, and congestion is alleviated. Furthermore, users are guided along optimized routes through the app, making their journey to their destination more efficient and comfortable.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects real-time traffic information from monitoring devices installed in the transportation infrastructure. This includes analyzing video data from traffic cameras to recognize the number and movement of vehicles using computer vision technology, and acquiring data on vehicle speed and traffic density from sensors.

[0046] Step 2:

[0047] The server stores the collected traffic data in a central database and manages it together with historical data. This creates a foundation for analyzing long-term traffic patterns and helps in future predictions.

[0048] Step 3:

[0049] The terminal receives real-time traffic data sent from the server and analyzes this data using its built-in artificial intelligence model. The terminal predicts traffic flow patterns from current traffic volume and speed and calculates the optimal timing for issuing instructions to traffic signaling devices.

[0050] Step 4:

[0051] The terminal creates control instructions for the signaling device based on the analysis results and transmits them to the signaling device via the interface. The signaling device executes these instructions and adjusts the timing of the signal changes at the intersection.

[0052] Step 5:

[0053] Users receive traffic information from a server via their smartphone or in-car terminal. They then check suggested routes and predicted traffic conditions within the application to optimize their travel plans.

[0054] Step 6:

[0055] Users provide feedback to the application about their travel experiences and selected route information, which the server then uses to improve the overall prediction accuracy of the system. This feedback loop improves the performance of the AI ​​model as more data is collected.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In modern urban areas, traffic congestion and signal control require appropriate information analysis and real-time signal adjustments to ensure smooth traffic flow. However, conventional systems have handled traffic data collection, prediction, and control separately, making efficient optimization of traffic flow difficult. Therefore, there is a need to analyze traffic conditions with high accuracy and optimize them rapidly.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for receiving environmental data from multiple monitoring devices installed to acquire traffic information, means for predicting traffic flow patterns using a generative AI model to analyze the received environmental data, and means for generating adjustment instructions to a control device based on the prediction results and dynamically optimizing the timing of signal devices. This enables optimal analysis and control of traffic information in real time.

[0061] A "monitoring device" is a device installed to acquire traffic information and collects environmental data such as traffic volume, speed, and congestion levels.

[0062] A "generative AI model" is a model that uses artificial intelligence technology to predict traffic flow patterns and traffic volume based on past and present environmental data.

[0063] "Environmental data" refers to data such as traffic volume, speed, and congestion levels obtained from monitoring devices to analyze traffic flow.

[0064] A "control device" is a device that receives instructions to adjust the timing and operation of signal devices based on prediction results.

[0065] "Communication equipment" refers to devices used to provide collected information and prediction results to the user's terminal.

[0066] "Feedback data" refers to information submitted by users based on actual traffic conditions and usage experiences, and is data that contributes to improving the accuracy of analysis.

[0067] To implement this invention, it is first necessary to install monitoring devices at urban intersections and major roads. These monitoring devices collect environmental data such as traffic volume, vehicle speed, and road congestion. A server receives the environmental data collected in real time from these monitoring devices and stores it in a database. The stored data is also used as a training dataset, including past traffic patterns.

[0068] The terminal analyzes traffic data received from the server using a generative AI model. Specifically, the generative AI model utilizes neural networks and machine learning algorithms to predict traffic flow patterns. Based on the predictive information obtained from the analysis, the terminal instructs the traffic signal system to make optimal timing adjustments. These timing adjustments are used to allow the traffic signal system to dynamically change signals at intersections.

[0069] Furthermore, users receive real-time traffic information transmitted from the server via communication devices on their smartphones or in-car devices. This information includes suggestions for the most efficient routes and information on congested sections, allowing users to optimize their routes and reduce travel time.

[0070] For example, if an increase in traffic is predicted at a certain intersection, the terminal instructs the traffic signal system to extend the green light, allowing vehicles to pass through the intersection smoothly and preventing congestion. It also assists users in reaching their destination comfortably by suggesting alternative routes.

[0071] As an example of a prompt to be input to the generating AI model, if it is in the format of "Please explain the algorithm for analyzing traffic flow during this morning's rush hour and calculating the optimal timing for operating traffic signals," the system will perform the optimal data processing according to the specified purpose.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server receives environmental data on traffic conditions from monitoring devices installed at various points in the city. Input includes traffic volume, vehicle speed, and congestion data collected by the monitoring devices. The server filters this data, extracts the necessary information, and stores it in a database. The output is structured real-time traffic data.

[0075] Step 2:

[0076] The terminal receives real-time traffic data transmitted from the server. The input is traffic data stored by the server. The terminal uses a generative AI model to analyze traffic flow patterns based on the input data. Specifically, it uses a neural network to analyze the data and predict traffic peaks and congested areas. The output is the predicted traffic pattern information.

[0077] Step 3:

[0078] The terminal generates instructions for the signaling device based on the analysis results. Its input is predicted traffic pattern information. Based on this information, it generates control signals to smooth traffic flow and transmits them to the signaling device via the network. Specific actions include extending the green light and adjusting the signal cycle. The output is dynamically generated signal control instructions.

[0079] Step 4:

[0080] The user receives real-time traffic information provided by the server via communication devices. The input includes optimal routes and congestion information transmitted from the server. The user uses this information to optimize their route and travel efficiently. Specifically, they follow the suggested route using an in-car navigation system or a smartphone app. The output is the optimized travel route.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] In modern cities, traffic congestion is a daily problem, leading to increased travel times and environmental burdens. Furthermore, with the increasing prevalence of autonomous vehicles, there is a need for technologies that utilize real-time traffic information to optimize routes and dynamically adjust traffic signal control. Additionally, conventional systems fail to adequately provide optimal route suggestions tailored to individual vehicles.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, and means for analyzing traffic patterns based on driving data from multiple mobile devices to support safe and efficient operation. This makes it possible to optimize traffic flow in real time, alleviate congestion, and suggest appropriate routes for autonomous vehicles.

[0086] "Traffic information" refers to data on traffic volume, speed, and vehicle flow collected from monitoring devices.

[0087] An "artificial intelligence model" is a machine learning algorithm used to predict traffic patterns and generate appropriate control instructions.

[0088] A "traffic signal system" is a device that controls traffic signals at intersections and manages the flow of traffic.

[0089] "User's device" refers to a device such as a smartphone or in-vehicle device that can receive and display traffic information.

[0090] "Driving data" refers to data that includes the location, speed, and route information of a mobile device.

[0091] "Means of supporting operations" refer to technical means for analyzing traffic information and proposing the optimal route for mobile devices.

[0092] To implement this invention, a traffic management system needs to be constructed. The system collects real-time traffic information by installing monitoring devices at major intersections and roads in cities. These monitoring devices include cameras and sensors, which collect data such as traffic volume, speed, and congestion levels. A server receives and stores the data collected from these monitoring devices. Furthermore, it uses artificial intelligence models based on this data to predict traffic flow patterns.

[0093] The server calculates the optimal timing for switching traffic signals based on predicted traffic information and dynamically adjusts the traffic light displays. For example, it can smooth traffic flow by extending the signal duration for specific lanes depending on the predicted traffic volume.

[0094] The terminal provides collected data and predictive information to the user's terminal, allowing the user to optimize their travel route based on this information. Examples of user terminals include smartphones and in-car devices, through which optimized routes and traffic information are provided.

[0095] This system utilizes a data analysis program built in Python, using Pandas for data preprocessing and TENSORFLOW® for running predictive models. A Web API is also used to receive real-time data.

[0096] As a concrete example, when a user uses their car for their morning commute, the system can automatically analyze current traffic information and suggest the optimal commute route. An example of a prompt message is "Suggest an optimized signal timing for the next intersection," and the system will calculate the best solution for signal control in response to this command.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server receives real-time traffic information from monitoring devices. The monitoring devices use cameras and sensors to acquire data such as traffic volume, vehicle speed, and vehicle flow, and transmit this data to the server. The input is raw data from the monitoring devices, which the server then retrieves.

[0100] Step 2:

[0101] The server preprocesses the received raw traffic data. This preprocessing is done using the Python Pandas library to remove noise and impute missing values. The output of this step is the cleaned traffic data.

[0102] Step 3:

[0103] The server uses the cleaned data to input a machine learning model, which is a generative AI model, to predict traffic flow patterns. This model is trained on historical traffic data and runs using TensorFlow. The output is the predicted traffic pattern.

[0104] Step 4:

[0105] The server calculates the optimal timing for switching traffic signals based on predicted traffic patterns. It dynamically adjusts the control instructions to the signaling devices to ensure smooth vehicle flow at specific intersections. The output is a specific control instruction for the signaling devices.

[0106] Step 5:

[0107] The terminal receives data and predictive information from the server and displays it on the user's terminal. The user's terminal can be a smartphone or an in-car navigation device, and it displays optimal travel routes and traffic information, suggesting a route optimized for the user. The output is visualized information provided to the user.

[0108] Step 6:

[0109] The user selects a travel route based on the information displayed on the terminal and drives according to traffic conditions. This enables safe and efficient travel. The output is the optimal route selected by the user.

[0110] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0111] This invention provides a system that achieves a higher level of traffic flow optimization by incorporating an emotion engine into urban traffic management systems. In this configuration, a monitoring device that acquires traffic conditions, a terminal that controls signal devices, and an emotion engine that recognizes user emotions work in close cooperation.

[0112] The server aggregates real-time traffic information collected through monitoring devices installed throughout the city. This includes data from traffic cameras and sensors, such as the number of vehicles, their speed, and the degree of congestion. This information is stored in a database and used as the basis for analysis.

[0113] The terminal receives this data and uses an artificial intelligence model to predict and analyze traffic flow. In addition, the terminal is equipped with an emotion engine that can collect and analyze emotional data from the user's device. The emotion engine detects the user's emotional state from their voice, facial expressions, or input feedback, and uses this to personalize traffic information.

[0114] Users receive real-time traffic information from a server via their smartphones or in-car devices. This includes personalized routes and traffic information adapted to the user's emotions. For example, if a user is stressed, the emotion engine recognizes this and suggests a different route that prioritizes comfort over the usual route. Or, for users in a hurry, it provides traffic guidance to reach their destination in the shortest possible time.

[0115] As a concrete example, during the morning rush hour, when the server detects a sudden surge in traffic at a specific intersection, the terminal analyzes the data and adjusts the timing of the traffic signals. Simultaneously, the emotion engine evaluates the user's current emotional state and sends the optimal route guidance based on the results to the user's smartphone. As a result, the user can create a travel plan that best suits their situation and emotions, which in turn helps to alleviate traffic congestion and improve traffic flow.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The server collects real-time traffic data from multiple monitoring devices installed in the transportation infrastructure. This collected data includes the number and speed of vehicles and road congestion, and it integrates the data that is periodically transmitted from each monitoring device.

[0119] Step 2:

[0120] The server stores the collected traffic data in a database. This storage process maintains data integrity and consistency while saving it in a format that is easy to use for subsequent analysis.

[0121] Step 3:

[0122] The terminal receives traffic data sent from the server and analyzes that data using an artificial intelligence model. The AI ​​model predicts traffic flow patterns and generates instructions to optimize the timing of signal switching.

[0123] Step 4:

[0124] The emotion engine installed in the device collects emotional data from the user's device. This emotional data is obtained through analysis of the user's voice and facial expressions, or from feedback entered directly.

[0125] Step 5:

[0126] The device uses emotional data analyzed by an emotion engine to generate personalized traffic information and travel routes tailored to the user. This provides personalized guidance so that users can relax and enjoy their journey.

[0127] Step 6:

[0128] The server transmits optimized signal control instructions to signaling devices, adjusting traffic flow in real time. Simultaneously, it provides users with the latest information by transmitting customized traffic data to their terminals.

[0129] Step 7:

[0130] Users act based on personalized traffic guidance received via smartphones or in-car devices. By following this guidance, users can travel to their destination efficiently while reducing stress and anxiety.

[0131] (Example 2)

[0132] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0133] In modern cities, traffic congestion and road overcrowding are major social challenges, and there is a need for efficient traffic management and comfortable travel for users. At the same time, traffic information systems often neglect to consider the stress and emotional state of individual users. Conventional systems manage traffic flow centrally, but lack the provision of services based on users' emotions. In response to this, there is a need to develop a traffic management system that can provide real-time information adapted to emotions.

[0134] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0135] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, and means for collecting user emotion data and analyzing it using an emotion analysis engine. This makes it possible to provide personalized traffic information that is adapted to the user's emotions.

[0136] A "monitoring device" is a physical device installed on roads and intersections to measure traffic conditions and acquire information in real time.

[0137] "Traffic information" refers to data about traffic conditions, specifically including the number of vehicles, speed, and degree of congestion.

[0138] An "artificial intelligence model" is an algorithm that learns patterns based on data collected in the past and uses them to predict traffic flow.

[0139] An "emotion analysis engine" is a technology for detecting and analyzing a user's emotional state, and it typically utilizes voice and facial expression data.

[0140] "Personalized traffic information" refers to traffic guidance and route information that is customized according to the emotional state and specific needs of individual users.

[0141] This invention aims to optimize real-time information in urban traffic management. Specific examples are shown below.

[0142] The server collects traffic information from monitoring devices installed on roads and intersections within the city. This includes cameras and sensors, and the data is used to measure the number and speed of vehicles, as well as the degree of congestion. The server stores this collected data in a database. The server continuously processes the data in real time, utilizing generative AI models to analyze traffic flow patterns. Frameworks such as TensorFlow and PyTorch are commonly used for this analysis.

[0143] The terminal receives analyzed traffic data transmitted from the server and also collects user emotion data. This emotion data is collected via smartphones and in-vehicle devices, and the user's emotional state can be analyzed using speech recognition and facial expression recognition software. This analysis incorporates an emotion analysis engine, which is used as additional information to find the optimal route for the user's travel.

[0144] Users can receive real-time traffic information adapted to their emotional state via their device. For example, if a user is experiencing high stress levels during the morning rush hour, the system will suggest a route that avoids congestion. It will also provide users in a hurry with the shortest possible route to their destination. This allows users to create travel plans that best suit their emotional state at that moment.

[0145] For example, during the morning rush hour, if traffic volume suddenly increases at a specific intersection, data is collected by a server, and the terminal controls traffic signals accordingly. Furthermore, based on the user's sentiment analysis, the smartphone provides optimal route guidance tailored to their emotions. An example of a prompt message might be, "If the user is feeling stressed during their morning commute, how would you suggest a comfortable route?"

[0146] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0147] Step 1:

[0148] The server collects traffic information from monitoring devices installed throughout the city. The input consists of raw data from traffic cameras and sensors, specifically information such as the number of vehicles, their speed, and congestion levels. This data is collected, organized, and stored in a database. This provides the foundational data needed to understand traffic conditions.

[0149] Step 2:

[0150] The server uses collected traffic information to generate an AI model that analyzes and predicts traffic flow. The input is data from the aforementioned traffic database. The AI ​​model analyzes this data and predicts future traffic flow patterns. As a result, it outputs congestion prediction data for each intersection and road section. This data enables control that anticipates future traffic conditions.

[0151] Step 3:

[0152] The terminal receives predictive data provided by the server and collects emotional data from the user's terminal. Inputs include predictive data from the server and audio / image data from the user's terminal. Using this data, an emotional analysis engine analyzes the user's emotional state. As a result, it outputs data indicating the user's emotional state. This allows for the preparation of personalized traffic guidance for each individual user.

[0153] Step 4:

[0154] The device integrates emotional state data and traffic prediction data to generate personalized traffic information. The input consists of emotional state data and traffic prediction data. Using this data, for example, it suggests a comfortable route for a stressed user and the shortest route for a user in a hurry. This enables adaptive and user-centered traffic guidance.

[0155] Step 5:

[0156] Users receive personalized traffic information from their devices and use it to guide their actual travel. Feedback is sent back to the device from the user. The input is personalized traffic information from the device. Based on user behavior and feedback, the system collects data to improve the accuracy of future directions. This improves the overall accuracy of the system and user satisfaction.

[0157] (Application Example 2)

[0158] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0159] In modern urban areas, traffic congestion is a daily occurrence, hindering efficient travel. Furthermore, existing traffic management systems, while considering the physical factors of traffic flow, fail to provide personalized traffic guidance that takes into account the emotional state of users. As a result, users often travel while experiencing emotional stress, further worsening overall traffic efficiency. The objective of this invention is to alleviate traffic congestion while providing traffic guidance that takes into account the emotional state of users, thereby achieving comfortable and efficient travel.

[0160] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0161] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, means for generating control instructions to traffic control devices based on the prediction results and dynamically adjusting the timing of signal devices, and means for acquiring and analyzing user emotional data and personalizing traffic guidance based on emotional state. This enables dynamic optimization of traffic flow while providing personalized traffic guidance based on the user's emotional state.

[0162] "Traffic conditions" refers to all information indicating the number of vehicles, their speed, the degree of congestion, etc., within a city.

[0163] A "monitoring device" is a device installed to acquire traffic information, such as a traffic camera or sensor.

[0164] "Means for analyzing traffic information" refers to processes and devices used to process received traffic data and understand traffic conditions based on the results.

[0165] An "artificial intelligence model" is a program that runs machine learning and predictive algorithms used to analyze data and predict traffic flow and traffic patterns.

[0166] "Traffic control devices" are equipment used to control the flow of traffic, such as traffic lights and electronic billboards.

[0167] A "control instruction" is a command sent to a traffic control device that adjusts the timing of signals and other traffic management parameters.

[0168] "Means of providing information via communication devices" refers to methods or devices for transmitting information to a user's terminal via a network.

[0169] "Emotional data" refers to data acquired to measure and analyze the emotional state of users.

[0170] An "emotion recognition engine" is a technology that detects and analyzes a user's emotional state from their facial expressions, voice, and input information.

[0171] "Emotionally-based traffic guidance" refers to traffic information and route guidance provided in a way that is adapted to the user's current emotions.

[0172] In this invention, multiple monitoring devices are installed on a server to highly optimize urban traffic management. These monitoring devices include traffic cameras and sensors to acquire real-time traffic information. The server aggregates the traffic information and uses an artificial intelligence model to predict traffic volume and traffic flow patterns. Based on this prediction, control instructions are generated for traffic control devices, and the timing of traffic signals is dynamically adjusted.

[0173] The server is equipped with an emotion recognition engine to acquire and analyze users' emotional data. Users can receive real-time traffic information and personalized traffic guidance based on their emotional state using their smartphones or in-car devices. The emotion recognition engine acquires emotional data from users' voices and facial expressions through speech recognition and image recognition technologies. This data is used in traffic guidance to provide optimal routes and information that match the user's current emotions.

[0174] Specifically, if a user is feeling stressed, the emotion recognition engine will detect this and provide route guidance that prioritizes comfort, such as a route with scenic views. Furthermore, if the system determines that the user is in a hurry, it will suggest a route that will get them to their destination in the shortest possible time. For example, a user who wants to enjoy a relaxing drive with their family on a Sunday afternoon will be offered a route that not only avoids congestion but also allows them to enjoy the scenery. This results in traffic guidance tailored to the user's emotional state, ultimately reducing traffic congestion and improving the overall comfort of travel.

[0175] An example of a prompt message is: "Based on the user's emotional state, suggest the best route from the current location to the destination. If you are relaxed, choose a scenic route; if you are in a hurry, choose the shortest route."

[0176] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0177] Step 1:

[0178] The server acquires real-time traffic information from multiple monitoring devices. Input is raw data from the monitoring devices, while output is the number of vehicles, their speed, and congestion levels collected by traffic cameras and sensors. The data is collected by a program and processed into a standardized format.

[0179] Step 2:

[0180] The server inputs this traffic information into an artificial intelligence model to predict traffic volume and traffic flow patterns. The input here is processed traffic information, and the output is predicted data for future traffic volume and congestion locations. This process utilizes machine learning algorithms, which compare current data with historical data to make predictions.

[0181] Step 3:

[0182] The terminal generates control instructions for the traffic control system based on the prediction results. The input is prediction data, and the output is a control signal that adjusts the timing of the signaling device. The terminal sends commands to the signaling device, dynamically adjusting the timing of the signals to optimize traffic flow.

[0183] Step 4:

[0184] The server collects voice and facial expression data from smartphones and in-car devices via an emotion recognition engine to acquire user emotional data. The input is the user's real-time voice and image data, and the output is analyzed emotional state data. Here, emotions are determined using voice recognition and image recognition technologies.

[0185] Step 5:

[0186] The device generates traffic guidance tailored to the user's emotional state based on analyzed emotional data. Input is emotional state data and current traffic information, and output is personalized route guidance. Based on a generating AI model, it selects routes and information that match the user's preferences.

[0187] Step 6:

[0188] The user receives emotion-based traffic guidance from a server via their device and travels along the optimal route. The input is route guidance from the device, and the output is the user's travel experience. Specifically, by following the route guidance instructions, a stress-free journey is achieved. This process makes it possible to smoothly meet the user's transportation needs according to their circumstances.

[0189] 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.

[0190] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0191] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0192] [Second Embodiment]

[0193] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0194] 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.

[0195] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0196] 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.

[0197] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0198] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0199] 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.

[0200] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0201] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0202] The 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.

[0203] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0204] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0205] To implement this invention, it is first necessary to install multiple monitoring devices in the city's traffic infrastructure and collect traffic information in real time through these devices. The monitoring devices include cameras and sensors installed at major intersections and major roads. These devices are used to understand traffic volume, speed, and congestion levels.

[0206] The server collects real-time traffic data from these monitoring devices and stores it in a database. This traffic data includes the current number, location, and speed of vehicles. The server also stores historical traffic datasets and uses them as training data to make future predictions.

[0207] The terminal receives collected real-time traffic data and analyzes it using an artificial intelligence model. Specifically, the terminal predicts traffic flow patterns and executes algorithms to estimate planned traffic volume. Based on this analysis, it calculates the optimal timing for switching traffic signals and generates instructions to smooth traffic flow.

[0208] The traffic signal system receives instructions from terminals and dynamically adjusts the display of traffic lights at intersections. This adjustment is based on predicted traffic patterns and can improve traffic flow, for example, by extending the green light time for a particular lane.

[0209] Users receive real-time traffic information transmitted from a server via their smartphones or in-car navigation devices. This information includes suggestions for the most efficient routes and congestion information, allowing users to optimize their travel paths based on this information.

[0210] As a concrete example, during the morning rush hour, a server detects an increase in traffic volume at a specific intersection, and a terminal analyzes this data to instruct the traffic signal system on when to switch signals. As a result, traffic flow becomes smoother, travel time is shortened, and congestion is alleviated. Furthermore, users are guided along optimized routes through the app, making their journey to their destination more efficient and comfortable.

[0211] The following describes the processing flow.

[0212] Step 1:

[0213] The server collects real-time traffic information from monitoring devices installed in the transportation infrastructure. This includes analyzing video data from traffic cameras to recognize the number and movement of vehicles using computer vision technology, and acquiring data on vehicle speed and traffic density from sensors.

[0214] Step 2:

[0215] The server stores the collected traffic data in a central database and manages it together with historical data. This creates a foundation for analyzing long-term traffic patterns and helps in future predictions.

[0216] Step 3:

[0217] The terminal receives real-time traffic data sent from the server and analyzes this data using its built-in artificial intelligence model. The terminal predicts traffic flow patterns from current traffic volume and speed and calculates the optimal timing for issuing instructions to traffic signaling devices.

[0218] Step 4:

[0219] The terminal creates control instructions for the signaling device based on the analysis results and transmits them to the signaling device via the interface. The signaling device executes these instructions and adjusts the timing of the signal changes at the intersection.

[0220] Step 5:

[0221] Users receive traffic information from a server via their smartphone or in-car terminal. They then check suggested routes and predicted traffic conditions within the application to optimize their travel plans.

[0222] Step 6:

[0223] Users provide feedback to the application about their travel experiences and selected route information, which the server then uses to improve the overall prediction accuracy of the system. This feedback loop improves the performance of the AI ​​model as more data is collected.

[0224] (Example 1)

[0225] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0226] In modern urban areas, traffic congestion and signal control require appropriate information analysis and real-time signal adjustments to ensure smooth traffic flow. However, conventional systems have handled traffic data collection, prediction, and control separately, making efficient optimization of traffic flow difficult. Therefore, there is a need to analyze traffic conditions with high accuracy and optimize them rapidly.

[0227] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0228] In this invention, the server includes means for receiving environmental data from multiple monitoring devices installed to acquire traffic information, means for predicting traffic flow patterns using a generative AI model to analyze the received environmental data, and means for generating adjustment instructions to a control device based on the prediction results and dynamically optimizing the timing of signal devices. This enables optimal analysis and control of traffic information in real time.

[0229] A "monitoring device" is a device installed to acquire traffic information and collects environmental data such as traffic volume, speed, and congestion levels.

[0230] A "generative AI model" is a model that uses artificial intelligence technology to predict traffic flow patterns and traffic volume based on past and present environmental data.

[0231] "Environmental data" refers to data such as traffic volume, speed, and congestion levels obtained from monitoring devices to analyze traffic flow.

[0232] A "control device" is a device that receives instructions to adjust the timing and operation of signal devices based on prediction results.

[0233] "Communication equipment" refers to devices used to provide collected information and prediction results to the user's terminal.

[0234] "Feedback data" refers to information submitted by users based on actual traffic conditions and usage experiences, and is data that contributes to improving the accuracy of analysis.

[0235] To implement this invention, it is first necessary to install monitoring devices at urban intersections and major roads. These monitoring devices collect environmental data such as traffic volume, vehicle speed, and road congestion. A server receives the environmental data collected in real time from these monitoring devices and stores it in a database. The stored data is also used as a training dataset, including past traffic patterns.

[0236] The terminal analyzes traffic data received from the server using a generative AI model. Specifically, the generative AI model utilizes neural networks and machine learning algorithms to predict traffic flow patterns. Based on the predictive information obtained from the analysis, the terminal instructs the traffic signal system to make optimal timing adjustments. These timing adjustments are used to allow the traffic signal system to dynamically change signals at intersections.

[0237] Furthermore, users receive real-time traffic information transmitted from the server via communication devices on their smartphones or in-car devices. This information includes suggestions for the most efficient routes and information on congested sections, allowing users to optimize their routes and reduce travel time.

[0238] For example, if an increase in traffic is predicted at a certain intersection, the terminal instructs the traffic signal system to extend the green light, allowing vehicles to pass through the intersection smoothly and preventing congestion. It also assists users in reaching their destination comfortably by suggesting alternative routes.

[0239] As an example of a prompt to be input to the generating AI model, if it is in the format of "Please explain the algorithm for analyzing traffic flow during this morning's rush hour and calculating the optimal timing for operating traffic signals," the system will perform the optimal data processing according to the specified purpose.

[0240] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0241] Step 1:

[0242] The server receives environmental data on traffic conditions from monitoring devices installed at various points in the city. Input includes traffic volume, vehicle speed, and congestion data collected by the monitoring devices. The server filters this data, extracts the necessary information, and stores it in a database. The output is structured real-time traffic data.

[0243] Step 2:

[0244] The terminal receives real-time traffic data transmitted from the server. The input is traffic data stored by the server. The terminal uses a generative AI model to analyze traffic flow patterns based on the input data. Specifically, it uses a neural network to analyze the data and predict traffic peaks and congested areas. The output is the predicted traffic pattern information.

[0245] Step 3:

[0246] The terminal generates instructions for the signaling device based on the analysis results. Its input is predicted traffic pattern information. Based on this information, it generates control signals to smooth traffic flow and transmits them to the signaling device via the network. Specific actions include extending the green light and adjusting the signal cycle. The output is dynamically generated signal control instructions.

[0247] Step 4:

[0248] The user receives real-time traffic information provided by the server via communication devices. The input includes optimal routes and congestion information transmitted from the server. The user uses this information to optimize their route and travel efficiently. Specifically, they follow the suggested route using an in-car navigation system or a smartphone app. The output is the optimized travel route.

[0249] (Application Example 1)

[0250] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0251] In modern cities, traffic congestion is a daily problem, leading to increased travel times and environmental burdens. Furthermore, with the increasing prevalence of autonomous vehicles, there is a need for technologies that utilize real-time traffic information to optimize routes and dynamically adjust traffic signal control. Additionally, conventional systems fail to adequately provide optimal route suggestions tailored to individual vehicles.

[0252] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0253] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, and means for analyzing traffic patterns based on driving data from multiple mobile devices to support safe and efficient operation. This makes it possible to optimize traffic flow in real time, alleviate congestion, and suggest appropriate routes for autonomous vehicles.

[0254] "Traffic information" refers to data on traffic volume, speed, and vehicle flow collected from monitoring devices.

[0255] An "artificial intelligence model" is a machine learning algorithm used to predict traffic patterns and generate appropriate control instructions.

[0256] A "traffic signal system" is a device that controls traffic signals at intersections and manages the flow of traffic.

[0257] "User's device" refers to a device such as a smartphone or in-vehicle device that can receive and display traffic information.

[0258] "Driving data" refers to data that includes the location, speed, and route information of a mobile device.

[0259] "Means of supporting operations" refer to technical means for analyzing traffic information and proposing the optimal route for mobile devices.

[0260] To implement this invention, a traffic management system needs to be constructed. The system collects real-time traffic information by installing monitoring devices at major intersections and roads in cities. These monitoring devices include cameras and sensors, which collect data such as traffic volume, speed, and congestion levels. A server receives and stores the data collected from these monitoring devices. Furthermore, it uses artificial intelligence models based on this data to predict traffic flow patterns.

[0261] The server calculates the optimal timing for switching traffic signals based on predicted traffic information and dynamically adjusts the traffic light displays. For example, it can smooth traffic flow by extending the signal duration for specific lanes depending on the predicted traffic volume.

[0262] The terminal provides collected data and predictive information to the user's terminal, allowing the user to optimize their travel route based on this information. Examples of user terminals include smartphones and in-car devices, through which optimized routes and traffic information are provided.

[0263] This system utilizes a data analysis program built in Python, using Pandas for data preprocessing and TensorFlow for running predictive models. A Web API is also used to receive real-time data.

[0264] As a concrete example, when a user uses their car for their morning commute, the system can automatically analyze current traffic information and suggest the optimal commute route. An example of a prompt message is "Suggest an optimized signal timing for the next intersection," and the system will calculate the best solution for signal control in response to this command.

[0265] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0266] Step 1:

[0267] The server receives real-time traffic information from monitoring devices. The monitoring devices use cameras and sensors to acquire data such as traffic volume, vehicle speed, and vehicle flow, and transmit this data to the server. The input is raw data from the monitoring devices, which the server then retrieves.

[0268] Step 2:

[0269] The server preprocesses the received raw traffic data. This preprocessing is done using the Python Pandas library to remove noise and impute missing values. The output of this step is the cleaned traffic data.

[0270] Step 3:

[0271] The server uses the cleaned data to input a machine learning model, which is a generative AI model, to predict traffic flow patterns. This model is trained on historical traffic data and runs using TensorFlow. The output is the predicted traffic pattern.

[0272] Step 4:

[0273] The server calculates the optimal timing for switching traffic signals based on predicted traffic patterns. It dynamically adjusts the control instructions to the signaling devices to ensure smooth vehicle flow at specific intersections. The output is a specific control instruction for the signaling devices.

[0274] Step 5:

[0275] The terminal receives data and predictive information from the server and displays it on the user's terminal. The user's terminal can be a smartphone or an in-car navigation device, and it displays optimal travel routes and traffic information, suggesting a route optimized for the user. The output is visualized information provided to the user.

[0276] Step 6:

[0277] The user selects a travel route based on the information displayed on the terminal and drives according to traffic conditions. This enables safe and efficient travel. The output is the optimal route selected by the user.

[0278] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0279] This invention provides a system that achieves a higher level of traffic flow optimization by incorporating an emotion engine into urban traffic management systems. In this configuration, a monitoring device that acquires traffic conditions, a terminal that controls signal devices, and an emotion engine that recognizes user emotions work in close cooperation.

[0280] The server aggregates real-time traffic information collected through monitoring devices installed throughout the city. This includes data from traffic cameras and sensors, such as the number of vehicles, their speed, and the degree of congestion. This information is stored in a database and used as the basis for analysis.

[0281] The terminal receives this data and uses an artificial intelligence model to predict and analyze traffic flow. In addition, the terminal is equipped with an emotion engine that can collect and analyze emotional data from the user's device. The emotion engine detects the user's emotional state from their voice, facial expressions, or input feedback, and uses this to personalize traffic information.

[0282] Users receive real-time traffic information from a server via their smartphones or in-car devices. This includes personalized routes and traffic information adapted to the user's emotions. For example, if a user is stressed, the emotion engine recognizes this and suggests a different route that prioritizes comfort over the usual route. Or, for users in a hurry, it provides traffic guidance to reach their destination in the shortest possible time.

[0283] As a specific example, in the morning rush hour, when the server detects a sudden increase in traffic volume at a specific intersection, the terminal analyzes the data and adjusts the timing of the signal device. At the same time, the emotion engine evaluates the user's current emotional state and sends the optimal route guidance based on the result to the user's smartphone. As a result, the user can make a movement plan that best suits their situation and emotions, and overall, traffic congestion is alleviated and traffic is smoothed out.

[0284] The following describes the processing flow.

[0285] Step 1:

[0286] The server collects real-time traffic data from a plurality of monitoring devices installed in the traffic infrastructure. This collected data includes the number and speed of vehicles and the congestion status of the road, and integrates the data periodically transmitted from each monitoring device.

[0287] Step 2:

[0288] The server stores the collected traffic data in a database. In this storage process, while maintaining the integrity and consistency of the data, it is stored in a form that is easy to use for subsequent analysis.

[0289] Step 3:

[0290] The terminal receives the traffic data sent from the server and analyzes the data using an artificial intelligence model. The artificial intelligence model predicts the traffic flow pattern and generates an instruction to optimize the switching timing of the signal device.

[0291] Step 4:

[0292] The emotion engine installed on the terminal collects emotion data from the user's terminal. This emotion data is obtained from the analysis of the user's voice and expression or directly input feedback.

[0293] Step 5:

[0294] The device uses emotional data analyzed by an emotion engine to generate personalized traffic information and travel routes tailored to the user. This provides personalized guidance so that users can relax and enjoy their journey.

[0295] Step 6:

[0296] The server transmits optimized signal control instructions to signaling devices, adjusting traffic flow in real time. Simultaneously, it provides users with the latest information by transmitting customized traffic data to their terminals.

[0297] Step 7:

[0298] Users act based on personalized traffic guidance received via smartphones or in-car devices. By following this guidance, users can travel to their destination efficiently while reducing stress and anxiety.

[0299] (Example 2)

[0300] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0301] In modern cities, traffic congestion and road overcrowding are major social challenges, and there is a need for efficient traffic management and comfortable travel for users. At the same time, traffic information systems often neglect to consider the stress and emotional state of individual users. Conventional systems manage traffic flow centrally, but lack the provision of services based on users' emotions. In response to this, there is a need to develop a traffic management system that can provide real-time information adapted to emotions.

[0302] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0303] In this invention, the server includes means for receiving traffic information from a plurality of monitoring devices installed to obtain traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model in order to analyze the received traffic information, and means for collecting the user's emotional data and analyzing it using an emotion analysis engine. Thereby, it becomes possible to provide personalized traffic information adapted to the emotions of users.

[0304] A "monitoring device" is a physical device installed on roads or intersections to measure traffic conditions and obtain information in real time.

[0305] "Traffic information" is data related to traffic conditions, specifically including the number of vehicles, speed, and degree of congestion.

[0306] An "artificial intelligence model" is an algorithm that learns patterns based on data collected in the past and predicts traffic flow.

[0307] An "emotion analysis engine" is a technology for detecting and analyzing the emotional state of a user, and it is common to use data such as voice and facial expressions.

[0308] "Personalized traffic information" is traffic guidance and route information customized according to the emotional state and specific needs of individual users.

[0309] This invention aims to optimize real-time information in urban traffic management. Specific examples are shown below.

[0310] The server collects traffic information from monitoring devices installed on roads and intersections within the city. This includes cameras and sensors, and the data is used to measure the number and speed of vehicles, as well as the degree of congestion. The server stores this collected data in a database. The server continuously processes the data in real time, utilizing generative AI models to analyze traffic flow patterns. Frameworks such as TensorFlow and PyTorch are commonly used for this analysis.

[0311] The terminal receives analyzed traffic data transmitted from the server and also collects user emotion data. This emotion data is collected via smartphones and in-vehicle devices, and the user's emotional state can be analyzed using speech recognition and facial expression recognition software. This analysis incorporates an emotion analysis engine, which is used as additional information to find the optimal route for the user's travel.

[0312] Users can receive real-time traffic information adapted to their emotional state via their device. For example, if a user is experiencing high stress levels during the morning rush hour, the system will suggest a route that avoids congestion. It will also provide users in a hurry with the shortest possible route to their destination. This allows users to create travel plans that best suit their emotional state at that moment.

[0313] For example, during the morning rush hour, if traffic volume suddenly increases at a specific intersection, data is collected by a server, and the terminal controls traffic signals accordingly. Furthermore, based on the user's sentiment analysis, the smartphone provides optimal route guidance tailored to their emotions. An example of a prompt message might be, "If the user is feeling stressed during their morning commute, how would you suggest a comfortable route?"

[0314] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0315] Step 1:

[0316] The server collects traffic information from monitoring devices installed throughout the city. The input consists of raw data from traffic cameras and sensors, specifically information such as the number of vehicles, their speed, and congestion levels. This data is collected, organized, and stored in a database. This provides the foundational data needed to understand traffic conditions.

[0317] Step 2:

[0318] The server uses collected traffic information to generate an AI model that analyzes and predicts traffic flow. The input is data from the aforementioned traffic database. The AI ​​model analyzes this data and predicts future traffic flow patterns. As a result, it outputs congestion prediction data for each intersection and road section. This data enables control that anticipates future traffic conditions.

[0319] Step 3:

[0320] The terminal receives predictive data provided by the server and collects emotional data from the user's terminal. Inputs include predictive data from the server and audio / image data from the user's terminal. Using this data, an emotional analysis engine analyzes the user's emotional state. As a result, it outputs data indicating the user's emotional state. This allows for the preparation of personalized traffic guidance for each individual user.

[0321] Step 4:

[0322] The device integrates emotional state data and traffic prediction data to generate personalized traffic information. The input consists of emotional state data and traffic prediction data. Using this data, for example, it suggests a comfortable route for a stressed user and the shortest route for a user in a hurry. This enables adaptive and user-centered traffic guidance.

[0323] Step 5:

[0324] Users receive personalized traffic information from their devices and use it to guide their actual travel. Feedback is sent back to the device from the user. The input is personalized traffic information from the device. Based on user behavior and feedback, the system collects data to improve the accuracy of future directions. This improves the overall accuracy of the system and user satisfaction.

[0325] (Application Example 2)

[0326] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0327] In modern urban areas, traffic congestion is a daily occurrence, hindering efficient travel. Furthermore, existing traffic management systems, while considering the physical factors of traffic flow, fail to provide personalized traffic guidance that takes into account the emotional state of users. As a result, users often travel while experiencing emotional stress, further worsening overall traffic efficiency. The objective of this invention is to alleviate traffic congestion while providing traffic guidance that takes into account the emotional state of users, thereby achieving comfortable and efficient travel.

[0328] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0329] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, means for generating control instructions to traffic control devices based on the prediction results and dynamically adjusting the timing of signal devices, and means for acquiring and analyzing user emotional data and personalizing traffic guidance based on emotional state. This enables dynamic optimization of traffic flow while providing personalized traffic guidance based on the user's emotional state.

[0330] "Traffic conditions" refers to all information indicating the number of vehicles, their speed, the degree of congestion, etc., within a city.

[0331] A "monitoring device" is a device installed to acquire traffic information, such as a traffic camera or sensor.

[0332] "Means for analyzing traffic information" refers to processes and devices used to process received traffic data and understand traffic conditions based on the results.

[0333] An "artificial intelligence model" is a program that runs machine learning and predictive algorithms used to analyze data and predict traffic flow and traffic patterns.

[0334] "Traffic control devices" are equipment used to control the flow of traffic, such as traffic lights and electronic billboards.

[0335] A "control instruction" is a command sent to a traffic control device that adjusts the timing of signals and other traffic management parameters.

[0336] "Means of providing information via communication devices" refers to methods or devices for transmitting information to a user's terminal via a network.

[0337] "Emotional data" refers to data acquired to measure and analyze the emotional state of users.

[0338] An "emotion recognition engine" is a technology that detects and analyzes a user's emotional state from their facial expressions, voice, and input information.

[0339] "Emotionally-based traffic guidance" refers to traffic information and route guidance provided in a way that is adapted to the user's current emotions.

[0340] In this invention, multiple monitoring devices are installed on a server to highly optimize urban traffic management. These monitoring devices include traffic cameras and sensors to acquire real-time traffic information. The server aggregates the traffic information and uses an artificial intelligence model to predict traffic volume and traffic flow patterns. Based on this prediction, control instructions are generated for traffic control devices, and the timing of traffic signals is dynamically adjusted.

[0341] The server is equipped with an emotion recognition engine to acquire and analyze users' emotional data. Users can receive real-time traffic information and personalized traffic guidance based on their emotional state using their smartphones or in-car devices. The emotion recognition engine acquires emotional data from users' voices and facial expressions through speech recognition and image recognition technologies. This data is used in traffic guidance to provide optimal routes and information that match the user's current emotions.

[0342] Specifically, if a user is feeling stressed, the emotion recognition engine will detect this and provide route guidance that prioritizes comfort, such as a route with scenic views. Furthermore, if the system determines that the user is in a hurry, it will suggest a route that will get them to their destination in the shortest possible time. For example, a user who wants to enjoy a relaxing drive with their family on a Sunday afternoon will be offered a route that not only avoids congestion but also allows them to enjoy the scenery. This results in traffic guidance tailored to the user's emotional state, ultimately reducing traffic congestion and improving the overall comfort of travel.

[0343] An example of a prompt message is: "Based on the user's emotional state, suggest the best route from the current location to the destination. If you are relaxed, choose a scenic route; if you are in a hurry, choose the shortest route."

[0344] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0345] Step 1:

[0346] The server acquires real-time traffic information from multiple monitoring devices. Input is raw data from the monitoring devices, while output is the number of vehicles, their speed, and congestion levels collected by traffic cameras and sensors. The data is collected by a program and processed into a standardized format.

[0347] Step 2:

[0348] The server inputs this traffic information into an artificial intelligence model to predict traffic volume and traffic flow patterns. The input here is processed traffic information, and the output is predicted data for future traffic volume and congestion locations. This process utilizes machine learning algorithms, which compare current data with historical data to make predictions.

[0349] Step 3:

[0350] The terminal generates control instructions for the traffic control system based on the prediction results. The input is prediction data, and the output is a control signal that adjusts the timing of the signaling device. The terminal sends commands to the signaling device, dynamically adjusting the timing of the signals to optimize traffic flow.

[0351] Step 4:

[0352] The server collects voice and facial expression data from smartphones and in-car devices via an emotion recognition engine to acquire user emotional data. The input is the user's real-time voice and image data, and the output is analyzed emotional state data. Here, emotions are determined using voice recognition and image recognition technologies.

[0353] Step 5:

[0354] The device generates traffic guidance tailored to the user's emotional state based on analyzed emotional data. Input is emotional state data and current traffic information, and output is personalized route guidance. Based on a generating AI model, it selects routes and information that match the user's preferences.

[0355] Step 6:

[0356] The user receives emotion-based traffic guidance from a server via their device and travels along the optimal route. The input is route guidance from the device, and the output is the user's travel experience. Specifically, by following the route guidance instructions, a stress-free journey is achieved. This process makes it possible to smoothly meet the user's transportation needs according to their circumstances.

[0357] 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.

[0358] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0359] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0360] [Third Embodiment]

[0361] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0362] 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.

[0363] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0364] 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.

[0365] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0366] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0367] 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.

[0368] 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.

[0369] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0370] The 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.

[0371] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0372] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0373] To implement this invention, it is first necessary to install multiple monitoring devices in the city's traffic infrastructure and collect traffic information in real time through these devices. The monitoring devices include cameras and sensors installed at major intersections and major roads. These devices are used to understand traffic volume, speed, and congestion levels.

[0374] The server collects real-time traffic data from these monitoring devices and stores it in a database. This traffic data includes the current number, location, and speed of vehicles. The server also stores historical traffic datasets and uses them as training data to make future predictions.

[0375] The terminal receives collected real-time traffic data and analyzes it using an artificial intelligence model. Specifically, the terminal predicts traffic flow patterns and executes algorithms to estimate planned traffic volume. Based on this analysis, it calculates the optimal timing for switching traffic signals and generates instructions to smooth traffic flow.

[0376] The traffic signal system receives instructions from terminals and dynamically adjusts the display of traffic lights at intersections. This adjustment is based on predicted traffic patterns and can improve traffic flow, for example, by extending the green light time for a particular lane.

[0377] Users receive real-time traffic information transmitted from a server via their smartphones or in-car navigation devices. This information includes suggestions for the most efficient routes and congestion information, allowing users to optimize their travel paths based on this information.

[0378] As a concrete example, during the morning rush hour, a server detects an increase in traffic volume at a specific intersection, and a terminal analyzes this data to instruct the traffic signal system on when to switch signals. As a result, traffic flow becomes smoother, travel time is shortened, and congestion is alleviated. Furthermore, users are guided along optimized routes through the app, making their journey to their destination more efficient and comfortable.

[0379] The following describes the processing flow.

[0380] Step 1:

[0381] The server collects real-time traffic information from monitoring devices installed in the transportation infrastructure. This includes analyzing video data from traffic cameras to recognize the number and movement of vehicles using computer vision technology, and acquiring data on vehicle speed and traffic density from sensors.

[0382] Step 2:

[0383] The server stores the collected traffic data in a central database and manages it together with historical data. This creates a foundation for analyzing long-term traffic patterns and helps in future predictions.

[0384] Step 3:

[0385] The terminal receives real-time traffic data sent from the server and analyzes this data using its built-in artificial intelligence model. The terminal predicts traffic flow patterns from current traffic volume and speed and calculates the optimal timing for issuing instructions to traffic signaling devices.

[0386] Step 4:

[0387] The terminal creates control instructions for the signaling device based on the analysis results and transmits them to the signaling device via the interface. The signaling device executes these instructions and adjusts the timing of the signal changes at the intersection.

[0388] Step 5:

[0389] Users receive traffic information from a server via their smartphone or in-car terminal. They then check suggested routes and predicted traffic conditions within the application to optimize their travel plans.

[0390] Step 6:

[0391] Users provide feedback to the application about their travel experiences and selected route information, which the server then uses to improve the overall prediction accuracy of the system. This feedback loop improves the performance of the AI ​​model as more data is collected.

[0392] (Example 1)

[0393] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0394] In modern urban areas, traffic congestion and signal control require appropriate information analysis and real-time signal adjustments to ensure smooth traffic flow. However, conventional systems have handled traffic data collection, prediction, and control separately, making efficient optimization of traffic flow difficult. Therefore, there is a need to analyze traffic conditions with high accuracy and optimize them rapidly.

[0395] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0396] In this invention, the server includes means for receiving environmental data from multiple monitoring devices installed to acquire traffic information, means for predicting traffic flow patterns using a generative AI model to analyze the received environmental data, and means for generating adjustment instructions to a control device based on the prediction results and dynamically optimizing the timing of signal devices. This enables optimal analysis and control of traffic information in real time.

[0397] A "monitoring device" is a device installed to acquire traffic information and collects environmental data such as traffic volume, speed, and congestion levels.

[0398] A "generative AI model" is a model that uses artificial intelligence technology to predict traffic flow patterns and traffic volume based on past and present environmental data.

[0399] "Environmental data" refers to data such as traffic volume, speed, and congestion levels obtained from monitoring devices to analyze traffic flow.

[0400] A "control device" is a device that receives instructions to adjust the timing and operation of signal devices based on prediction results.

[0401] "Communication equipment" refers to devices used to provide collected information and prediction results to the user's terminal.

[0402] "Feedback data" refers to information submitted by users based on actual traffic conditions and usage experiences, and is data that contributes to improving the accuracy of analysis.

[0403] To implement this invention, it is first necessary to install monitoring devices at urban intersections and major roads. These monitoring devices collect environmental data such as traffic volume, vehicle speed, and road congestion. A server receives the environmental data collected in real time from these monitoring devices and stores it in a database. The stored data is also used as a training dataset, including past traffic patterns.

[0404] The terminal analyzes traffic data received from the server using a generative AI model. Specifically, the generative AI model utilizes neural networks and machine learning algorithms to predict traffic flow patterns. Based on the predictive information obtained from the analysis, the terminal instructs the traffic signal system to make optimal timing adjustments. These timing adjustments are used to allow the traffic signal system to dynamically change signals at intersections.

[0405] Furthermore, users receive real-time traffic information transmitted from the server via communication devices on their smartphones or in-car devices. This information includes suggestions for the most efficient routes and information on congested sections, allowing users to optimize their routes and reduce travel time.

[0406] For example, if an increase in traffic is predicted at a certain intersection, the terminal instructs the traffic signal system to extend the green light, allowing vehicles to pass through the intersection smoothly and preventing congestion. It also assists users in reaching their destination comfortably by suggesting alternative routes.

[0407] As an example of a prompt to be input to the generating AI model, if it is in the format of "Please explain the algorithm for analyzing traffic flow during this morning's rush hour and calculating the optimal timing for operating traffic signals," the system will perform the optimal data processing according to the specified purpose.

[0408] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0409] Step 1:

[0410] The server receives environmental data on traffic conditions from monitoring devices installed at various points in the city. Input includes traffic volume, vehicle speed, and congestion data collected by the monitoring devices. The server filters this data, extracts the necessary information, and stores it in a database. The output is structured real-time traffic data.

[0411] Step 2:

[0412] The terminal receives real-time traffic data transmitted from the server. The input is traffic data stored by the server. The terminal uses a generative AI model to analyze traffic flow patterns based on the input data. Specifically, it uses a neural network to analyze the data and predict traffic peaks and congested areas. The output is the predicted traffic pattern information.

[0413] Step 3:

[0414] The terminal generates instructions for the signaling device based on the analysis results. Its input is predicted traffic pattern information. Based on this information, it generates control signals to smooth traffic flow and transmits them to the signaling device via the network. Specific actions include extending the green light and adjusting the signal cycle. The output is dynamically generated signal control instructions.

[0415] Step 4:

[0416] The user receives real-time traffic information provided by the server via communication devices. The input includes optimal routes and congestion information transmitted from the server. The user uses this information to optimize their route and travel efficiently. Specifically, they follow the suggested route using an in-car navigation system or a smartphone app. The output is the optimized travel route.

[0417] (Application Example 1)

[0418] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0419] In modern cities, traffic congestion is a daily problem, leading to increased travel times and environmental burdens. Furthermore, with the increasing prevalence of autonomous vehicles, there is a need for technologies that utilize real-time traffic information to optimize routes and dynamically adjust traffic signal control. Additionally, conventional systems fail to adequately provide optimal route suggestions tailored to individual vehicles.

[0420] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0421] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, and means for analyzing traffic patterns based on driving data from multiple mobile devices to support safe and efficient operation. This makes it possible to optimize traffic flow in real time, alleviate congestion, and suggest appropriate routes for autonomous vehicles.

[0422] "Traffic information" refers to data on traffic volume, speed, and vehicle flow collected from monitoring devices.

[0423] An "artificial intelligence model" is a machine learning algorithm used to predict traffic patterns and generate appropriate control instructions.

[0424] A "traffic signal system" is a device that controls traffic signals at intersections and manages the flow of traffic.

[0425] "User's device" refers to a device such as a smartphone or in-vehicle device that can receive and display traffic information.

[0426] "Driving data" refers to data that includes the location, speed, and route information of a mobile device.

[0427] "Means of supporting operations" refer to technical means for analyzing traffic information and proposing the optimal route for mobile devices.

[0428] To implement this invention, a traffic management system needs to be constructed. The system collects real-time traffic information by installing monitoring devices at major intersections and roads in cities. These monitoring devices include cameras and sensors, which collect data such as traffic volume, speed, and congestion levels. A server receives and stores the data collected from these monitoring devices. Furthermore, it uses artificial intelligence models based on this data to predict traffic flow patterns.

[0429] The server calculates the optimal timing for switching traffic signals based on predicted traffic information and dynamically adjusts the traffic light displays. For example, it can smooth traffic flow by extending the signal duration for specific lanes depending on the predicted traffic volume.

[0430] The terminal provides collected data and predictive information to the user's terminal, allowing the user to optimize their travel route based on this information. Examples of user terminals include smartphones and in-car devices, through which optimized routes and traffic information are provided.

[0431] This system utilizes a data analysis program built in Python, using Pandas for data preprocessing and TensorFlow for running predictive models. A Web API is also used to receive real-time data.

[0432] As a concrete example, when a user uses their car for their morning commute, the system can automatically analyze current traffic information and suggest the optimal commute route. An example of a prompt message is "Suggest an optimized signal timing for the next intersection," and the system will calculate the best solution for signal control in response to this command.

[0433] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0434] Step 1:

[0435] The server receives real-time traffic information from monitoring devices. The monitoring devices use cameras and sensors to acquire data such as traffic volume, vehicle speed, and vehicle flow, and transmit this data to the server. The input is raw data from the monitoring devices, which the server then retrieves.

[0436] Step 2:

[0437] The server preprocesses the received raw traffic data. This preprocessing is done using the Python Pandas library to remove noise and impute missing values. The output of this step is the cleaned traffic data.

[0438] Step 3:

[0439] The server uses the cleaned data to input a machine learning model, which is a generative AI model, to predict traffic flow patterns. This model is trained on historical traffic data and runs using TensorFlow. The output is the predicted traffic pattern.

[0440] Step 4:

[0441] The server calculates the optimal timing for switching traffic signals based on predicted traffic patterns. It dynamically adjusts the control instructions to the signaling devices to ensure smooth vehicle flow at specific intersections. The output is a specific control instruction for the signaling devices.

[0442] Step 5:

[0443] The terminal receives data and predictive information from the server and displays it on the user's terminal. The user's terminal can be a smartphone or an in-car navigation device, and it displays optimal travel routes and traffic information, suggesting a route optimized for the user. The output is visualized information provided to the user.

[0444] Step 6:

[0445] The user selects a travel route based on the information displayed on the terminal and drives according to traffic conditions. This enables safe and efficient travel. The output is the optimal route selected by the user.

[0446] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0447] This invention provides a system that achieves a higher level of traffic flow optimization by incorporating an emotion engine into urban traffic management systems. In this configuration, a monitoring device that acquires traffic conditions, a terminal that controls signal devices, and an emotion engine that recognizes user emotions work in close cooperation.

[0448] The server aggregates real-time traffic information collected through monitoring devices installed throughout the city. This includes data from traffic cameras and sensors, such as the number of vehicles, their speed, and the degree of congestion. This information is stored in a database and used as the basis for analysis.

[0449] The terminal receives this data and uses an artificial intelligence model to predict and analyze traffic flow. In addition, the terminal is equipped with an emotion engine that can collect and analyze emotional data from the user's device. The emotion engine detects the user's emotional state from their voice, facial expressions, or input feedback, and uses this to personalize traffic information.

[0450] Users receive real-time traffic information from a server via their smartphones or in-car devices. This includes personalized routes and traffic information adapted to the user's emotions. For example, if a user is stressed, the emotion engine recognizes this and suggests a different route that prioritizes comfort over the usual route. Or, for users in a hurry, it provides traffic guidance to reach their destination in the shortest possible time.

[0451] As a concrete example, during the morning rush hour, when the server detects a sudden surge in traffic at a specific intersection, the terminal analyzes the data and adjusts the timing of the traffic signals. Simultaneously, the emotion engine evaluates the user's current emotional state and sends the optimal route guidance based on the results to the user's smartphone. As a result, the user can create a travel plan that best suits their situation and emotions, which in turn helps to alleviate traffic congestion and improve traffic flow.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The server collects real-time traffic data from multiple monitoring devices installed in the transportation infrastructure. This collected data includes the number and speed of vehicles and road congestion, and it integrates the data that is periodically transmitted from each monitoring device.

[0455] Step 2:

[0456] The server stores the collected traffic data in a database. This storage process maintains data integrity and consistency while saving it in a format that is easy to use for subsequent analysis.

[0457] Step 3:

[0458] The terminal receives traffic data sent from the server and analyzes that data using an artificial intelligence model. The AI ​​model predicts traffic flow patterns and generates instructions to optimize the timing of signal switching.

[0459] Step 4:

[0460] The emotion engine installed in the device collects emotional data from the user's device. This emotional data is obtained through analysis of the user's voice and facial expressions, or from feedback entered directly.

[0461] Step 5:

[0462] The device uses emotional data analyzed by an emotion engine to generate personalized traffic information and travel routes tailored to the user. This provides personalized guidance so that users can relax and enjoy their journey.

[0463] Step 6:

[0464] The server transmits optimized signal control instructions to signaling devices, adjusting traffic flow in real time. Simultaneously, it provides users with the latest information by transmitting customized traffic data to their terminals.

[0465] Step 7:

[0466] Users act based on personalized traffic guidance received via smartphones or in-car devices. By following this guidance, users can travel to their destination efficiently while reducing stress and anxiety.

[0467] (Example 2)

[0468] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0469] In modern cities, traffic congestion and road overcrowding are major social challenges, and there is a need for efficient traffic management and comfortable travel for users. At the same time, traffic information systems often neglect to consider the stress and emotional state of individual users. Conventional systems manage traffic flow centrally, but lack the provision of services based on users' emotions. In response to this, there is a need to develop a traffic management system that can provide real-time information adapted to emotions.

[0470] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0471] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, and means for collecting user emotion data and analyzing it using an emotion analysis engine. This makes it possible to provide personalized traffic information that is adapted to the user's emotions.

[0472] A "monitoring device" is a physical device installed on roads and intersections to measure traffic conditions and acquire information in real time.

[0473] "Traffic information" refers to data about traffic conditions, specifically including the number of vehicles, speed, and degree of congestion.

[0474] An "artificial intelligence model" is an algorithm that learns patterns based on data collected in the past and uses them to predict traffic flow.

[0475] An "emotion analysis engine" is a technology for detecting and analyzing a user's emotional state, and it typically utilizes voice and facial expression data.

[0476] "Personalized traffic information" refers to traffic guidance and route information that is customized according to the emotional state and specific needs of individual users.

[0477] This invention aims to optimize real-time information in urban traffic management. Specific examples are shown below.

[0478] The server collects traffic information from monitoring devices installed on roads and intersections within the city. This includes cameras and sensors, and the data is used to measure the number and speed of vehicles, as well as the degree of congestion. The server stores this collected data in a database. The server continuously processes the data in real time, utilizing generative AI models to analyze traffic flow patterns. Frameworks such as TensorFlow and PyTorch are commonly used for this analysis.

[0479] The terminal receives analyzed traffic data transmitted from the server and also collects user emotion data. This emotion data is collected via smartphones and in-vehicle devices, and the user's emotional state can be analyzed using speech recognition and facial expression recognition software. This analysis incorporates an emotion analysis engine, which is used as additional information to find the optimal route for the user's travel.

[0480] Users can receive real-time traffic information adapted to their emotional state via their device. For example, if a user is experiencing high stress levels during the morning rush hour, the system will suggest a route that avoids congestion. It will also provide users in a hurry with the shortest possible route to their destination. This allows users to create travel plans that best suit their emotional state at that moment.

[0481] For example, during the morning rush hour, if traffic volume suddenly increases at a specific intersection, data is collected by a server, and the terminal controls traffic signals accordingly. Furthermore, based on the user's sentiment analysis, the smartphone provides optimal route guidance tailored to their emotions. An example of a prompt message might be, "If the user is feeling stressed during their morning commute, how would you suggest a comfortable route?"

[0482] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0483] Step 1:

[0484] The server collects traffic information from monitoring devices installed throughout the city. The input consists of raw data from traffic cameras and sensors, specifically information such as the number of vehicles, their speed, and congestion levels. This data is collected, organized, and stored in a database. This provides the foundational data needed to understand traffic conditions.

[0485] Step 2:

[0486] The server uses collected traffic information to generate an AI model that analyzes and predicts traffic flow. The input is data from the aforementioned traffic database. The AI ​​model analyzes this data and predicts future traffic flow patterns. As a result, it outputs congestion prediction data for each intersection and road section. This data enables control that anticipates future traffic conditions.

[0487] Step 3:

[0488] The terminal receives predictive data provided by the server and collects emotional data from the user's terminal. Inputs include predictive data from the server and audio / image data from the user's terminal. Using this data, an emotional analysis engine analyzes the user's emotional state. As a result, it outputs data indicating the user's emotional state. This allows for the preparation of personalized traffic guidance for each individual user.

[0489] Step 4:

[0490] The device integrates emotional state data and traffic prediction data to generate personalized traffic information. The input consists of emotional state data and traffic prediction data. Using this data, for example, it suggests a comfortable route for a stressed user and the shortest route for a user in a hurry. This enables adaptive and user-centered traffic guidance.

[0491] Step 5:

[0492] Users receive personalized traffic information from their devices and use it to guide their actual travel. Feedback is sent back to the device from the user. The input is personalized traffic information from the device. Based on user behavior and feedback, the system collects data to improve the accuracy of future directions. This improves the overall accuracy of the system and user satisfaction.

[0493] (Application Example 2)

[0494] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0495] In modern urban areas, traffic congestion is a daily occurrence, hindering efficient travel. Furthermore, existing traffic management systems, while considering the physical factors of traffic flow, fail to provide personalized traffic guidance that takes into account the emotional state of users. As a result, users often travel while experiencing emotional stress, further worsening overall traffic efficiency. The objective of this invention is to alleviate traffic congestion while providing traffic guidance that takes into account the emotional state of users, thereby achieving comfortable and efficient travel.

[0496] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0497] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, means for generating control instructions to traffic control devices based on the prediction results and dynamically adjusting the timing of signal devices, and means for acquiring and analyzing user emotional data and personalizing traffic guidance based on emotional state. This enables dynamic optimization of traffic flow while providing personalized traffic guidance based on the user's emotional state.

[0498] "Traffic conditions" refers to all information indicating the number of vehicles, their speed, the degree of congestion, etc., within a city.

[0499] A "monitoring device" is a device installed to acquire traffic information, such as a traffic camera or sensor.

[0500] "Means for analyzing traffic information" refers to processes and devices used to process received traffic data and understand traffic conditions based on the results.

[0501] An "artificial intelligence model" is a program that runs machine learning and predictive algorithms used to analyze data and predict traffic flow and traffic patterns.

[0502] "Traffic control devices" are equipment used to control the flow of traffic, such as traffic lights and electronic billboards.

[0503] A "control instruction" is a command sent to a traffic control device that adjusts the timing of signals and other traffic management parameters.

[0504] "Means of providing information via communication devices" refers to methods or devices for transmitting information to a user's terminal via a network.

[0505] "Emotional data" refers to data acquired to measure and analyze the emotional state of users.

[0506] An "emotion recognition engine" is a technology that detects and analyzes a user's emotional state from their facial expressions, voice, and input information.

[0507] "Emotionally-based traffic guidance" refers to traffic information and route guidance provided in a way that is adapted to the user's current emotions.

[0508] In this invention, multiple monitoring devices are installed on a server to highly optimize urban traffic management. These monitoring devices include traffic cameras and sensors to acquire real-time traffic information. The server aggregates the traffic information and uses an artificial intelligence model to predict traffic volume and traffic flow patterns. Based on this prediction, control instructions are generated for traffic control devices, and the timing of traffic signals is dynamically adjusted.

[0509] The server is equipped with an emotion recognition engine to acquire and analyze users' emotional data. Users can receive real-time traffic information and personalized traffic guidance based on their emotional state using their smartphones or in-car devices. The emotion recognition engine acquires emotional data from users' voices and facial expressions through speech recognition and image recognition technologies. This data is used in traffic guidance to provide optimal routes and information that match the user's current emotions.

[0510] Specifically, if a user is feeling stressed, the emotion recognition engine will detect this and provide route guidance that prioritizes comfort, such as a route with scenic views. Furthermore, if the system determines that the user is in a hurry, it will suggest a route that will get them to their destination in the shortest possible time. For example, a user who wants to enjoy a relaxing drive with their family on a Sunday afternoon will be offered a route that not only avoids congestion but also allows them to enjoy the scenery. This results in traffic guidance tailored to the user's emotional state, ultimately reducing traffic congestion and improving the overall comfort of travel.

[0511] An example of a prompt message is: "Based on the user's emotional state, suggest the best route from the current location to the destination. If you are relaxed, choose a scenic route; if you are in a hurry, choose the shortest route."

[0512] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0513] Step 1:

[0514] The server acquires real-time traffic information from multiple monitoring devices. Input is raw data from the monitoring devices, while output is the number of vehicles, their speed, and congestion levels collected by traffic cameras and sensors. The data is collected by a program and processed into a standardized format.

[0515] Step 2:

[0516] The server inputs this traffic information into an artificial intelligence model to predict traffic volume and traffic flow patterns. The input here is processed traffic information, and the output is predicted data for future traffic volume and congestion locations. This process utilizes machine learning algorithms, which compare current data with historical data to make predictions.

[0517] Step 3:

[0518] The terminal generates control instructions for the traffic control system based on the prediction results. The input is prediction data, and the output is a control signal that adjusts the timing of the signaling device. The terminal sends commands to the signaling device, dynamically adjusting the timing of the signals to optimize traffic flow.

[0519] Step 4:

[0520] The server collects voice and facial expression data from smartphones and in-car devices via an emotion recognition engine to acquire user emotional data. The input is the user's real-time voice and image data, and the output is analyzed emotional state data. Here, emotions are determined using voice recognition and image recognition technologies.

[0521] Step 5:

[0522] The device generates traffic guidance tailored to the user's emotional state based on analyzed emotional data. Input is emotional state data and current traffic information, and output is personalized route guidance. Based on a generating AI model, it selects routes and information that match the user's preferences.

[0523] Step 6:

[0524] The user receives emotion-based traffic guidance from a server via their device and travels along the optimal route. The input is route guidance from the device, and the output is the user's travel experience. Specifically, by following the route guidance instructions, a stress-free journey is achieved. This process makes it possible to smoothly meet the user's transportation needs according to their circumstances.

[0525] 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.

[0526] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0527] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0528] [Fourth Embodiment]

[0529] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0530] 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.

[0531] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0532] 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.

[0533] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0534] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0535] 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.

[0536] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0537] 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.

[0538] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0539] The 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.

[0540] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0541] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0542] To implement this invention, it is first necessary to install multiple monitoring devices in the city's traffic infrastructure and collect traffic information in real time through these devices. The monitoring devices include cameras and sensors installed at major intersections and major roads. These devices are used to understand traffic volume, speed, and congestion levels.

[0543] The server collects real-time traffic data from these monitoring devices and stores it in a database. This traffic data includes the current number, location, and speed of vehicles. The server also stores historical traffic datasets and uses them as training data to make future predictions.

[0544] The terminal receives collected real-time traffic data and analyzes it using an artificial intelligence model. Specifically, the terminal predicts traffic flow patterns and executes algorithms to estimate planned traffic volume. Based on this analysis, it calculates the optimal timing for switching traffic signals and generates instructions to smooth traffic flow.

[0545] The traffic signal system receives instructions from terminals and dynamically adjusts the display of traffic lights at intersections. This adjustment is based on predicted traffic patterns and can improve traffic flow, for example, by extending the green light time for a particular lane.

[0546] Users receive real-time traffic information transmitted from a server via their smartphones or in-car navigation devices. This information includes suggestions for the most efficient routes and congestion information, allowing users to optimize their travel paths based on this information.

[0547] As a concrete example, during the morning rush hour, a server detects an increase in traffic volume at a specific intersection, and a terminal analyzes this data to instruct the traffic signal system on when to switch signals. As a result, traffic flow becomes smoother, travel time is shortened, and congestion is alleviated. Furthermore, users are guided along optimized routes through the app, making their journey to their destination more efficient and comfortable.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] The server collects real-time traffic information from monitoring devices installed in the transportation infrastructure. This includes analyzing video data from traffic cameras to recognize the number and movement of vehicles using computer vision technology, and acquiring data on vehicle speed and traffic density from sensors.

[0551] Step 2:

[0552] The server stores the collected traffic data in a central database and manages it together with historical data. This creates a foundation for analyzing long-term traffic patterns and helps in future predictions.

[0553] Step 3:

[0554] The terminal receives real-time traffic data sent from the server and analyzes this data using its built-in artificial intelligence model. The terminal predicts traffic flow patterns from current traffic volume and speed and calculates the optimal timing for issuing instructions to traffic signaling devices.

[0555] Step 4:

[0556] The terminal creates control instructions for the signaling device based on the analysis results and transmits them to the signaling device via the interface. The signaling device executes these instructions and adjusts the timing of the signal changes at the intersection.

[0557] Step 5:

[0558] Users receive traffic information from a server via their smartphone or in-car terminal. They then check suggested routes and predicted traffic conditions within the application to optimize their travel plans.

[0559] Step 6:

[0560] Users provide feedback to the application about their travel experiences and selected route information, which the server then uses to improve the overall prediction accuracy of the system. This feedback loop improves the performance of the AI ​​model as more data is collected.

[0561] (Example 1)

[0562] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0563] In modern urban areas, traffic congestion and signal control require appropriate information analysis and real-time signal adjustments to ensure smooth traffic flow. However, conventional systems have handled traffic data collection, prediction, and control separately, making efficient optimization of traffic flow difficult. Therefore, there is a need to analyze traffic conditions with high accuracy and optimize them rapidly.

[0564] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0565] In this invention, the server includes means for receiving environmental data from multiple monitoring devices installed to acquire traffic information, means for predicting traffic flow patterns using a generative AI model to analyze the received environmental data, and means for generating adjustment instructions to a control device based on the prediction results and dynamically optimizing the timing of signal devices. This enables optimal analysis and control of traffic information in real time.

[0566] A "monitoring device" is a device installed to acquire traffic information and collects environmental data such as traffic volume, speed, and congestion levels.

[0567] A "generative AI model" is a model that uses artificial intelligence technology to predict traffic flow patterns and traffic volume based on past and present environmental data.

[0568] "Environmental data" refers to data such as traffic volume, speed, and congestion levels obtained from monitoring devices to analyze traffic flow.

[0569] A "control device" is a device that receives instructions to adjust the timing and operation of signal devices based on prediction results.

[0570] "Communication equipment" refers to devices used to provide collected information and prediction results to the user's terminal.

[0571] "Feedback data" refers to information submitted by users based on actual traffic conditions and usage experiences, and is data that contributes to improving the accuracy of analysis.

[0572] To implement this invention, it is first necessary to install monitoring devices at urban intersections and major roads. These monitoring devices collect environmental data such as traffic volume, vehicle speed, and road congestion. A server receives the environmental data collected in real time from these monitoring devices and stores it in a database. The stored data is also used as a training dataset, including past traffic patterns.

[0573] The terminal analyzes traffic data received from the server using a generative AI model. Specifically, the generative AI model utilizes neural networks and machine learning algorithms to predict traffic flow patterns. Based on the predictive information obtained from the analysis, the terminal instructs the traffic signal system to make optimal timing adjustments. These timing adjustments are used to allow the traffic signal system to dynamically change signals at intersections.

[0574] Furthermore, users receive real-time traffic information transmitted from the server via communication devices on their smartphones or in-car devices. This information includes suggestions for the most efficient routes and information on congested sections, allowing users to optimize their routes and reduce travel time.

[0575] For example, if an increase in traffic is predicted at a certain intersection, the terminal instructs the traffic signal system to extend the green light, allowing vehicles to pass through the intersection smoothly and preventing congestion. It also assists users in reaching their destination comfortably by suggesting alternative routes.

[0576] As an example of a prompt to be input to the generating AI model, if it is in the format of "Please explain the algorithm for analyzing traffic flow during this morning's rush hour and calculating the optimal timing for operating traffic signals," the system will perform the optimal data processing according to the specified purpose.

[0577] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0578] Step 1:

[0579] The server receives environmental data on traffic conditions from monitoring devices installed at various points in the city. Input includes traffic volume, vehicle speed, and congestion data collected by the monitoring devices. The server filters this data, extracts the necessary information, and stores it in a database. The output is structured real-time traffic data.

[0580] Step 2:

[0581] The terminal receives real-time traffic data transmitted from the server. The input is traffic data stored by the server. The terminal uses a generative AI model to analyze traffic flow patterns based on the input data. Specifically, it uses a neural network to analyze the data and predict traffic peaks and congested areas. The output is the predicted traffic pattern information.

[0582] Step 3:

[0583] The terminal generates instructions for the signaling device based on the analysis results. Its input is predicted traffic pattern information. Based on this information, it generates control signals to smooth traffic flow and transmits them to the signaling device via the network. Specific actions include extending the green light and adjusting the signal cycle. The output is dynamically generated signal control instructions.

[0584] Step 4:

[0585] The user receives real-time traffic information provided by the server via communication devices. The input includes optimal routes and congestion information transmitted from the server. The user uses this information to optimize their route and travel efficiently. Specifically, they follow the suggested route using an in-car navigation system or a smartphone app. The output is the optimized travel route.

[0586] (Application Example 1)

[0587] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0588] In modern cities, traffic congestion is a daily problem, leading to increased travel times and environmental burdens. Furthermore, with the increasing prevalence of autonomous vehicles, there is a need for technologies that utilize real-time traffic information to optimize routes and dynamically adjust traffic signal control. Additionally, conventional systems fail to adequately provide optimal route suggestions tailored to individual vehicles.

[0589] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0590] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, and means for analyzing traffic patterns based on driving data from multiple mobile devices to support safe and efficient operation. This makes it possible to optimize traffic flow in real time, alleviate congestion, and suggest appropriate routes for autonomous vehicles.

[0591] "Traffic information" refers to data on traffic volume, speed, and vehicle flow collected from monitoring devices.

[0592] An "artificial intelligence model" is a machine learning algorithm used to predict traffic patterns and generate appropriate control instructions.

[0593] A "traffic signal system" is a device that controls traffic signals at intersections and manages the flow of traffic.

[0594] "User's device" refers to a device such as a smartphone or in-vehicle device that can receive and display traffic information.

[0595] "Driving data" refers to data that includes the location, speed, and route information of a mobile device.

[0596] "Means of supporting operations" refer to technical means for analyzing traffic information and proposing the optimal route for mobile devices.

[0597] To implement this invention, a traffic management system needs to be constructed. The system collects real-time traffic information by installing monitoring devices at major intersections and roads in cities. These monitoring devices include cameras and sensors, which collect data such as traffic volume, speed, and congestion levels. A server receives and stores the data collected from these monitoring devices. Furthermore, it uses artificial intelligence models based on this data to predict traffic flow patterns.

[0598] The server calculates the optimal timing for switching traffic signals based on predicted traffic information and dynamically adjusts the traffic light displays. For example, it can smooth traffic flow by extending the signal duration for specific lanes depending on the predicted traffic volume.

[0599] The terminal provides collected data and predictive information to the user's terminal, allowing the user to optimize their travel route based on this information. Examples of user terminals include smartphones and in-car devices, through which optimized routes and traffic information are provided.

[0600] This system utilizes a data analysis program built in Python, using Pandas for data preprocessing and TensorFlow for running predictive models. A Web API is also used to receive real-time data.

[0601] As a concrete example, when a user uses their car for their morning commute, the system can automatically analyze current traffic information and suggest the optimal commute route. An example of a prompt message is "Suggest an optimized signal timing for the next intersection," and the system will calculate the best solution for signal control in response to this command.

[0602] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0603] Step 1:

[0604] The server receives real-time traffic information from monitoring devices. The monitoring devices use cameras and sensors to acquire data such as traffic volume, vehicle speed, and vehicle flow, and transmit this data to the server. The input is raw data from the monitoring devices, which the server then retrieves.

[0605] Step 2:

[0606] The server preprocesses the received raw traffic data. This preprocessing is done using the Python Pandas library to remove noise and impute missing values. The output of this step is the cleaned traffic data.

[0607] Step 3:

[0608] The server uses the cleaned data to input a machine learning model, which is a generative AI model, to predict traffic flow patterns. This model is trained on historical traffic data and runs using TensorFlow. The output is the predicted traffic pattern.

[0609] Step 4:

[0610] The server calculates the optimal timing for switching traffic signals based on predicted traffic patterns. It dynamically adjusts the control instructions to the signaling devices to ensure smooth vehicle flow at specific intersections. The output is a specific control instruction for the signaling devices.

[0611] Step 5:

[0612] The terminal receives data and predictive information from the server and displays it on the user's terminal. The user's terminal can be a smartphone or an in-car navigation device, and it displays optimal travel routes and traffic information, suggesting a route optimized for the user. The output is visualized information provided to the user.

[0613] Step 6:

[0614] The user selects a travel route based on the information displayed on the terminal and drives according to traffic conditions. This enables safe and efficient travel. The output is the optimal route selected by the user.

[0615] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0616] This invention provides a system that achieves a higher level of traffic flow optimization by incorporating an emotion engine into urban traffic management systems. In this configuration, a monitoring device that acquires traffic conditions, a terminal that controls signal devices, and an emotion engine that recognizes user emotions work in close cooperation.

[0617] The server aggregates real-time traffic information collected through monitoring devices installed throughout the city. This includes data from traffic cameras and sensors, such as the number of vehicles, their speed, and the degree of congestion. This information is stored in a database and used as the basis for analysis.

[0618] The terminal receives this data and uses an artificial intelligence model to predict and analyze traffic flow. In addition, the terminal is equipped with an emotion engine that can collect and analyze emotional data from the user's device. The emotion engine detects the user's emotional state from their voice, facial expressions, or input feedback, and uses this to personalize traffic information.

[0619] Users receive real-time traffic information from a server via their smartphones or in-car devices. This includes personalized routes and traffic information adapted to the user's emotions. For example, if a user is stressed, the emotion engine recognizes this and suggests a different route that prioritizes comfort over the usual route. Or, for users in a hurry, it provides traffic guidance to reach their destination in the shortest possible time.

[0620] As a concrete example, during the morning rush hour, when the server detects a sudden surge in traffic at a specific intersection, the terminal analyzes the data and adjusts the timing of the traffic signals. Simultaneously, the emotion engine evaluates the user's current emotional state and sends the optimal route guidance based on the results to the user's smartphone. As a result, the user can create a travel plan that best suits their situation and emotions, which in turn helps to alleviate traffic congestion and improve traffic flow.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The server collects real-time traffic data from multiple monitoring devices installed in the transportation infrastructure. This collected data includes the number and speed of vehicles and road congestion, and it integrates the data that is periodically transmitted from each monitoring device.

[0624] Step 2:

[0625] The server stores the collected traffic data in a database. This storage process maintains data integrity and consistency while saving it in a format that is easy to use for subsequent analysis.

[0626] Step 3:

[0627] The terminal receives traffic data sent from the server and analyzes that data using an artificial intelligence model. The AI ​​model predicts traffic flow patterns and generates instructions to optimize the timing of signal switching.

[0628] Step 4:

[0629] The emotion engine installed in the device collects emotional data from the user's device. This emotional data is obtained through analysis of the user's voice and facial expressions, or from feedback entered directly.

[0630] Step 5:

[0631] The device uses emotional data analyzed by an emotion engine to generate personalized traffic information and travel routes tailored to the user. This provides personalized guidance so that users can relax and enjoy their journey.

[0632] Step 6:

[0633] The server transmits optimized signal control instructions to signaling devices, adjusting traffic flow in real time. Simultaneously, it provides users with the latest information by transmitting customized traffic data to their terminals.

[0634] Step 7:

[0635] Users act based on personalized traffic guidance received via smartphones or in-car devices. By following this guidance, users can travel to their destination efficiently while reducing stress and anxiety.

[0636] (Example 2)

[0637] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] In modern cities, traffic congestion and road overcrowding are major social challenges, and there is a need for efficient traffic management and comfortable travel for users. At the same time, traffic information systems often neglect to consider the stress and emotional state of individual users. Conventional systems manage traffic flow centrally, but lack the provision of services based on users' emotions. In response to this, there is a need to develop a traffic management system that can provide real-time information adapted to emotions.

[0639] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0640] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, and means for collecting user emotion data and analyzing it using an emotion analysis engine. This makes it possible to provide personalized traffic information that is adapted to the user's emotions.

[0641] A "monitoring device" is a physical device installed on roads and intersections to measure traffic conditions and acquire information in real time.

[0642] "Traffic information" refers to data about traffic conditions, specifically including the number of vehicles, speed, and degree of congestion.

[0643] An "artificial intelligence model" is an algorithm that learns patterns based on data collected in the past and uses them to predict traffic flow.

[0644] An "emotion analysis engine" is a technology for detecting and analyzing a user's emotional state, and it typically utilizes voice and facial expression data.

[0645] "Personalized traffic information" refers to traffic guidance and route information that is customized according to the emotional state and specific needs of individual users.

[0646] This invention aims to optimize real-time information in urban traffic management. Specific examples are shown below.

[0647] The server collects traffic information from monitoring devices installed on roads and intersections within the city. This includes cameras and sensors, and the data is used to measure the number and speed of vehicles, as well as the degree of congestion. The server stores this collected data in a database. The server continuously processes the data in real time, utilizing generative AI models to analyze traffic flow patterns. Frameworks such as TensorFlow and PyTorch are commonly used for this analysis.

[0648] The terminal receives analyzed traffic data transmitted from the server and also collects user emotion data. This emotion data is collected via smartphones and in-vehicle devices, and the user's emotional state can be analyzed using speech recognition and facial expression recognition software. This analysis incorporates an emotion analysis engine, which is used as additional information to find the optimal route for the user's travel.

[0649] Users can receive real-time traffic information adapted to their emotional state via their device. For example, if a user is experiencing high stress levels during the morning rush hour, the system will suggest a route that avoids congestion. It will also provide users in a hurry with the shortest possible route to their destination. This allows users to create travel plans that best suit their emotional state at that moment.

[0650] For example, during the morning rush hour, if traffic volume suddenly increases at a specific intersection, data is collected by a server, and the terminal controls traffic signals accordingly. Furthermore, based on the user's sentiment analysis, the smartphone provides optimal route guidance tailored to their emotions. An example of a prompt message might be, "If the user is feeling stressed during their morning commute, how would you suggest a comfortable route?"

[0651] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0652] Step 1:

[0653] The server collects traffic information from monitoring devices installed throughout the city. The input consists of raw data from traffic cameras and sensors, specifically information such as the number of vehicles, their speed, and congestion levels. This data is collected, organized, and stored in a database. This provides the foundational data needed to understand traffic conditions.

[0654] Step 2:

[0655] The server uses collected traffic information to generate an AI model that analyzes and predicts traffic flow. The input is data from the aforementioned traffic database. The AI ​​model analyzes this data and predicts future traffic flow patterns. As a result, it outputs congestion prediction data for each intersection and road section. This data enables control that anticipates future traffic conditions.

[0656] Step 3:

[0657] The terminal receives predictive data provided by the server and collects emotional data from the user's terminal. Inputs include predictive data from the server and audio / image data from the user's terminal. Using this data, an emotional analysis engine analyzes the user's emotional state. As a result, it outputs data indicating the user's emotional state. This allows for the preparation of personalized traffic guidance for each individual user.

[0658] Step 4:

[0659] The device integrates emotional state data and traffic prediction data to generate personalized traffic information. The input consists of emotional state data and traffic prediction data. Using this data, for example, it suggests a comfortable route for a stressed user and the shortest route for a user in a hurry. This enables adaptive and user-centered traffic guidance.

[0660] Step 5:

[0661] Users receive personalized traffic information from their devices and use it to guide their actual travel. Feedback is sent back to the device from the user. The input is personalized traffic information from the device. Based on user behavior and feedback, the system collects data to improve the accuracy of future directions. This improves the overall accuracy of the system and user satisfaction.

[0662] (Application Example 2)

[0663] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] In modern urban areas, traffic congestion is a daily occurrence, hindering efficient travel. Furthermore, existing traffic management systems, while considering the physical factors of traffic flow, fail to provide personalized traffic guidance that takes into account the emotional state of users. As a result, users often travel while experiencing emotional stress, further worsening overall traffic efficiency. The objective of this invention is to alleviate traffic congestion while providing traffic guidance that takes into account the emotional state of users, thereby achieving comfortable and efficient travel.

[0665] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0666] In this invention, the server includes means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, means for predicting traffic volume and traffic flow patterns using an artificial intelligence model to analyze the received traffic information, means for generating control instructions to traffic control devices based on the prediction results and dynamically adjusting the timing of signal devices, and means for acquiring and analyzing user emotional data and personalizing traffic guidance based on emotional state. This enables dynamic optimization of traffic flow while providing personalized traffic guidance based on the user's emotional state.

[0667] "Traffic conditions" refers to all information indicating the number of vehicles, their speed, the degree of congestion, etc., within a city.

[0668] A "monitoring device" is a device installed to acquire traffic information, such as a traffic camera or sensor.

[0669] "Means for analyzing traffic information" refers to processes and devices used to process received traffic data and understand traffic conditions based on the results.

[0670] An "artificial intelligence model" is a program that runs machine learning and predictive algorithms used to analyze data and predict traffic flow and traffic patterns.

[0671] "Traffic control devices" are equipment used to control the flow of traffic, such as traffic lights and electronic billboards.

[0672] A "control instruction" is a command sent to a traffic control device that adjusts the timing of signals and other traffic management parameters.

[0673] "Means of providing information via communication devices" refers to methods or devices for transmitting information to a user's terminal via a network.

[0674] "Emotional data" refers to data acquired to measure and analyze the emotional state of users.

[0675] An "emotion recognition engine" is a technology that detects and analyzes a user's emotional state from their facial expressions, voice, and input information.

[0676] "Emotionally-based traffic guidance" refers to traffic information and route guidance provided in a way that is adapted to the user's current emotions.

[0677] In this invention, multiple monitoring devices are installed on a server to highly optimize urban traffic management. These monitoring devices include traffic cameras and sensors to acquire real-time traffic information. The server aggregates the traffic information and uses an artificial intelligence model to predict traffic volume and traffic flow patterns. Based on this prediction, control instructions are generated for traffic control devices, and the timing of traffic signals is dynamically adjusted.

[0678] The server is equipped with an emotion recognition engine to acquire and analyze users' emotional data. Users can receive real-time traffic information and personalized traffic guidance based on their emotional state using their smartphones or in-car devices. The emotion recognition engine acquires emotional data from users' voices and facial expressions through speech recognition and image recognition technologies. This data is used in traffic guidance to provide optimal routes and information that match the user's current emotions.

[0679] Specifically, if a user is feeling stressed, the emotion recognition engine will detect this and provide route guidance that prioritizes comfort, such as a route with scenic views. Furthermore, if the system determines that the user is in a hurry, it will suggest a route that will get them to their destination in the shortest possible time. For example, a user who wants to enjoy a relaxing drive with their family on a Sunday afternoon will be offered a route that not only avoids congestion but also allows them to enjoy the scenery. This results in traffic guidance tailored to the user's emotional state, ultimately reducing traffic congestion and improving the overall comfort of travel.

[0680] An example of a prompt message is: "Based on the user's emotional state, suggest the best route from the current location to the destination. If you are relaxed, choose a scenic route; if you are in a hurry, choose the shortest route."

[0681] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0682] Step 1:

[0683] The server acquires real-time traffic information from multiple monitoring devices. Input is raw data from the monitoring devices, while output is the number of vehicles, their speed, and congestion levels collected by traffic cameras and sensors. The data is collected by a program and processed into a standardized format.

[0684] Step 2:

[0685] The server inputs this traffic information into an artificial intelligence model to predict traffic volume and traffic flow patterns. The input here is processed traffic information, and the output is predicted data for future traffic volume and congestion locations. This process utilizes machine learning algorithms, which compare current data with historical data to make predictions.

[0686] Step 3:

[0687] The terminal generates control instructions for the traffic control system based on the prediction results. The input is prediction data, and the output is a control signal that adjusts the timing of the signaling device. The terminal sends commands to the signaling device, dynamically adjusting the timing of the signals to optimize traffic flow.

[0688] Step 4:

[0689] The server collects voice and facial expression data from smartphones and in-car devices via an emotion recognition engine to acquire user emotional data. The input is the user's real-time voice and image data, and the output is analyzed emotional state data. Here, emotions are determined using voice recognition and image recognition technologies.

[0690] Step 5:

[0691] The device generates traffic guidance tailored to the user's emotional state based on analyzed emotional data. Input is emotional state data and current traffic information, and output is personalized route guidance. Based on a generating AI model, it selects routes and information that match the user's preferences.

[0692] Step 6:

[0693] The user receives emotion-based traffic guidance from a server via their device and travels along the optimal route. The input is route guidance from the device, and the output is the user's travel experience. Specifically, by following the route guidance instructions, a stress-free journey is achieved. This process makes it possible to smoothly meet the user's transportation needs according to their circumstances.

[0694] 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.

[0695] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0696] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0697] 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.

[0698] Figure 9 shows an 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.

[0699] 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.

[0700] 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.

[0701] 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, motorcycles, etc., 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, for example, based 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.

[0702] 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."

[0703] 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.

[0704] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0705] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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 the like 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.

[0714] 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.

[0715] The following is further disclosed regarding the embodiments described above.

[0716] (Claim 1)

[0717] A means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions,

[0718] A means for predicting traffic volume and traffic flow patterns using an artificial intelligence model in order to analyze received traffic information,

[0719] A means for generating control instructions to the traffic control device based on prediction results and dynamically adjusting the timing of the signal device,

[0720] A means of providing collected data and predictive information to the user's terminal via a communication device,

[0721] A system that includes this.

[0722] (Claim 2)

[0723] The system according to claim 1, which implements an artificial intelligence model that has learned past traffic patterns in order to optimize traffic flow in real time.

[0724] (Claim 3)

[0725] The system according to claim 1, which predicts the flow of vehicles at a specific intersection and optimizes the timing of signal switching to alleviate traffic congestion.

[0726] "Example 1"

[0727] (Claim 1)

[0728] A means for receiving environmental data from multiple monitoring devices installed to acquire traffic information,

[0729] A means of predicting traffic flow patterns using a generative AI model in order to analyze the received environmental data,

[0730] A means for generating adjustment instructions to the control device based on prediction results and dynamically optimizing the timing of the signal device,

[0731] A means of providing the collected information and prediction results to the user's terminal via a communication device,

[0732] A means of learning from feedback data and improving analysis accuracy,

[0733] A system that includes this.

[0734] (Claim 2)

[0735] The system according to claim 1, which implements a generative AI model that learns past environmental patterns and optimizes traffic flow in real time.

[0736] (Claim 3)

[0737] The system according to claim 1, which predicts the movement of vehicles at specific locations and precisely adjusts the timing of signal operation to alleviate traffic congestion.

[0738] "Application Example 1"

[0739] (Claim 1)

[0740] A means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions,

[0741] A means for predicting traffic volume and traffic flow patterns using an artificial intelligence model in order to analyze received traffic information,

[0742] A means for generating control instructions to the traffic control device based on prediction results and dynamically adjusting the timing of the signal device,

[0743] A means for providing collected data and predictive information to the user's terminal via a communication device and generating an optimized route plan,

[0744] A means of supporting safe and efficient operation by analyzing traffic patterns based on driving data from multiple mobile devices,

[0745] A system that includes this.

[0746] (Claim 2)

[0747] The system according to claim 1, which implements an artificial intelligence model that has learned past traffic patterns in order to optimize traffic flow in real time and proposes a specific travel route.

[0748] (Claim 3)

[0749] The system according to claim 1, which predicts the flow of vehicles at a specific intersection, optimizes the timing of signal switching, and proposes a driving route in order to alleviate traffic congestion.

[0750] "Example 2 of combining an emotion engine"

[0751] (Claim 1)

[0752] A means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions,

[0753] A means for predicting traffic volume and traffic flow patterns using an artificial intelligence model in order to analyze received traffic information,

[0754] A means of collecting user emotion data and analyzing it using an emotion analysis engine,

[0755] A means of generating and providing traffic information adapted to the user's emotions based on the analysis results,

[0756] A means of collecting feedback from user terminals based on the traffic information provided,

[0757] A means to continuously improve the personalization of traffic information based on feedback,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, which optimizes traffic flow in real time and provides route guidance adapted to the emotional state of the user.

[0761] (Claim 3)

[0762] The system according to claim 1, which integrates vehicle flow and user emotions at a specific intersection to optimize the timing of signal switching and the route, in order to alleviate traffic congestion and provide traffic guidance tailored to the different emotional states of users.

[0763] "Application example 2 of combining emotional engines"

[0764] (Claim 1)

[0765] A means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions,

[0766] A means for predicting traffic volume and traffic flow patterns using an artificial intelligence model in order to analyze received traffic information,

[0767] A means for generating control instructions to the traffic control device based on prediction results and dynamically adjusting the timing of the signal device,

[0768] A means of providing collected data and predictive information to the user's terminal via a communication device,

[0769] It includes an emotion recognition engine for acquiring and analyzing user emotional data, and means for personalizing traffic guidance based on emotional state,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, which implements an artificial intelligence model that has learned past traffic patterns in order to optimize traffic flow in real time.

[0773] (Claim 3)

[0774] The system according to claim 1, characterized in that, in order to alleviate traffic congestion, it predicts the flow of vehicles at a specific intersection, optimizes the timing of signal switching, and provides route guidance that takes into account the emotional state of the user. [Explanation of symbols]

[0775] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving traffic information from multiple monitoring devices installed to acquire traffic conditions, A means for predicting traffic volume and traffic flow patterns using an artificial intelligence model in order to analyze received traffic information, A means for generating control instructions to the traffic control device based on prediction results and dynamically adjusting the timing of the signal device, A means of providing collected data and predictive information to the user's terminal via a communication device, A system that includes this.

2. The system according to claim 1, which implements an artificial intelligence model that has learned past traffic patterns in order to optimize traffic flow in real time.

3. The system according to claim 1, which predicts the flow of vehicles at a specific intersection and optimizes the timing of signal switching in order to alleviate traffic congestion.

Images