system

Abstract

We provide the system. [Solution] A means of acquiring traffic data from multiple detection devices placed in an urban environment, A means for analyzing the aforementioned traffic data to predict traffic flow, A means for generating traffic control signals based on the aforementioned prediction and operating a traffic signal device, Means for providing the user's information terminal with a travel route optimized by the traffic control signal, A means for collecting feedback from the aforementioned users and improving the analysis model, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern urban environments, frequent traffic congestion and traffic accidents have become major problems in citizens' lives, causing economic losses and adverse effects on the environment. In addition, existing traffic management systems lack real-time performance and predictive ability, and there is a problem that it is difficult to optimize an efficient traffic flow. There is a need to overcome such constraints and achieve rapid and accurate management of traffic conditions.

Means for Solving the Problems

[0005] This invention provides a system for acquiring traffic data using multiple detection devices installed in an urban environment. The acquired traffic data is analyzed and has a function to predict future traffic flow. Based on this prediction, traffic control signals are generated and traffic signal devices are automatically operated. Furthermore, optimized routes based on these traffic control signals are provided to users' information terminals, enabling users to travel efficiently. In addition, the accuracy of the entire system is improved by collecting feedback from users and continuously refining the analysis model. This makes it possible to analyze traffic conditions in real time and maintain an efficient traffic flow.

[0006] A "detection device" is a device installed in an urban environment to acquire traffic-related data, and includes optical cameras, environmental sensors, and mobile communication devices.

[0007] "Traffic data" refers to a collection of information used to indicate traffic conditions within a city, such as traffic volume, vehicle speed, vehicle density, and traffic signal status.

[0008] A "traffic control signal" is a signal generated to operate traffic signaling equipment, and includes adjustments to the timing and parameters of the signal.

[0009] A "traffic signal system" is a device used to control traffic on a road, and includes signal lights, signal control boxes, and other similar components.

[0010] An "information terminal" is an electronic device owned by a user, such as a smartphone, tablet, or portable computer, that is used for communication and information processing.

[0011] "Feedback" refers to information provided by users to a system, such as opinions, experiences, and satisfaction levels, which is used to improve and optimize the system.

[0012] An "analytical model" is a mathematical or machine learning model used to analyze and predict traffic conditions using acquired traffic data. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single 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.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a 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, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The system implementing this invention is designed to manage urban traffic smoothly and consists of a detection device, a server, a terminal, and a user. The operation of the system will be described in detail below.

[0035] First, detection devices are installed at various locations throughout the city to acquire traffic data such as traffic volume and vehicle speed in real time. This data is transmitted to a server. Furthermore, users' smartphones and other devices provide anonymized location information to the server, supplementing the collection of more detailed traffic data.

[0036] Next, the server centrally analyzes the diverse data collected. Generative AI technology is used in this analysis, enabling not only an understanding of current traffic conditions but also the prediction of future traffic flow. Specifically, it models historically accumulated data and learns trends and patterns to identify locations and times when signal adjustments are necessary.

[0037] The server then generates optimal traffic control signals based on the predicted traffic conditions. These signals are sent to traffic signal devices within the city, and the timing of the lights is automatically adjusted. This reduces waiting times at traffic lights and alleviates congestion. An optimized travel route is also calculated and sent to the user's terminal.

[0038] Users can receive real-time traffic information and optimal routes through a smartphone app. This allows users to avoid congestion and contributes to improving the overall flow of urban traffic. Furthermore, user feedback is sent back to the server and used to further improve the analysis model.

[0039] As an example, when a user sets a destination using a smartphone app while traversing the city center, the server provides the optimal route based on current traffic conditions and forecasts. By following the suggested route, the user can avoid congestion. This improves individual travel convenience and increases overall traffic efficiency in the city.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal acquires data such as traffic flow and vehicle speed in real time through IoT sensors within the city and transmits it to the server. The user's smartphone also collects location information anonymously and prepares to transmit it to the server.

[0043] Step 2:

[0044] The server aggregates the received data, standardizing and integrating information in different formats. This creates a single, comprehensive dataset, laying the foundation for analysis.

[0045] Step 3:

[0046] The server utilizes generative AI to analyze the collected and integrated data. This analysis includes real-time traffic conditions and predictions of future traffic patterns. Anomaly detection is also performed by referencing existing data patterns.

[0047] Step 4:

[0048] Based on the analysis results, the server generates optimal traffic control signals to operate the traffic signal system. These control signals automatically adjust signal timing and set priorities to ensure smooth traffic flow.

[0049] Step 5:

[0050] The terminal transmits control signals to the traffic signal system and performs the specified adjustments. The switching times and sequences of the signal lights are optimized in real time, allowing for immediate response to on-site traffic conditions.

[0051] Step 6:

[0052] The server sends optimized travel route information to the user's information terminal. Personalized route guidance is provided for each user, helping to avoid traffic congestion.

[0053] Step 7:

[0054] Users check the optimal route received through the app and follow the instructions to travel. While traveling, users can utilize features that allow them to check real-time traffic conditions, ensuring a comfortable journey.

[0055] Step 8:

[0056] Users provide feedback through the app after completing their journey. They submit opinions on the system's usability and the effectiveness of suggested routes, which contributes to improving the analysis model on the server.

[0057] (Example 1)

[0058] 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."

[0059] In urban areas, traffic congestion significantly impacts economic activity and daily life, necessitating efficient traffic management. However, current traffic management systems lack the ability to respond in real time or predict future traffic flow adequately, resulting in inefficient signal adjustments and route guidance. Furthermore, while providing traffic information that is relevant to users is crucial, its accuracy is currently insufficient.

[0060] 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.

[0061] In this invention, the server includes means for acquiring dynamic data from multiple detection devices placed in an urban environment, means for using a generative AI model that analyzes the dynamic data and location information data from terminals together to predict trends, and means for generating control signals based on the trend predictions and operating control devices. This makes it possible to optimize traffic flow in real time and provide users with the optimal travel route.

[0062] The term "urban environment" refers to areas and conditions in urban areas where traffic and pedestrian flow are concentrated, and includes spaces where various detection devices and control equipment can be installed.

[0063] A "detection device" is equipment installed to acquire dynamic data such as traffic flow, vehicle speed, and environmental conditions, and includes optical instruments and detection sensors.

[0064] "Dynamic data" refers to data that shows information about movement, such as traffic flow, vehicle speed, and traffic light waiting time.

[0065] A "terminal" refers to a user's portable device that is connected to a communication network and capable of sending and receiving information.

[0066] "Location data" refers to information obtained from a device that indicates its current geographical location.

[0067] A "generative AI model" refers to artificial intelligence technology used to analyze traffic conditions and predict future trends based on data.

[0068] A "control signal" is a signal generated to dynamically operate traffic signal equipment and includes parameters for optimizing traffic flow.

[0069] "Control equipment" refers to signaling devices that operate in response to generated control signals and perform traffic control.

[0070] "Feedback" refers to information collected from users regarding their opinions on the system and their usage results.

[0071] This system is designed to efficiently manage traffic in urban environments. The system primarily functions around three components: servers, terminals, and users.

[0072] The server acquires dynamic data from multiple detection devices placed throughout the city. This data includes traffic flow and vehicle speed. Furthermore, it utilizes anonymized location data received from terminals to enable detailed analysis of traffic conditions. The server uses this data to run a generative AI model, analyzing the current traffic state and predicting future trends. Trend analysis and pattern recognition are used in the analysis to generate optimal control signals.

[0073] The generative AI model learns from historical dynamic data and has the ability to detect specific traffic patterns and anomalies. This model dynamically adjusts the timing of traffic signals to optimize congestion and reduce waiting times. The generated control signals are also transmitted to control equipment within the city, automatically adjusting the timing of traffic signals.

[0074] The terminal refers to the user's smartphone or other device, and it has the function of providing the user with real-time travel route and traffic information transmitted from the server. This information allows the user to select the optimal route based on current traffic conditions and reach their destination efficiently.

[0075] Users navigate based on the provided information and, after use, send feedback regarding traffic information and the provided route back to the server via their device. This feedback contributes to improving the accuracy of the analysis and is used to improve the analysis model for future use.

[0076] As a concrete example, when a user sets a destination while traveling through the city center, the server analyzes all current traffic data and generates the most efficient route. For instance, a prompt such as "Provide the user with the best possible route based on current traffic conditions and forecasts" could be used. This method improves the travel efficiency of individual users and also improves overall traffic flow in the city.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The detection device acquires dynamic data. It senses vehicle speed, traffic flow, etc., at each installation point and transmits this data to a server. The input is actual traffic condition data, and the output is this data being sent to the server.

[0080] Step 2:

[0081] The server receives anonymized location data from the terminal. This data, transmitted from the user's terminal, is used to gain a more detailed understanding of current traffic conditions. The input is location data from the terminal, and the output is the collection of location data on the server.

[0082] Step 3:

[0083] The server analyzes the acquired dynamic and location data. Using a generative AI model, it analyzes traffic conditions based on past data and predicts future traffic flow. Data processing includes trend analysis and pattern recognition, and the output identifies locations where signal adjustments are necessary.

[0084] Step 4:

[0085] The server generates optimized control signals based on the analysis results. It creates control signals containing specific parameters for dynamically adjusting signal timing and transmits them to the relevant traffic signal equipment. The input is the analysis results, and the output is the generated control signals.

[0086] Step 5:

[0087] The server sends the calculated optimal travel route to the user's terminal. It provides the user with the best route, taking into account real-time, fluctuating traffic information. The input is the current traffic conditions and predicted data, and the output is route guidance for the user.

[0088] Step 6:

[0089] Users travel based on the provided route information and select the optimal route for efficient travel. After travel, users send feedback about traffic and routes back to the server via the app. The feedback information is collected on the server and used to improve the accuracy of the analysis.

[0090] (Application Example 1)

[0091] 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."

[0092] Urban traffic congestion leads to decreased mobility efficiency and increased environmental burden. Therefore, there is a need for a system that optimizes traffic flow throughout the urban environment and allows autonomous vehicles to select the optimal route in real time. However, conventional methods have limitations in their ability to collect and analyze traffic data, making it difficult to provide rapid and accurate navigation.

[0093] 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.

[0094] In this invention, the server includes means for acquiring traffic data from multiple detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, and means for generating traffic control signals based on the predictions and operating traffic signal devices. This makes it possible to efficiently analyze traffic information and provide optimal navigation information to an automated mobile vehicle in real time.

[0095] "Urban environment" refers to urban areas and their surrounding regions where a large number of people live and transportation methods are concentrated.

[0096] A "detection device" refers to equipment used to collect traffic data such as traffic flow and vehicle speed.

[0097] "Traffic data" refers to data that includes information such as traffic volume, vehicle speed, and timing of movement.

[0098] "Traffic flow" refers to the flow and movement of traffic within a specific area.

[0099] A "traffic control signal" is a signal generated to operate traffic signals, and is a means of controlling the flow of traffic.

[0100] "Information terminals" refer to digital devices, including smartphones and tablets.

[0101] An "autonomous vehicle" refers to a vehicle equipped with a system that independently selects its own route and moves accordingly.

[0102] "Navigation information" refers to the optimal route to the destination and various instructions regarding operation.

[0103] An "analytical model" is a mathematical or computer simulation model used to predict traffic trends and patterns based on collected data.

[0104] A "wireless communication device" refers to hardware that can send and receive information using radio waves.

[0105] "Signal parameters" refer to the specific settings and conditions used to determine the operation of traffic signals.

[0106] A "generative AI model" is an artificial intelligence model created using machine learning algorithms based on a large amount of data, for predicting traffic flow and optimal routes.

[0107] The system that realizes this invention is intended to improve the efficiency of traffic management in urban environments. It mainly consists of components such as a server, a detection device, an information terminal, and an automated mobile unit.

[0108] The server collects traffic data in real time from multiple detection devices placed throughout the city and predicts traffic flow using a generative AI model. Based on this prediction, the server generates traffic control signals and operates traffic signal devices to optimize traffic flow. The generated control signals include dynamically adjustable signal parameters, allowing for real-time changes to the timing of traffic signals.

[0109] Meanwhile, the information terminal receives optimized route information transmitted from the server and provides this information to the automated vehicle as navigation information. The automated vehicle selects a route in real time according to the received navigation information and operates accordingly. This makes it possible to avoid congestion and travel efficiently.

[0110] Specifically, for automated vehicles passing through busy areas, the server predicts congestion levels and proposes routes. This process involves building generative AI models using programming languages ​​like Python and machine learning frameworks such as TENSORFLOW®. For data processing, traffic data is analyzed using Pandas, and the predictive model is implemented using Scikit-learn.

[0111] Examples of prompts include, "Predict the optimal route during weekday evening rush hour," and "Predict congested areas due to increased events on weekends and suggest routes to avoid them."

[0112] In this way, the server improves the overall traffic efficiency of the city and creates an environment where autonomous vehicles can operate smoothly.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server collects real-time traffic data from multiple detection devices located throughout the city. Specifically, it aggregates information such as vehicle speed and traffic flow via wireless communication devices. At this stage, the input is raw data acquired from each detection device, and the output is traffic data that has been normalized and integrated into an analyzable format.

[0116] Step 2:

[0117] The server uses collected traffic data to run a generative AI model, analyzes current traffic flow, and predicts future congestion points and the timing of traffic jams. Specifically, it inputs data into a model trained on past traffic patterns using Python and TensorFlow, and outputs prediction results. At this stage, normalized traffic data is used as input, and the output is the predicted traffic trend.

[0118] Step 3:

[0119] The server generates traffic control signals based on the prediction results. Specifically, it optimizes signal parameters to control the timing of traffic signals and transmits them to the traffic signaling equipment. Here, the predicted traffic flow results are used as input, and the output is a set of adjusted signal parameters.

[0120] Step 4:

[0121] The terminal transmits optimized route information provided by the server to the automated vehicle. This process involves receiving the route information, integrating it into the internal navigation system, and updating the operational plan. The input is route information from the server, and the output is the automated vehicle's real-time operational route.

[0122] Step 5:

[0123] The user monitors the movement of the automated vehicle based on navigation information received during operation. Furthermore, they send feedback to the server based on the operation results, contributing to the improvement of the analysis model. The input is historical information of the route operated, and the output is feedback information.

[0124] Step 6:

[0125] The server analyzes user feedback to update the generated AI model and build a highly accurate traffic prediction model. Based on this feedback, it optimizes model parameters to improve the accuracy of the next prediction. The input is user feedback data, and the output is the updated AI model.

[0126] 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.

[0127] The system implementing this invention not only improves the efficiency of urban traffic management but also enables mobility support that takes into account the emotional state of users. This system consists of a detection device, a server, a terminal, and an emotion engine.

[0128] First, detection devices are installed within the city to acquire real-time data such as traffic flow and vehicle speed, and transmit this data to a server. User terminals support the acquisition of detailed traffic data by collecting anonymized location information and transmitting it to the server.

[0129] Next, the server centralizes the received traffic data and uses generating AI for analysis. This includes real-time traffic situation recognition and future traffic flow prediction. Furthermore, it dynamically optimizes traffic conditions by generating personalized and optimal traffic control signals for each user and adjusting each traffic signal device.

[0130] On the other hand, the device is equipped with an emotion engine that uses voice and image analysis to determine the user's emotional state. This emotion engine can evaluate the user's stress level and satisfaction level in real time and adjust the suggested travel route based on its output. This function allows, for example, if the user is feeling anxious, to suggest a more relaxing route.

[0131] Users receive optimized travel routes and traffic information from a server via an app on their device, along with information tailored to their emotions. This allows users to enjoy an efficient and comfortable travel experience. After their trip, feedback is sent from their device to the server and used to improve the analysis model and emotion engine.

[0132] As an example, when a user travels through a city during peak hours, the emotion engine detects their stress level and suggests the most suitable route for that state. By choosing a relaxing route and traveling efficiently along a congested path, the user's comfort level is improved. In this way, the system simultaneously optimizes urban traffic and enhances the mental satisfaction of individual users.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The terminal acquires data such as traffic flow and vehicle speed in real time via IoT sensors within the city and transmits it to a server. At the same time, the smartphone app prepares to collect the user's location information anonymized.

[0136] Step 2:

[0137] An emotion engine operates on the device, performing sentiment analysis using the user's voice data and camera images. This allows for real-time determination of the user's stress level and satisfaction level. This information is then used to individually adjust the presentation method of traffic information.

[0138] Step 3:

[0139] The server aggregates traffic data transmitted from terminals to create a unified dataset. This allows for the integration of data from different sources, preparing it for analysis.

[0140] Step 4:

[0141] The server utilizes AI to analyze integrated data, grasp real-time traffic conditions, and predict future traffic flow by referencing past patterns. This creates a foundation for optimizing traffic flow.

[0142] Step 5:

[0143] The server generates optimal traffic control signals based on predicted traffic conditions. These signals are transmitted to the traffic signaling system, which automatically adjusts the signal timing. As the traffic signals change, the traffic flow at the site is dynamically optimized.

[0144] Step 6:

[0145] The server integrates the emotion engine's output with traffic analysis results to calculate a suitable travel route for the user. This route information is adjusted to take the user's emotional state into account and then sent to the terminal.

[0146] Step 7:

[0147] The user checks an optimized travel route on a smartphone app on their device and travels according to the suggested route. During travel, the device continuously monitors the user's emotional changes using an emotion engine and receives instructions from the server as needed.

[0148] Step 8:

[0149] After arriving at their destination, users submit feedback about their travel experience through the app. This feedback is collected on a server and contributes to improving the analysis model and emotion engine.

[0150] (Example 2)

[0151] 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".

[0152] In modern urban environments, traffic congestion and the resulting stress and discomfort place a significant burden on users. Therefore, there is a need to efficiently manage traffic conditions while suggesting travel routes that take into account users' emotional states. However, existing traffic management systems do not adequately consider individual emotional states when suggesting travel routes, and there is room for improvement. To solve this problem, a new system is needed that combines the analysis of traffic data with the analysis of users' emotional states.

[0153] 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.

[0154] In this invention, the server includes means for acquiring traffic data from a plurality of detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, and means for evaluating the emotional state of users using acoustic and video analysis and optimizing travel routes based on the evaluation. This enables efficient traffic management as well as optimal travel suggestions that are appropriate to the emotional state of users.

[0155] A "detection device" is a device placed in an urban environment to acquire data such as traffic flow and vehicle speed.

[0156] "Traffic data" refers to a collection of information that represents traffic conditions, such as traffic volume and vehicle speed.

[0157] "Traffic flow" refers to the phenomenon of movement of vehicles and people within a certain area.

[0158] A "traffic control signal" is a signal transmitted to operate traffic signaling equipment and is used to regulate the flow of traffic.

[0159] "Acoustic and visual analysis" refers to analytical methods used to evaluate a user's emotional state from audio and visual data.

[0160] "Emotional state" refers to the mental state experienced by users, including stress levels and satisfaction levels.

[0161] "Feedback" refers to opinions and evaluations based on the travel experience provided by users.

[0162] An "analytical model" is a computational method or algorithm that analyzes traffic data and emotional states to predict traffic conditions and travel routes.

[0163] The "emotion engine" is a component within the system that evaluates the user's emotional state in real time through acoustic and visual analysis and reflects this in the suggestion of travel routes.

[0164] The system implementing this invention aims to streamline traffic management in urban environments and support travel that takes into account the emotional state of users. This system consists of a detection device, a server, a terminal, and an emotion engine.

[0165] The server receives traffic data from multiple detection devices located throughout the city. These detection devices include optical devices for measuring traffic flow and vehicle speed, as well as environmental monitoring devices. The server centrally manages this data and analyzes traffic flow in real time using a generative AI model. This analysis includes recognizing the current traffic situation and predicting future traffic. Based on the analysis data, the server generates traffic control signals and dynamically operates traffic signaling systems.

[0166] The device incorporates an emotion engine that analyzes the user's voice and video data in real time. This process allows the device to assess the user's emotional state and determine their stress level and satisfaction level. Based on this, the server suggests a travel route optimized for the user. For example, a user experiencing stress might be offered a relaxing route.

[0167] The user receives an optimized travel route provided by the server via their device and begins their journey. This process ensures a smooth and comfortable travel experience. After the journey, feedback based on the user's experience is collected and sent to the server. This feedback is used to improve the analysis model and the emotion engine.

[0168] As a concrete example, when a user enters a prompt such as "Please suggest a route that takes my current emotional state into account" into their device, the system uses acoustic and visual analysis to evaluate the user's emotions and then suggests the optimal travel route based on that evaluation. In this way, the system simultaneously achieves efficient traffic management and improved individual satisfaction.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The server collects traffic data from detection devices placed throughout the city. It receives traffic flow and vehicle speed data transmitted from these devices as input. This data is acquired from optical and environmental monitoring devices and transmitted to the server in real time. The server centralizes this data and converts it into a format necessary for future analysis. The output is an integrated traffic dataset.

[0172] Step 2:

[0173] The server uses aggregated traffic data to perform analysis with a generative AI model. It uses an integrated traffic dataset as input to analyze traffic flow. This analysis includes current traffic awareness and future traffic prediction. The AI ​​model applies pattern recognition algorithms to predict changes in traffic flow from the data. The output provides real-time traffic awareness results and predicted data.

[0174] Step 3:

[0175] The server generates traffic control signals based on the analysis results and transmits them to the traffic signal equipment. It takes traffic prediction data output by the AI ​​model as input. Based on this data, it optimizes the signal plan and generates control signals to operate the traffic signal equipment. The output is dynamically adjusted traffic control signals.

[0176] Step 4:

[0177] The device analyzes the user's voice and video data and uses an emotion engine to evaluate the user's emotional state. It receives voice recordings and video capture data as input. The emotion engine analyzes this data and calculates emotional metrics such as stress levels and satisfaction levels in real time. The output is an evaluation of the user's emotional state.

[0178] Step 5:

[0179] The server proposes an optimized travel route that takes the user's emotional state into consideration. It takes real-time traffic recognition results and user emotional state evaluation results as input. It then integrates these and runs an algorithm to calculate the optimal travel route for the user. The output is a travel route adjusted based on emotions.

[0180] Step 6:

[0181] The user receives suggested routes via their terminal and then proceeds. As input, route suggestions from the server are displayed on the terminal. The user reviews and selects the suggested route. As output, navigation begins, assisting the user's movement.

[0182] Step 7:

[0183] Users provide feedback on their experience after their journey. As input, they enter their opinions and evaluations of the journey experience into a terminal. This feedback is sent to the server and used to improve the analysis model and sentiment engine. As output, data is obtained that contributes to updating the server's algorithms and models.

[0184] (Application Example 2)

[0185] 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".

[0186] Traffic management in modern cities requires not only ensuring efficient traffic flow but also providing mobility support that considers the mental comfort of users. However, conventional traffic management systems primarily focus on generating traffic control signals based on real-time traffic data and optimizing traffic flow, and fail to provide personalized travel experiences that take into account the emotional state of users. Therefore, it is necessary to provide users with optimal travel routes that avoid traffic congestion and stressful routes, allowing them to relax.

[0187] 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.

[0188] In this invention, the server includes means for acquiring traffic data from a plurality of detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, means for generating traffic control signals and operating traffic signal devices based on the prediction, means for detecting the emotional state of the user and adjusting the travel route according to that state, and means for collecting feedback from the user and improving the analysis model. This makes it possible not only to optimize traffic flow in an urban environment but also to provide a personalized travel experience that responds to the user's emotions.

[0189] A "detection device" is a device installed in an urban environment to acquire traffic data, and includes optical cameras, environmental sensors, voice acquisition devices, and data communication devices.

[0190] "Traffic data" refers to various types of information related to traffic flow, such as traffic volume and vehicle speed.

[0191] "Traffic flow" refers to the state and patterns of movement of vehicles and people in an urban environment.

[0192] A "traffic control signal" is a signal generated to regulate traffic flow by operating traffic signaling equipment.

[0193] "Emotional state" refers to the psychological or emotional state of a user, such as their stress level or satisfaction level.

[0194] "Travel route" refers to the path a user takes to reach their destination.

[0195] "Personalization" refers to adjusting or optimizing a service according to the individual characteristics and circumstances of the user.

[0196] "Feedback" refers to information such as responses, reactions, and opinions collected from users.

[0197] The system implementing this invention simultaneously achieves improved efficiency in urban traffic management and mobility support tailored to the emotional state of users.

[0198] The server acquires traffic data from multiple detection devices placed throughout the city and predicts traffic flow in real time. These detection devices include optical cameras, environmental sensors, and voice acquisition devices, which collect data such as traffic volume and vehicle speed. This data is transmitted to the server via data communication devices, where it is analyzed using a generative AI model. Based on the analysis results, the server generates traffic control signals to be sent to each traffic signal device, dynamically optimizing traffic flow.

[0199] Meanwhile, the terminal is equipped with an emotion engine to evaluate the user's emotional state. This engine uses camera-based facial recognition technology (utilizing OpenCV and TensorFlow) and speech analysis technology (using a speech acquisition device and IBM Watson's Speech to Text API) to evaluate the user's stress level and satisfaction level in real time. Based on the evaluation results, the server provides the user with the optimal travel route and presents it on the user's information terminal.

[0200] Users can move efficiently and comfortably within the city by following the optimized travel routes provided. After the journey, feedback from the user is sent to the server via an information terminal and used to improve the analysis model. For example, if the user is feeling stressed, the server uses generative AI to analyze emotional state data and provides a recommended route using a prompt message such as, "Based on the user's emotional data, please suggest the most relaxing route."

[0201] Thus, this system incorporates advanced data analysis and personalization features that respond to the emotional state of users, embodying a novel technology that balances smooth traffic flow with the mental satisfaction of individual users.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The server receives traffic data from detection devices placed throughout the city. The input consists of traffic flow and vehicle speed data acquired via optical cameras, environmental sensors, and voice acquisition devices. This data is transmitted to the server via a data communication device to generate and store an initial traffic condition dataset.

[0205] Step 2:

[0206] The server uses received traffic data to predict traffic flow. It performs an analysis process using a generative AI model on the input traffic data, thereby generating predicted data for traffic flow and congestion.

[0207] Step 3:

[0208] The server generates traffic control signals based on predicted traffic flow. The input is predicted traffic flow data, and the server outputs signals to operate traffic signaling devices, such as optical or electronic signals, based on the analysis results. This attempts to optimize traffic flow.

[0209] Step 4:

[0210] The device collects user emotion data. It uses image data acquired from the camera and audio data from the microphone as input, and analyzes the images and audio using OpenCV, TensorFlow, and IBM Watson's Speech to Text API. This allows it to output data indicating the user's emotional state.

[0211] Step 5:

[0212] The server provides the optimal travel route based on the user's emotional state. The input is the user's emotional state data, and a generative AI model is used to generate the prompt message "Based on the user's emotional data, please suggest the most relaxing route." Based on this prompt, the server identifies a travel route suitable for the user and outputs that information.

[0213] Step 6:

[0214] The user begins moving according to the provided route. Based on the route information transmitted from the server, the user moves according to the navigation displayed on their information terminal. During this process, feedback on the movement is collected as input by the terminal, this feedback data is output, and finally sent to the server.

[0215] Step 7:

[0216] The server uses the feedback obtained after travel to improve its analysis models. It analyzes the feedback data as input and retrains the generative AI model and emotion engine to improve their performance. This enables more accurate traffic prediction and personalization.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] The system implementing this invention is designed to manage urban traffic smoothly and consists of a detection device, a server, a terminal, and a user. The operation of the system will be described in detail below.

[0234] First, detection devices are installed at various locations throughout the city to acquire traffic data such as traffic volume and vehicle speed in real time. This data is transmitted to a server. Furthermore, users' smartphones and other devices provide anonymized location information to the server, supplementing the collection of more detailed traffic data.

[0235] Next, the server centrally analyzes the diverse data collected. Generative AI technology is used in this analysis, enabling not only an understanding of current traffic conditions but also the prediction of future traffic flow. Specifically, it models historically accumulated data and learns trends and patterns to identify locations and times when signal adjustments are necessary.

[0236] The server then generates optimal traffic control signals based on the predicted traffic conditions. These signals are sent to traffic signal devices within the city, and the timing of the lights is automatically adjusted. This reduces waiting times at traffic lights and alleviates congestion. An optimized travel route is also calculated and sent to the user's terminal.

[0237] Users can receive real-time traffic information and optimal routes through a smartphone app. This allows users to avoid congestion and contributes to improving the overall flow of urban traffic. Furthermore, user feedback is sent back to the server and used to further improve the analysis model.

[0238] As an example, when a user sets a destination using a smartphone app while traversing the city center, the server provides the optimal route based on current traffic conditions and forecasts. By following the suggested route, the user can avoid congestion. This improves individual travel convenience and increases overall traffic efficiency in the city.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The terminal acquires data such as traffic flow and vehicle speed in real time through IoT sensors within the city and transmits it to the server. The user's smartphone also collects location information anonymously and prepares to transmit it to the server.

[0242] Step 2:

[0243] The server aggregates the received data, standardizing and integrating information in different formats. This creates a single, comprehensive dataset, laying the foundation for analysis.

[0244] Step 3:

[0245] The server utilizes generative AI to analyze the collected and integrated data. This analysis includes real-time traffic conditions and predictions of future traffic patterns. Anomaly detection is also performed by referencing existing data patterns.

[0246] Step 4:

[0247] Based on the analysis results, the server generates optimal traffic control signals to operate the traffic signal system. These control signals automatically adjust signal timing and set priorities to ensure smooth traffic flow.

[0248] Step 5:

[0249] The terminal transmits control signals to the traffic signal system and performs the specified adjustments. The switching times and sequences of the signal lights are optimized in real time, allowing for immediate response to on-site traffic conditions.

[0250] Step 6:

[0251] The server sends optimized travel route information to the user's information terminal. Personalized route guidance is provided for each user, helping to avoid traffic congestion.

[0252] Step 7:

[0253] Users check the optimal route received through the app and follow the instructions to travel. While traveling, users can utilize features that allow them to check real-time traffic conditions, ensuring a comfortable journey.

[0254] Step 8:

[0255] Users provide feedback through the app after completing their journey. They submit opinions on the system's usability and the effectiveness of suggested routes, which contributes to improving the analysis model on the server.

[0256] (Example 1)

[0257] 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."

[0258] In urban areas, traffic congestion significantly impacts economic activity and daily life, necessitating efficient traffic management. However, current traffic management systems lack the ability to respond in real time or predict future traffic flow adequately, resulting in inefficient signal adjustments and route guidance. Furthermore, while providing traffic information that is relevant to users is crucial, its accuracy is currently insufficient.

[0259] 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.

[0260] In this invention, the server includes means for acquiring dynamic data from multiple detection devices placed in an urban environment, means for using a generative AI model that analyzes the dynamic data and location information data from terminals together to predict trends, and means for generating control signals based on the trend predictions and operating control devices. This makes it possible to optimize traffic flow in real time and provide users with the optimal travel route.

[0261] The term "urban environment" refers to areas and conditions in urban areas where traffic and pedestrian flow are concentrated, and includes spaces where various detection devices and control equipment can be installed.

[0262] A "detection device" is equipment installed to acquire dynamic data such as traffic flow, vehicle speed, and environmental conditions, and includes optical instruments and detection sensors.

[0263] "Dynamic data" refers to data that shows information about movement, such as traffic flow, vehicle speed, and traffic light waiting time.

[0264] A "terminal" refers to a user's portable device that is connected to a communication network and capable of sending and receiving information.

[0265] "Location data" refers to information obtained from a device that indicates its current geographical location.

[0266] A "generative AI model" refers to artificial intelligence technology used to analyze traffic conditions and predict future trends based on data.

[0267] A "control signal" is a signal generated to dynamically operate traffic signal equipment and includes parameters for optimizing traffic flow.

[0268] "Control equipment" refers to signaling devices that operate in response to generated control signals and perform traffic control.

[0269] "Feedback" refers to information collected from users regarding their opinions on the system and their usage results.

[0270] This system is designed to efficiently manage traffic in urban environments. The system primarily functions around three components: servers, terminals, and users.

[0271] The server acquires dynamic data from multiple detection devices placed throughout the city. This data includes traffic flow and vehicle speed. Furthermore, it utilizes anonymized location data received from terminals to enable detailed analysis of traffic conditions. The server uses this data to run a generative AI model, analyzing the current traffic state and predicting future trends. Trend analysis and pattern recognition are used in the analysis to generate optimal control signals.

[0272] The generative AI model learns from historical dynamic data and has the ability to detect specific traffic patterns and anomalies. This model dynamically adjusts the timing of traffic signals to optimize congestion and reduce waiting times. The generated control signals are also transmitted to control equipment within the city, automatically adjusting the timing of traffic signals.

[0273] The terminal refers to the user's smartphone or other device, and it has the function of providing the user with real-time travel route and traffic information transmitted from the server. This information allows the user to select the optimal route based on current traffic conditions and reach their destination efficiently.

[0274] Users navigate based on the provided information and, after use, send feedback regarding traffic information and the provided route back to the server via their device. This feedback contributes to improving the accuracy of the analysis and is used to improve the analysis model for future use.

[0275] As a concrete example, when a user sets a destination while traveling through the city center, the server analyzes all current traffic data and generates the most efficient route. For instance, a prompt such as "Provide the user with the best possible route based on current traffic conditions and forecasts" could be used. This method improves the travel efficiency of individual users and also improves overall traffic flow in the city.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The detection device acquires dynamic data. It senses vehicle speed, traffic flow, etc., at each installation point and transmits this data to a server. The input is actual traffic condition data, and the output is this data being sent to the server.

[0279] Step 2:

[0280] The server receives anonymized location information data from the terminal. This data transmitted from the user's terminal is used to better understand the current traffic situation. The input is the location information data from the terminal, and the output is the accumulation of location information on the server.

[0281] Step 3:

[0282] The server analyzes the acquired dynamic data and location information data. Using the generated AI model, it analyzes the traffic situation based on past data and predicts the future traffic flow. As data processing, trend analysis and pattern recognition are performed, and as output, the locations where signal adjustment is required are identified.

[0283] Step 4:

[0284] The server generates an optimized control signal based on the analysis results. It creates a control signal including specific parameters for dynamically adjusting the signal timing and transmits it to the relevant traffic signal devices. The input is the analysis result, and the output is the generation of the control signal.

[0285] Step 5:

[0286] The server transmits the calculated optimal travel route to the user's terminal. Considering the real-time changing traffic information, it provides the user with an optimal route. The input is the current traffic situation and prediction data, and the output is the route guidance for the user.

[0287] Step 6:

[0288] The user moves based on the provided route information and selects the optimal route to achieve efficient movement. After moving, the user returns feedback on traffic and routes to the server via the app. As output, the feedback information is accumulated on the server and used to improve the analysis accuracy.

[0289] (Application Example 1)

[0290] 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."

[0291] Urban traffic congestion leads to decreased mobility efficiency and increased environmental burden. Therefore, there is a need for a system that optimizes traffic flow throughout the urban environment and allows autonomous vehicles to select the optimal route in real time. However, conventional methods have limitations in their ability to collect and analyze traffic data, making it difficult to provide rapid and accurate navigation.

[0292] 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.

[0293] In this invention, the server includes means for acquiring traffic data from multiple detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, and means for generating traffic control signals based on the predictions and operating traffic signal devices. This makes it possible to efficiently analyze traffic information and provide optimal navigation information to an automated mobile vehicle in real time.

[0294] "Urban environment" refers to urban areas and their surrounding regions where a large number of people live and transportation methods are concentrated.

[0295] A "detection device" refers to equipment used to collect traffic data such as traffic flow and vehicle speed.

[0296] "Traffic data" refers to data that includes information such as traffic volume, vehicle speed, and timing of movement.

[0297] "Traffic flow" refers to the flow and movement of traffic within a specific area.

[0298] A "traffic control signal" is a signal generated to operate traffic signals, and is a means of controlling the flow of traffic.

[0299] The "information terminal" refers to a digital device, including smartphones and tablets.

[0300] The "autonomous mobile body" refers to a vehicle equipped with a system that independently selects a route and moves.

[0301] The "navigation information" refers to various instruction information regarding the optimal route to the destination and operation.

[0302] The "analysis model" is a mathematical or computer simulation model for predicting traffic trends and patterns based on the collected data.

[0303] The "wireless communication device" refers to hardware that can transmit and receive information using radio waves.

[0304] The "signal parameter" refers to specific setting values and conditions used to determine the operation of traffic signals.

[0305] The "generative AI model" is an artificial intelligence model for predicting traffic flow and optimal routes, generated using machine learning algorithms based on a large amount of data.

[0306] The system that realizes this invention is for efficient traffic management in an urban environment. It mainly consists of components such as a server, a detection device, an information terminal, and an autonomous mobile body.

[0307] The server collects traffic data in real time from a plurality of detection devices arranged in the city, and predicts traffic flow using the generative AI model. Based on this prediction, the server generates traffic control signals and operates traffic signal devices to optimize the traffic flow. The generated control signals include signal parameters that are dynamically adjusted, and can change the timing of traffic signals in real time.

[0308] Meanwhile, the information terminal receives optimized route information transmitted from the server and provides this information to the automated vehicle as navigation information. The automated vehicle selects a route in real time according to the received navigation information and operates accordingly. This makes it possible to avoid congestion and travel efficiently.

[0309] Specifically, for automated vehicles traveling through busy areas, the server predicts congestion levels and proposes routes. This process involves building generative AI models using programming languages ​​like Python and machine learning frameworks such as TensorFlow. For data processing, traffic data is analyzed using Pandas, and the predictive model is implemented using Scikit-learn.

[0310] Examples of prompts include, "Predict the optimal route during weekday evening rush hour," and "Predict congested areas due to increased events on weekends and suggest routes to avoid them."

[0311] In this way, the server improves the overall traffic efficiency of the city and creates an environment where autonomous vehicles can operate smoothly.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The server collects real-time traffic data from multiple detection devices located throughout the city. Specifically, it aggregates information such as vehicle speed and traffic flow via wireless communication devices. At this stage, the input is raw data acquired from each detection device, and the output is traffic data that has been normalized and integrated into an analyzable format.

[0315] Step 2:

[0316] The server uses collected traffic data to run a generative AI model, analyzes current traffic flow, and predicts future congestion points and the timing of traffic jams. Specifically, it inputs data into a model trained on past traffic patterns using Python and TensorFlow, and outputs prediction results. At this stage, normalized traffic data is used as input, and the output is the predicted traffic trend.

[0317] Step 3:

[0318] The server generates traffic control signals based on the prediction results. Specifically, it optimizes signal parameters to control the timing of traffic signals and transmits them to the traffic signaling equipment. Here, the predicted traffic flow results are used as input, and the output is a set of adjusted signal parameters.

[0319] Step 4:

[0320] The terminal transmits optimized route information provided by the server to the automated vehicle. This process involves receiving the route information, integrating it into the internal navigation system, and updating the operational plan. The input is route information from the server, and the output is the automated vehicle's real-time operational route.

[0321] Step 5:

[0322] The user monitors the movement of the automated vehicle based on navigation information received during operation. Furthermore, they send feedback to the server based on the operation results, contributing to the improvement of the analysis model. The input is historical information of the route operated, and the output is feedback information.

[0323] Step 6:

[0324] The server analyzes user feedback to update the generated AI model and build a highly accurate traffic prediction model. Based on this feedback, it optimizes model parameters to improve the accuracy of the next prediction. The input is user feedback data, and the output is the updated AI model.

[0325] 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.

[0326] The system implementing this invention not only improves the efficiency of urban traffic management but also enables mobility support that takes into account the emotional state of users. This system consists of a detection device, a server, a terminal, and an emotion engine.

[0327] First, detection devices are installed within the city to acquire real-time data such as traffic flow and vehicle speed, and transmit this data to a server. User terminals support the acquisition of detailed traffic data by collecting anonymized location information and transmitting it to the server.

[0328] Next, the server centralizes the received traffic data and uses generating AI for analysis. This includes real-time traffic situation recognition and future traffic flow prediction. Furthermore, it dynamically optimizes traffic conditions by generating personalized and optimal traffic control signals for each user and adjusting each traffic signal device.

[0329] On the other hand, the device is equipped with an emotion engine that uses voice and image analysis to determine the user's emotional state. This emotion engine can evaluate the user's stress level and satisfaction level in real time and adjust the suggested travel route based on its output. This function allows, for example, if the user is feeling anxious, to suggest a more relaxing route.

[0330] Users receive optimized travel routes and traffic information from a server via an app on their device, along with information tailored to their emotions. This allows users to enjoy an efficient and comfortable travel experience. After their trip, feedback is sent from their device to the server and used to improve the analysis model and emotion engine.

[0331] As an example, when a user travels through a city during peak hours, the emotion engine detects their stress level and suggests the most suitable route for that state. By choosing a relaxing route and traveling efficiently along a congested path, the user's comfort level is improved. In this way, the system simultaneously optimizes urban traffic and enhances the mental satisfaction of individual users.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The terminal acquires data such as traffic flow and vehicle speed in real time via IoT sensors within the city and transmits it to a server. At the same time, the smartphone app prepares to collect the user's location information anonymized.

[0335] Step 2:

[0336] An emotion engine operates on the device, performing sentiment analysis using the user's voice data and camera images. This allows for real-time determination of the user's stress level and satisfaction level. This information is then used to individually adjust the presentation method of traffic information.

[0337] Step 3:

[0338] The server aggregates traffic data transmitted from terminals to create a unified dataset. This allows for the integration of data from different sources, preparing it for analysis.

[0339] Step 4:

[0340] The server utilizes AI to analyze integrated data, grasp real-time traffic conditions, and predict future traffic flow by referencing past patterns. This creates a foundation for optimizing traffic flow.

[0341] Step 5:

[0342] The server generates optimal traffic control signals based on predicted traffic conditions. These signals are transmitted to the traffic signaling system, which automatically adjusts the signal timing. As the traffic signals change, the traffic flow at the site is dynamically optimized.

[0343] Step 6:

[0344] The server integrates the emotion engine's output with traffic analysis results to calculate a suitable travel route for the user. This route information is adjusted to take the user's emotional state into account and then sent to the terminal.

[0345] Step 7:

[0346] The user checks an optimized travel route on a smartphone app on their device and travels according to the suggested route. During travel, the device continuously monitors the user's emotional changes using an emotion engine and receives instructions from the server as needed.

[0347] Step 8:

[0348] After arriving at their destination, users submit feedback about their travel experience through the app. This feedback is collected on a server and contributes to improving the analysis model and emotion engine.

[0349] (Example 2)

[0350] 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".

[0351] In modern urban environments, traffic congestion and the resulting stress and discomfort place a significant burden on users. Therefore, there is a need to efficiently manage traffic conditions while suggesting travel routes that take into account users' emotional states. However, existing traffic management systems do not adequately consider individual emotional states when suggesting travel routes, and there is room for improvement. To solve this problem, a new system is needed that combines the analysis of traffic data with the analysis of users' emotional states.

[0352] 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.

[0353] In this invention, the server includes means for acquiring traffic data from a plurality of detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, and means for evaluating the emotional state of users using acoustic and video analysis and optimizing travel routes based on the evaluation. This enables efficient traffic management as well as optimal travel suggestions that are appropriate to the emotional state of users.

[0354] A "detection device" is a device placed in an urban environment to acquire data such as traffic flow and vehicle speed.

[0355] "Traffic data" refers to a collection of information that represents traffic conditions, such as traffic volume and vehicle speed.

[0356] "Traffic flow" refers to the phenomenon of movement of vehicles and people within a certain area.

[0357] A "traffic control signal" is a signal transmitted to operate traffic signaling equipment and is used to regulate the flow of traffic.

[0358] "Acoustic and visual analysis" refers to analytical methods used to evaluate a user's emotional state from audio and visual data.

[0359] "Emotional state" refers to the mental state experienced by users, including stress levels and satisfaction levels.

[0360] "Feedback" refers to opinions and evaluations based on the travel experience provided by users.

[0361] An "analytical model" is a computational method or algorithm that analyzes traffic data and emotional states to predict traffic conditions and travel routes.

[0362] The "emotion engine" is a component within the system that evaluates the user's emotional state in real time through acoustic and visual analysis and reflects this in the suggestion of travel routes.

[0363] The system implementing this invention aims to streamline traffic management in urban environments and support travel that takes into account the emotional state of users. This system consists of a detection device, a server, a terminal, and an emotion engine.

[0364] The server receives traffic data from multiple detection devices located throughout the city. These detection devices include optical devices for measuring traffic flow and vehicle speed, as well as environmental monitoring devices. The server centrally manages this data and analyzes traffic flow in real time using a generative AI model. This analysis includes recognizing the current traffic situation and predicting future traffic. Based on the analysis data, the server generates traffic control signals and dynamically operates traffic signaling systems.

[0365] The device incorporates an emotion engine that analyzes the user's voice and video data in real time. This process allows the device to assess the user's emotional state and determine their stress level and satisfaction level. Based on this, the server suggests a travel route optimized for the user. For example, a user experiencing stress might be offered a relaxing route.

[0366] The user receives an optimized travel route provided by the server via their device and begins their journey. This process ensures a smooth and comfortable travel experience. After the journey, feedback based on the user's experience is collected and sent to the server. This feedback is used to improve the analysis model and the emotion engine.

[0367] As a concrete example, when a user enters a prompt such as "Please suggest a route that takes my current emotional state into account" into their device, the system uses acoustic and visual analysis to evaluate the user's emotions and then suggests the optimal travel route based on that evaluation. In this way, the system simultaneously achieves efficient traffic management and improved individual satisfaction.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The server collects traffic data from detection devices placed throughout the city. It receives traffic flow and vehicle speed data transmitted from these devices as input. This data is acquired from optical and environmental monitoring devices and transmitted to the server in real time. The server centralizes this data and converts it into a format necessary for future analysis. The output is an integrated traffic dataset.

[0371] Step 2:

[0372] The server uses aggregated traffic data to perform analysis with a generative AI model. It uses an integrated traffic dataset as input to analyze traffic flow. This analysis includes current traffic awareness and future traffic prediction. The AI ​​model applies pattern recognition algorithms to predict changes in traffic flow from the data. The output provides real-time traffic awareness results and predicted data.

[0373] Step 3:

[0374] The server generates traffic control signals based on the analysis results and transmits them to the traffic signal equipment. It takes traffic prediction data output by the AI ​​model as input. Based on this data, it optimizes the signal plan and generates control signals to operate the traffic signal equipment. The output is dynamically adjusted traffic control signals.

[0375] Step 4:

[0376] The device analyzes the user's voice and video data and uses an emotion engine to evaluate the user's emotional state. It receives voice recordings and video capture data as input. The emotion engine analyzes this data and calculates emotional metrics such as stress levels and satisfaction levels in real time. The output is an evaluation of the user's emotional state.

[0377] Step 5:

[0378] The server proposes an optimized travel route that takes the user's emotional state into consideration. It takes real-time traffic recognition results and user emotional state evaluation results as input. It then integrates these and runs an algorithm to calculate the optimal travel route for the user. The output is a travel route adjusted based on emotions.

[0379] Step 6:

[0380] The user receives suggested routes via their terminal and then proceeds. As input, route suggestions from the server are displayed on the terminal. The user reviews and selects the suggested route. As output, navigation begins, assisting the user's movement.

[0381] Step 7:

[0382] Users provide feedback on their experience after their journey. As input, they enter their opinions and evaluations of the journey experience into a terminal. This feedback is sent to the server and used to improve the analysis model and sentiment engine. As output, data is obtained that contributes to updating the server's algorithms and models.

[0383] (Application Example 2)

[0384] 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."

[0385] Traffic management in modern cities requires not only ensuring efficient traffic flow but also providing mobility support that considers the mental comfort of users. However, conventional traffic management systems primarily focus on generating traffic control signals based on real-time traffic data and optimizing traffic flow, and fail to provide personalized travel experiences that take into account the emotional state of users. Therefore, it is necessary to provide users with optimal travel routes that avoid traffic congestion and stressful routes, allowing them to relax.

[0386] 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.

[0387] In this invention, the server includes means for acquiring traffic data from a plurality of detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, means for generating traffic control signals and operating traffic signal devices based on the prediction, means for detecting the emotional state of the user and adjusting the travel route according to that state, and means for collecting feedback from the user and improving the analysis model. This makes it possible not only to optimize traffic flow in an urban environment but also to provide a personalized travel experience that responds to the user's emotions.

[0388] A "detection device" is a device installed in an urban environment to acquire traffic data, and includes optical cameras, environmental sensors, voice acquisition devices, and data communication devices.

[0389] "Traffic data" refers to various types of information related to traffic flow, such as traffic volume and vehicle speed.

[0390] "Traffic flow" refers to the state and patterns of movement of vehicles and people in an urban environment.

[0391] A "traffic control signal" is a signal generated to regulate traffic flow by operating traffic signaling equipment.

[0392] "Emotional state" refers to the psychological or emotional state of a user, such as their stress level or satisfaction level.

[0393] "Travel route" refers to the path a user takes to reach their destination.

[0394] "Personalization" refers to adjusting or optimizing a service according to the individual characteristics and circumstances of the user.

[0395] "Feedback" refers to information such as responses, reactions, and opinions collected from users.

[0396] The system implementing this invention simultaneously achieves improved efficiency in urban traffic management and mobility support tailored to the emotional state of users.

[0397] The server acquires traffic data from multiple detection devices placed throughout the city and predicts traffic flow in real time. These detection devices include optical cameras, environmental sensors, and voice acquisition devices, which collect data such as traffic volume and vehicle speed. This data is transmitted to the server via data communication devices, where it is analyzed using a generative AI model. Based on the analysis results, the server generates traffic control signals to be sent to each traffic signal device, dynamically optimizing traffic flow.

[0398] Meanwhile, the terminal is equipped with an emotion engine to evaluate the user's emotional state. This engine uses camera-based facial recognition technology (utilizing OpenCV and TensorFlow) and speech analysis technology (using a speech acquisition device and IBM Watson's Speech to Text API) to evaluate the user's stress level and satisfaction level in real time. Based on the evaluation results, the server provides the user with the optimal travel route and presents it on the user's information terminal.

[0399] Users can move efficiently and comfortably within the city by following the optimized travel routes provided. After the journey, feedback from the user is sent to the server via an information terminal and used to improve the analysis model. For example, if the user is feeling stressed, the server uses generative AI to analyze emotional state data and provides a recommended route using a prompt message such as, "Based on the user's emotional data, please suggest the most relaxing route."

[0400] Thus, this system incorporates advanced data analysis and personalization features that respond to the emotional state of users, embodying a novel technology that balances smooth traffic flow with the mental satisfaction of individual users.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The server receives traffic data from detection devices placed throughout the city. The input consists of traffic flow and vehicle speed data acquired via optical cameras, environmental sensors, and voice acquisition devices. This data is transmitted to the server via a data communication device to generate and store an initial traffic condition dataset.

[0404] Step 2:

[0405] The server uses received traffic data to predict traffic flow. It performs an analysis process using a generative AI model on the input traffic data, thereby generating predicted data for traffic flow and congestion.

[0406] Step 3:

[0407] The server generates traffic control signals based on predicted traffic flow. The input is predicted traffic flow data, and the server outputs signals to operate traffic signaling devices, such as optical or electronic signals, based on the analysis results. This attempts to optimize traffic flow.

[0408] Step 4:

[0409] The device collects user emotion data. It uses image data acquired from the camera and audio data from the microphone as input, and analyzes the images and audio using OpenCV, TensorFlow, and IBM Watson's Speech to Text API. This allows it to output data indicating the user's emotional state.

[0410] Step 5:

[0411] The server provides the optimal travel route based on the user's emotional state. The input is the user's emotional state data, and a generative AI model is used to generate the prompt message "Based on the user's emotional data, please suggest the most relaxing route." Based on this prompt, the server identifies a travel route suitable for the user and outputs that information.

[0412] Step 6:

[0413] The user begins moving according to the provided route. Based on the route information transmitted from the server, the user moves according to the navigation displayed on their information terminal. During this process, feedback on the movement is collected as input by the terminal, this feedback data is output, and finally sent to the server.

[0414] Step 7:

[0415] The server uses the feedback obtained after travel to improve its analysis models. It analyzes the feedback data as input and retrains the generative AI model and emotion engine to improve their performance. This enables more accurate traffic prediction and personalization.

[0416] 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.

[0417] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] The system implementing this invention is designed to manage urban traffic smoothly and consists of a detection device, a server, a terminal, and a user. The operation of the system will be described in detail below.

[0433] First, detection devices are installed at various locations throughout the city to acquire traffic data such as traffic volume and vehicle speed in real time. This data is transmitted to a server. Furthermore, users' smartphones and other devices provide anonymized location information to the server, supplementing the collection of more detailed traffic data.

[0434] Next, the server centrally analyzes the diverse data collected. Generative AI technology is used in this analysis, enabling not only an understanding of current traffic conditions but also the prediction of future traffic flow. Specifically, it models historically accumulated data and learns trends and patterns to identify locations and times when signal adjustments are necessary.

[0435] The server then generates optimal traffic control signals based on the predicted traffic conditions. These signals are sent to traffic signal devices within the city, and the timing of the lights is automatically adjusted. This reduces waiting times at traffic lights and alleviates congestion. An optimized travel route is also calculated and sent to the user's terminal.

[0436] Users can receive real-time traffic information and optimal routes through a smartphone app. This allows users to avoid congestion and contributes to improving the overall flow of urban traffic. Furthermore, user feedback is sent back to the server and used to further improve the analysis model.

[0437] As an example, when a user sets a destination using a smartphone app while traversing the city center, the server provides the optimal route based on current traffic conditions and forecasts. By following the suggested route, the user can avoid congestion. This improves individual travel convenience and increases overall traffic efficiency in the city.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The terminal acquires data such as traffic flow and vehicle speed in real time through IoT sensors within the city and transmits it to the server. The user's smartphone also collects location information anonymously and prepares to transmit it to the server.

[0441] Step 2:

[0442] The server aggregates the received data, standardizing and integrating information in different formats. This creates a single, comprehensive dataset, laying the foundation for analysis.

[0443] Step 3:

[0444] The server utilizes generative AI to analyze the collected and integrated data. This analysis includes real-time traffic conditions and predictions of future traffic patterns. Anomaly detection is also performed by referencing existing data patterns.

[0445] Step 4:

[0446] Based on the analysis results, the server generates optimal traffic control signals to operate the traffic signal system. These control signals automatically adjust signal timing and set priorities to ensure smooth traffic flow.

[0447] Step 5:

[0448] The terminal transmits control signals to the traffic signal system and performs the specified adjustments. The switching times and sequences of the signal lights are optimized in real time, allowing for immediate response to on-site traffic conditions.

[0449] Step 6:

[0450] The server sends optimized travel route information to the user's information terminal. Personalized route guidance is provided for each user, helping to avoid traffic congestion.

[0451] Step 7:

[0452] Users check the optimal route received through the app and follow the instructions to travel. While traveling, users can utilize features that allow them to check real-time traffic conditions, ensuring a comfortable journey.

[0453] Step 8:

[0454] Users provide feedback through the app after completing their journey. They submit opinions on the system's usability and the effectiveness of suggested routes, which contributes to improving the analysis model on the server.

[0455] (Example 1)

[0456] 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."

[0457] In urban areas, traffic congestion significantly impacts economic activity and daily life, necessitating efficient traffic management. However, current traffic management systems lack the ability to respond in real time or predict future traffic flow adequately, resulting in inefficient signal adjustments and route guidance. Furthermore, while providing traffic information that is relevant to users is crucial, its accuracy is currently insufficient.

[0458] 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.

[0459] In this invention, the server includes means for acquiring dynamic data from multiple detection devices placed in an urban environment, means for using a generative AI model that analyzes the dynamic data and location information data from terminals together to predict trends, and means for generating control signals based on the trend predictions and operating control devices. This makes it possible to optimize traffic flow in real time and provide users with the optimal travel route.

[0460] The term "urban environment" refers to areas and conditions in urban areas where traffic and pedestrian flow are concentrated, and includes spaces where various detection devices and control equipment can be installed.

[0461] A "detection device" is equipment installed to acquire dynamic data such as traffic flow, vehicle speed, and environmental conditions, and includes optical instruments and detection sensors.

[0462] "Dynamic data" refers to data that shows information about movement, such as traffic flow, vehicle speed, and traffic light waiting time.

[0463] A "terminal" refers to a user's portable device that is connected to a communication network and capable of sending and receiving information.

[0464] "Location data" refers to information obtained from a device that indicates its current geographical location.

[0465] A "generative AI model" refers to artificial intelligence technology used to analyze traffic conditions and predict future trends based on data.

[0466] A "control signal" is a signal generated to dynamically operate traffic signal equipment and includes parameters for optimizing traffic flow.

[0467] "Control equipment" refers to signaling devices that operate in response to generated control signals and perform traffic control.

[0468] "Feedback" refers to information collected from users regarding their opinions on the system and their usage results.

[0469] This system is designed to efficiently manage traffic in urban environments. The system primarily functions around three components: servers, terminals, and users.

[0470] The server acquires dynamic data from multiple detection devices placed throughout the city. This data includes traffic flow and vehicle speed. Furthermore, it utilizes anonymized location data received from terminals to enable detailed analysis of traffic conditions. The server uses this data to run a generative AI model, analyzing the current traffic state and predicting future trends. Trend analysis and pattern recognition are used in the analysis to generate optimal control signals.

[0471] The generative AI model learns from historical dynamic data and has the ability to detect specific traffic patterns and anomalies. This model dynamically adjusts the timing of traffic signals to optimize congestion and reduce waiting times. The generated control signals are also transmitted to control equipment within the city, automatically adjusting the timing of traffic signals.

[0472] The terminal refers to the user's smartphone or other device, and it has the function of providing the user with real-time travel route and traffic information transmitted from the server. This information allows the user to select the optimal route based on current traffic conditions and reach their destination efficiently.

[0473] Users navigate based on the provided information and, after use, send feedback regarding traffic information and the provided route back to the server via their device. This feedback contributes to improving the accuracy of the analysis and is used to improve the analysis model for future use.

[0474] As a concrete example, when a user sets a destination while traveling through the city center, the server analyzes all current traffic data and generates the most efficient route. For instance, a prompt such as "Provide the user with the best possible route based on current traffic conditions and forecasts" could be used. This method improves the travel efficiency of individual users and also improves overall traffic flow in the city.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The detection device acquires dynamic data. It senses vehicle speed, traffic flow, etc., at each installation point and transmits this data to a server. The input is actual traffic condition data, and the output is this data being sent to the server.

[0478] Step 2:

[0479] The server receives anonymized location data from the terminal. This data, transmitted from the user's terminal, is used to gain a more detailed understanding of current traffic conditions. The input is location data from the terminal, and the output is the collection of location data on the server.

[0480] Step 3:

[0481] The server analyzes the acquired dynamic and location data. Using a generative AI model, it analyzes traffic conditions based on past data and predicts future traffic flow. Data processing includes trend analysis and pattern recognition, and the output identifies locations where signal adjustments are necessary.

[0482] Step 4:

[0483] The server generates optimized control signals based on the analysis results. It creates control signals containing specific parameters for dynamically adjusting signal timing and transmits them to the relevant traffic signal equipment. The input is the analysis results, and the output is the generated control signals.

[0484] Step 5:

[0485] The server sends the calculated optimal travel route to the user's terminal. It provides the user with the best route, taking into account real-time, fluctuating traffic information. The input is the current traffic conditions and predicted data, and the output is route guidance for the user.

[0486] Step 6:

[0487] Users travel based on the provided route information and select the optimal route for efficient travel. After travel, users send feedback about traffic and routes back to the server via the app. The feedback information is collected on the server and used to improve the accuracy of the analysis.

[0488] (Application Example 1)

[0489] 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."

[0490] Urban traffic congestion leads to decreased mobility efficiency and increased environmental burden. Therefore, there is a need for a system that optimizes traffic flow throughout the urban environment and allows autonomous vehicles to select the optimal route in real time. However, conventional methods have limitations in their ability to collect and analyze traffic data, making it difficult to provide rapid and accurate navigation.

[0491] 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.

[0492] In this invention, the server includes means for acquiring traffic data from multiple detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, and means for generating traffic control signals based on the predictions and operating traffic signal devices. This makes it possible to efficiently analyze traffic information and provide optimal navigation information to an automated mobile vehicle in real time.

[0493] "Urban environment" refers to urban areas and their surrounding regions where a large number of people live and transportation methods are concentrated.

[0494] A "detection device" refers to equipment used to collect traffic data such as traffic flow and vehicle speed.

[0495] "Traffic data" refers to data that includes information such as traffic volume, vehicle speed, and timing of movement.

[0496] "Traffic flow" refers to the flow and movement of traffic within a specific area.

[0497] A "traffic control signal" is a signal generated to operate traffic signals, and is a means of controlling the flow of traffic.

[0498] "Information terminals" refer to digital devices, including smartphones and tablets.

[0499] An "autonomous vehicle" refers to a vehicle equipped with a system that independently selects its own route and moves accordingly.

[0500] "Navigation information" refers to the optimal route to the destination and various instructions regarding operation.

[0501] An "analytical model" is a mathematical or computer simulation model used to predict traffic trends and patterns based on collected data.

[0502] A "wireless communication device" refers to hardware that can send and receive information using radio waves.

[0503] "Signal parameters" refer to the specific settings and conditions used to determine the operation of traffic signals.

[0504] A "generative AI model" is an artificial intelligence model created using machine learning algorithms based on a large amount of data, for predicting traffic flow and optimal routes.

[0505] The system that realizes this invention is intended to improve the efficiency of traffic management in urban environments. It mainly consists of components such as a server, a detection device, an information terminal, and an automated mobile unit.

[0506] The server collects traffic data in real time from multiple detection devices placed throughout the city and predicts traffic flow using a generative AI model. Based on this prediction, the server generates traffic control signals and operates traffic signal devices to optimize traffic flow. The generated control signals include dynamically adjustable signal parameters, allowing for real-time changes to the timing of traffic signals.

[0507] Meanwhile, the information terminal receives optimized route information transmitted from the server and provides this information to the automated vehicle as navigation information. The automated vehicle selects a route in real time according to the received navigation information and operates accordingly. This makes it possible to avoid congestion and travel efficiently.

[0508] Specifically, for automated vehicles traveling through busy areas, the server predicts congestion levels and proposes routes. This process involves building generative AI models using programming languages ​​like Python and machine learning frameworks such as TensorFlow. For data processing, traffic data is analyzed using Pandas, and the predictive model is implemented using Scikit-learn.

[0509] Examples of prompts include, "Predict the optimal route during weekday evening rush hour," and "Predict congested areas due to increased events on weekends and suggest routes to avoid them."

[0510] In this way, the server improves the overall traffic efficiency of the city and creates an environment where autonomous vehicles can operate smoothly.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The server collects real-time traffic data from multiple detection devices located throughout the city. Specifically, it aggregates information such as vehicle speed and traffic flow via wireless communication devices. At this stage, the input is raw data acquired from each detection device, and the output is traffic data that has been normalized and integrated into an analyzable format.

[0514] Step 2:

[0515] The server uses collected traffic data to run a generative AI model, analyzes current traffic flow, and predicts future congestion points and the timing of traffic jams. Specifically, it inputs data into a model trained on past traffic patterns using Python and TensorFlow, and outputs prediction results. At this stage, normalized traffic data is used as input, and the output is the predicted traffic trend.

[0516] Step 3:

[0517] The server generates traffic control signals based on the prediction results. Specifically, it optimizes signal parameters to control the timing of traffic signals and transmits them to the traffic signaling equipment. Here, the predicted traffic flow results are used as input, and the output is a set of adjusted signal parameters.

[0518] Step 4:

[0519] The terminal transmits optimized route information provided by the server to the automated vehicle. This process involves receiving the route information, integrating it into the internal navigation system, and updating the operational plan. The input is route information from the server, and the output is the automated vehicle's real-time operational route.

[0520] Step 5:

[0521] The user monitors the movement of the automated vehicle based on navigation information received during operation. Furthermore, they send feedback to the server based on the operation results, contributing to the improvement of the analysis model. The input is historical information of the route operated, and the output is feedback information.

[0522] Step 6:

[0523] The server analyzes user feedback to update the generated AI model and build a highly accurate traffic prediction model. Based on this feedback, it optimizes model parameters to improve the accuracy of the next prediction. The input is user feedback data, and the output is the updated AI model.

[0524] 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.

[0525] The system implementing this invention not only improves the efficiency of urban traffic management but also enables mobility support that takes into account the emotional state of users. This system consists of a detection device, a server, a terminal, and an emotion engine.

[0526] First, detection devices are installed within the city to acquire real-time data such as traffic flow and vehicle speed, and transmit this data to a server. User terminals support the acquisition of detailed traffic data by collecting anonymized location information and transmitting it to the server.

[0527] Next, the server centralizes the received traffic data and uses generating AI for analysis. This includes real-time traffic situation recognition and future traffic flow prediction. Furthermore, it dynamically optimizes traffic conditions by generating personalized and optimal traffic control signals for each user and adjusting each traffic signal device.

[0528] On the other hand, the device is equipped with an emotion engine that uses voice and image analysis to determine the user's emotional state. This emotion engine can evaluate the user's stress level and satisfaction level in real time and adjust the suggested travel route based on its output. This function allows, for example, if the user is feeling anxious, to suggest a more relaxing route.

[0529] Users receive optimized travel routes and traffic information from a server via an app on their device, along with information tailored to their emotions. This allows users to enjoy an efficient and comfortable travel experience. After their trip, feedback is sent from their device to the server and used to improve the analysis model and emotion engine.

[0530] As an example, when a user travels through a city during peak hours, the emotion engine detects their stress level and suggests the most suitable route for that state. By choosing a relaxing route and traveling efficiently along a congested path, the user's comfort level is improved. In this way, the system simultaneously optimizes urban traffic and enhances the mental satisfaction of individual users.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The terminal acquires data such as traffic flow and vehicle speed in real time via IoT sensors within the city and transmits it to a server. At the same time, the smartphone app prepares to collect the user's location information anonymized.

[0534] Step 2:

[0535] An emotion engine operates on the device, performing sentiment analysis using the user's voice data and camera images. This allows for real-time determination of the user's stress level and satisfaction level. This information is then used to individually adjust the presentation method of traffic information.

[0536] Step 3:

[0537] The server aggregates traffic data transmitted from terminals to create a unified dataset. This allows for the integration of data from different sources, preparing it for analysis.

[0538] Step 4:

[0539] The server utilizes AI to analyze integrated data, grasp real-time traffic conditions, and predict future traffic flow by referencing past patterns. This creates a foundation for optimizing traffic flow.

[0540] Step 5:

[0541] The server generates optimal traffic control signals based on predicted traffic conditions. These signals are transmitted to the traffic signaling system, which automatically adjusts the signal timing. As the traffic signals change, the traffic flow at the site is dynamically optimized.

[0542] Step 6:

[0543] The server integrates the emotion engine's output with traffic analysis results to calculate a suitable travel route for the user. This route information is adjusted to take the user's emotional state into account and then sent to the terminal.

[0544] Step 7:

[0545] The user checks an optimized travel route on a smartphone app on their device and travels according to the suggested route. During travel, the device continuously monitors the user's emotional changes using an emotion engine and receives instructions from the server as needed.

[0546] Step 8:

[0547] After arriving at their destination, users submit feedback about their travel experience through the app. This feedback is collected on a server and contributes to improving the analysis model and emotion engine.

[0548] (Example 2)

[0549] 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."

[0550] In modern urban environments, traffic congestion and the resulting stress and discomfort place a significant burden on users. Therefore, there is a need to efficiently manage traffic conditions while suggesting travel routes that take into account users' emotional states. However, existing traffic management systems do not adequately consider individual emotional states when suggesting travel routes, and there is room for improvement. To solve this problem, a new system is needed that combines the analysis of traffic data with the analysis of users' emotional states.

[0551] 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.

[0552] In this invention, the server includes means for acquiring traffic data from a plurality of detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, and means for evaluating the emotional state of users using acoustic and video analysis and optimizing travel routes based on the evaluation. This enables efficient traffic management as well as optimal travel suggestions that are appropriate to the emotional state of users.

[0553] A "detection device" is a device placed in an urban environment to acquire data such as traffic flow and vehicle speed.

[0554] "Traffic data" refers to a collection of information that represents traffic conditions, such as traffic volume and vehicle speed.

[0555] "Traffic flow" refers to the phenomenon of movement of vehicles and people within a certain area.

[0556] A "traffic control signal" is a signal transmitted to operate traffic signaling equipment and is used to regulate the flow of traffic.

[0557] "Acoustic and visual analysis" refers to analytical methods used to evaluate a user's emotional state from audio and visual data.

[0558] "Emotional state" refers to the mental state experienced by users, including stress levels and satisfaction levels.

[0559] "Feedback" refers to opinions and evaluations based on the travel experience provided by users.

[0560] An "analytical model" is a computational method or algorithm that analyzes traffic data and emotional states to predict traffic conditions and travel routes.

[0561] The "emotion engine" is a component within the system that evaluates the user's emotional state in real time through acoustic and visual analysis and reflects this in the suggestion of travel routes.

[0562] The system implementing this invention aims to streamline traffic management in urban environments and support travel that takes into account the emotional state of users. This system consists of a detection device, a server, a terminal, and an emotion engine.

[0563] The server receives traffic data from multiple detection devices located throughout the city. These detection devices include optical devices for measuring traffic flow and vehicle speed, as well as environmental monitoring devices. The server centrally manages this data and analyzes traffic flow in real time using a generative AI model. This analysis includes recognizing the current traffic situation and predicting future traffic. Based on the analysis data, the server generates traffic control signals and dynamically operates traffic signaling systems.

[0564] The device incorporates an emotion engine that analyzes the user's voice and video data in real time. This process allows the device to assess the user's emotional state and determine their stress level and satisfaction level. Based on this, the server suggests a travel route optimized for the user. For example, a user experiencing stress might be offered a relaxing route.

[0565] The user receives an optimized travel route provided by the server via their device and begins their journey. This process ensures a smooth and comfortable travel experience. After the journey, feedback based on the user's experience is collected and sent to the server. This feedback is used to improve the analysis model and the emotion engine.

[0566] As a concrete example, when a user enters a prompt such as "Please suggest a route that takes my current emotional state into account" into their device, the system uses acoustic and visual analysis to evaluate the user's emotions and then suggests the optimal travel route based on that evaluation. In this way, the system simultaneously achieves efficient traffic management and improved individual satisfaction.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The server collects traffic data from detection devices placed throughout the city. It receives traffic flow and vehicle speed data transmitted from these devices as input. This data is acquired from optical and environmental monitoring devices and transmitted to the server in real time. The server centralizes this data and converts it into a format necessary for future analysis. The output is an integrated traffic dataset.

[0570] Step 2:

[0571] The server uses aggregated traffic data to perform analysis with a generative AI model. It uses an integrated traffic dataset as input to analyze traffic flow. This analysis includes current traffic awareness and future traffic prediction. The AI ​​model applies pattern recognition algorithms to predict changes in traffic flow from the data. The output provides real-time traffic awareness results and predicted data.

[0572] Step 3:

[0573] The server generates traffic control signals based on the analysis results and transmits them to the traffic signal equipment. It takes traffic prediction data output by the AI ​​model as input. Based on this data, it optimizes the signal plan and generates control signals to operate the traffic signal equipment. The output is dynamically adjusted traffic control signals.

[0574] Step 4:

[0575] The device analyzes the user's voice and video data and uses an emotion engine to evaluate the user's emotional state. It receives voice recordings and video capture data as input. The emotion engine analyzes this data and calculates emotional metrics such as stress levels and satisfaction levels in real time. The output is an evaluation of the user's emotional state.

[0576] Step 5:

[0577] The server proposes an optimized travel route that takes the user's emotional state into consideration. It takes real-time traffic recognition results and user emotional state evaluation results as input. It then integrates these and runs an algorithm to calculate the optimal travel route for the user. The output is a travel route adjusted based on emotions.

[0578] Step 6:

[0579] The user receives suggested routes via their terminal and then proceeds. As input, route suggestions from the server are displayed on the terminal. The user reviews and selects the suggested route. As output, navigation begins, assisting the user's movement.

[0580] Step 7:

[0581] Users provide feedback on their experience after their journey. As input, they enter their opinions and evaluations of the journey experience into a terminal. This feedback is sent to the server and used to improve the analysis model and sentiment engine. As output, data is obtained that contributes to updating the server's algorithms and models.

[0582] (Application Example 2)

[0583] 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."

[0584] Traffic management in modern cities requires not only ensuring efficient traffic flow but also providing mobility support that considers the mental comfort of users. However, conventional traffic management systems primarily focus on generating traffic control signals based on real-time traffic data and optimizing traffic flow, and fail to provide personalized travel experiences that take into account the emotional state of users. Therefore, it is necessary to provide users with optimal travel routes that avoid traffic congestion and stressful routes, allowing them to relax.

[0585] 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.

[0586] In this invention, the server includes means for acquiring traffic data from a plurality of detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, means for generating traffic control signals and operating traffic signal devices based on the prediction, means for detecting the emotional state of the user and adjusting the travel route according to that state, and means for collecting feedback from the user and improving the analysis model. This makes it possible not only to optimize traffic flow in an urban environment but also to provide a personalized travel experience that responds to the user's emotions.

[0587] A "detection device" is a device installed in an urban environment to acquire traffic data, and includes optical cameras, environmental sensors, voice acquisition devices, and data communication devices.

[0588] "Traffic data" refers to various types of information related to traffic flow, such as traffic volume and vehicle speed.

[0589] "Traffic flow" refers to the state and patterns of movement of vehicles and people in an urban environment.

[0590] A "traffic control signal" is a signal generated to regulate traffic flow by operating traffic signaling equipment.

[0591] "Emotional state" refers to the psychological or emotional state of a user, such as their stress level or satisfaction level.

[0592] "Travel route" refers to the path a user takes to reach their destination.

[0593] "Personalization" refers to adjusting or optimizing a service according to the individual characteristics and circumstances of the user.

[0594] "Feedback" refers to information such as responses, reactions, and opinions collected from users.

[0595] The system implementing this invention simultaneously achieves improved efficiency in urban traffic management and mobility support tailored to the emotional state of users.

[0596] The server acquires traffic data from multiple detection devices placed throughout the city and predicts traffic flow in real time. These detection devices include optical cameras, environmental sensors, and voice acquisition devices, which collect data such as traffic volume and vehicle speed. This data is transmitted to the server via data communication devices, where it is analyzed using a generative AI model. Based on the analysis results, the server generates traffic control signals to be sent to each traffic signal device, dynamically optimizing traffic flow.

[0597] Meanwhile, the terminal is equipped with an emotion engine to evaluate the user's emotional state. This engine uses camera-based facial recognition technology (utilizing OpenCV and TensorFlow) and speech analysis technology (using a speech acquisition device and IBM Watson's Speech to Text API) to evaluate the user's stress level and satisfaction level in real time. Based on the evaluation results, the server provides the user with the optimal travel route and presents it on the user's information terminal.

[0598] Users can move efficiently and comfortably within the city by following the optimized travel routes provided. After the journey, feedback from the user is sent to the server via an information terminal and used to improve the analysis model. For example, if the user is feeling stressed, the server uses generative AI to analyze emotional state data and provides a recommended route using a prompt message such as, "Based on the user's emotional data, please suggest the most relaxing route."

[0599] Thus, this system incorporates advanced data analysis and personalization features that respond to the emotional state of users, embodying a novel technology that balances smooth traffic flow with the mental satisfaction of individual users.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The server receives traffic data from detection devices placed throughout the city. The input consists of traffic flow and vehicle speed data acquired via optical cameras, environmental sensors, and voice acquisition devices. This data is transmitted to the server via a data communication device to generate and store an initial traffic condition dataset.

[0603] Step 2:

[0604] The server uses received traffic data to predict traffic flow. It performs an analysis process using a generative AI model on the input traffic data, thereby generating predicted data for traffic flow and congestion.

[0605] Step 3:

[0606] The server generates traffic control signals based on predicted traffic flow. The input is predicted traffic flow data, and the server outputs signals to operate traffic signaling devices, such as optical or electronic signals, based on the analysis results. This attempts to optimize traffic flow.

[0607] Step 4:

[0608] The device collects user emotion data. It uses image data acquired from the camera and audio data from the microphone as input, and analyzes the images and audio using OpenCV, TensorFlow, and IBM Watson's Speech to Text API. This allows it to output data indicating the user's emotional state.

[0609] Step 5:

[0610] The server provides the optimal travel route based on the user's emotional state. The input is the user's emotional state data, and a generative AI model is used to generate the prompt message "Based on the user's emotional data, please suggest the most relaxing route." Based on this prompt, the server identifies a travel route suitable for the user and outputs that information.

[0611] Step 6:

[0612] The user begins moving according to the provided route. Based on the route information transmitted from the server, the user moves according to the navigation displayed on their information terminal. During this process, feedback on the movement is collected as input by the terminal, this feedback data is output, and finally sent to the server.

[0613] Step 7:

[0614] The server uses the feedback obtained after travel to improve its analysis models. It analyzes the feedback data as input and retrains the generative AI model and emotion engine to improve their performance. This enables more accurate traffic prediction and personalization.

[0615] 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.

[0616] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] The system implementing this invention is designed to manage urban traffic smoothly and consists of a detection device, a server, a terminal, and a user. The operation of the system will be described in detail below.

[0633] First, detection devices are installed at various locations throughout the city to acquire traffic data such as traffic volume and vehicle speed in real time. This data is transmitted to a server. Furthermore, users' smartphones and other devices provide anonymized location information to the server, supplementing the collection of more detailed traffic data.

[0634] Next, the server centrally analyzes the diverse data collected. Generative AI technology is used in this analysis, enabling not only an understanding of current traffic conditions but also the prediction of future traffic flow. Specifically, it models historically accumulated data and learns trends and patterns to identify locations and times when signal adjustments are necessary.

[0635] The server then generates optimal traffic control signals based on the predicted traffic conditions. These signals are sent to traffic signal devices within the city, and the timing of the lights is automatically adjusted. This reduces waiting times at traffic lights and alleviates congestion. An optimized travel route is also calculated and sent to the user's terminal.

[0636] Users can receive real-time traffic information and optimal routes through a smartphone app. This allows users to avoid congestion and contributes to improving the overall flow of urban traffic. Furthermore, user feedback is sent back to the server and used to further improve the analysis model.

[0637] As an example, when a user sets a destination using a smartphone app while traversing the city center, the server provides the optimal route based on current traffic conditions and forecasts. By following the suggested route, the user can avoid congestion. This improves individual travel convenience and increases overall traffic efficiency in the city.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The terminal acquires data such as traffic flow and vehicle speed in real time through IoT sensors within the city and transmits it to the server. The user's smartphone also collects location information anonymously and prepares to transmit it to the server.

[0641] Step 2:

[0642] The server aggregates the received data, standardizing and integrating information in different formats. This creates a single, comprehensive dataset, laying the foundation for analysis.

[0643] Step 3:

[0644] The server utilizes generative AI to analyze the collected and integrated data. This analysis includes real-time traffic conditions and predictions of future traffic patterns. Anomaly detection is also performed by referencing existing data patterns.

[0645] Step 4:

[0646] Based on the analysis results, the server generates optimal traffic control signals to operate the traffic signal system. These control signals automatically adjust signal timing and set priorities to ensure smooth traffic flow.

[0647] Step 5:

[0648] The terminal transmits control signals to the traffic signal system and performs the specified adjustments. The switching times and sequences of the signal lights are optimized in real time, allowing for immediate response to on-site traffic conditions.

[0649] Step 6:

[0650] The server sends optimized travel route information to the user's information terminal. Personalized route guidance is provided for each user, helping to avoid traffic congestion.

[0651] Step 7:

[0652] Users check the optimal route received through the app and follow the instructions to travel. While traveling, users can utilize features that allow them to check real-time traffic conditions, ensuring a comfortable journey.

[0653] Step 8:

[0654] Users provide feedback through the app after completing their journey. They submit opinions on the system's usability and the effectiveness of suggested routes, which contributes to improving the analysis model on the server.

[0655] (Example 1)

[0656] 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".

[0657] In urban areas, traffic congestion significantly impacts economic activity and daily life, necessitating efficient traffic management. However, current traffic management systems lack the ability to respond in real time or predict future traffic flow adequately, resulting in inefficient signal adjustments and route guidance. Furthermore, while providing traffic information that is relevant to users is crucial, its accuracy is currently insufficient.

[0658] 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.

[0659] In this invention, the server includes means for acquiring dynamic data from multiple detection devices placed in an urban environment, means for using a generative AI model that analyzes the dynamic data and location information data from terminals together to predict trends, and means for generating control signals based on the trend predictions and operating control devices. This makes it possible to optimize traffic flow in real time and provide users with the optimal travel route.

[0660] The term "urban environment" refers to areas and conditions in urban areas where traffic and pedestrian flow are concentrated, and includes spaces where various detection devices and control equipment can be installed.

[0661] A "detection device" is equipment installed to acquire dynamic data such as traffic flow, vehicle speed, and environmental conditions, and includes optical instruments and detection sensors.

[0662] "Dynamic data" refers to data that shows information about movement, such as traffic flow, vehicle speed, and traffic light waiting time.

[0663] A "terminal" refers to a user's portable device that is connected to a communication network and capable of sending and receiving information.

[0664] "Location data" refers to information obtained from a device that indicates its current geographical location.

[0665] A "generative AI model" refers to artificial intelligence technology used to analyze traffic conditions and predict future trends based on data.

[0666] A "control signal" is a signal generated to dynamically operate traffic signal equipment and includes parameters for optimizing traffic flow.

[0667] "Control equipment" refers to signaling devices that operate in response to generated control signals and perform traffic control.

[0668] "Feedback" refers to information collected from users regarding their opinions on the system and their usage results.

[0669] This system is designed to efficiently manage traffic in urban environments. The system primarily functions around three components: servers, terminals, and users.

[0670] The server acquires dynamic data from multiple detection devices placed throughout the city. This data includes traffic flow and vehicle speed. Furthermore, it utilizes anonymized location data received from terminals to enable detailed analysis of traffic conditions. The server uses this data to run a generative AI model, analyzing the current traffic state and predicting future trends. Trend analysis and pattern recognition are used in the analysis to generate optimal control signals.

[0671] The generative AI model learns from historical dynamic data and has the ability to detect specific traffic patterns and anomalies. This model dynamically adjusts the timing of traffic signals to optimize congestion and reduce waiting times. The generated control signals are also transmitted to control equipment within the city, automatically adjusting the timing of traffic signals.

[0672] The terminal refers to the user's smartphone or other device, and it has the function of providing the user with real-time travel route and traffic information transmitted from the server. This information allows the user to select the optimal route based on current traffic conditions and reach their destination efficiently.

[0673] Users navigate based on the provided information and, after use, send feedback regarding traffic information and the provided route back to the server via their device. This feedback contributes to improving the accuracy of the analysis and is used to improve the analysis model for future use.

[0674] As a concrete example, when a user sets a destination while traveling through the city center, the server analyzes all current traffic data and generates the most efficient route. For instance, a prompt such as "Provide the user with the best possible route based on current traffic conditions and forecasts" could be used. This method improves the travel efficiency of individual users and also improves overall traffic flow in the city.

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The detection device acquires dynamic data. It senses vehicle speed, traffic flow, etc., at each installation point and transmits this data to a server. The input is actual traffic condition data, and the output is this data being sent to the server.

[0678] Step 2:

[0679] The server receives anonymized location data from the terminal. This data, transmitted from the user's terminal, is used to gain a more detailed understanding of current traffic conditions. The input is location data from the terminal, and the output is the collection of location data on the server.

[0680] Step 3:

[0681] The server analyzes the acquired dynamic and location data. Using a generative AI model, it analyzes traffic conditions based on past data and predicts future traffic flow. Data processing includes trend analysis and pattern recognition, and the output identifies locations where signal adjustments are necessary.

[0682] Step 4:

[0683] The server generates optimized control signals based on the analysis results. It creates control signals containing specific parameters for dynamically adjusting signal timing and transmits them to the relevant traffic signal equipment. The input is the analysis results, and the output is the generated control signals.

[0684] Step 5:

[0685] The server sends the calculated optimal travel route to the user's terminal. It provides the user with the best route, taking into account real-time, fluctuating traffic information. The input is the current traffic conditions and predicted data, and the output is route guidance for the user.

[0686] Step 6:

[0687] Users travel based on the provided route information and select the optimal route for efficient travel. After travel, users send feedback about traffic and routes back to the server via the app. The feedback information is collected on the server and used to improve the accuracy of the analysis.

[0688] (Application Example 1)

[0689] 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".

[0690] Urban traffic congestion leads to decreased mobility efficiency and increased environmental burden. Therefore, there is a need for a system that optimizes traffic flow throughout the urban environment and allows autonomous vehicles to select the optimal route in real time. However, conventional methods have limitations in their ability to collect and analyze traffic data, making it difficult to provide rapid and accurate navigation.

[0691] 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.

[0692] In this invention, the server includes means for acquiring traffic data from multiple detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, and means for generating traffic control signals based on the predictions and operating traffic signal devices. This makes it possible to efficiently analyze traffic information and provide optimal navigation information to an automated mobile vehicle in real time.

[0693] "Urban environment" refers to urban areas and their surrounding regions where a large number of people live and transportation methods are concentrated.

[0694] A "detection device" refers to equipment used to collect traffic data such as traffic flow and vehicle speed.

[0695] "Traffic data" refers to data that includes information such as traffic volume, vehicle speed, and timing of movement.

[0696] "Traffic flow" refers to the flow and movement of traffic within a specific area.

[0697] A "traffic control signal" is a signal generated to operate traffic signals, and is a means of controlling the flow of traffic.

[0698] "Information terminals" refer to digital devices, including smartphones and tablets.

[0699] An "autonomous vehicle" refers to a vehicle equipped with a system that independently selects its own route and moves accordingly.

[0700] "Navigation information" refers to the optimal route to the destination and various instructions regarding operation.

[0701] An "analytical model" is a mathematical or computer simulation model used to predict traffic trends and patterns based on collected data.

[0702] A "wireless communication device" refers to hardware that can send and receive information using radio waves.

[0703] "Signal parameters" refer to the specific settings and conditions used to determine the operation of traffic signals.

[0704] A "generative AI model" is an artificial intelligence model created using machine learning algorithms based on a large amount of data, for predicting traffic flow and optimal routes.

[0705] The system that realizes this invention is intended to improve the efficiency of traffic management in urban environments. It mainly consists of components such as a server, a detection device, an information terminal, and an automated mobile unit.

[0706] The server collects traffic data in real time from multiple detection devices placed throughout the city and predicts traffic flow using a generative AI model. Based on this prediction, the server generates traffic control signals and operates traffic signal devices to optimize traffic flow. The generated control signals include dynamically adjustable signal parameters, allowing for real-time changes to the timing of traffic signals.

[0707] Meanwhile, the information terminal receives optimized route information transmitted from the server and provides this information to the automated vehicle as navigation information. The automated vehicle selects a route in real time according to the received navigation information and operates accordingly. This makes it possible to avoid congestion and travel efficiently.

[0708] Specifically, for automated vehicles traveling through busy areas, the server predicts congestion levels and proposes routes. This process involves building generative AI models using programming languages ​​like Python and machine learning frameworks such as TensorFlow. For data processing, traffic data is analyzed using Pandas, and the predictive model is implemented using Scikit-learn.

[0709] Examples of prompts include, "Predict the optimal route during weekday evening rush hour," and "Predict congested areas due to increased events on weekends and suggest routes to avoid them."

[0710] In this way, the server improves the overall traffic efficiency of the city and creates an environment where autonomous vehicles can operate smoothly.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The server collects real-time traffic data from multiple detection devices located throughout the city. Specifically, it aggregates information such as vehicle speed and traffic flow via wireless communication devices. At this stage, the input is raw data acquired from each detection device, and the output is traffic data that has been normalized and integrated into an analyzable format.

[0714] Step 2:

[0715] The server uses collected traffic data to run a generative AI model, analyzes current traffic flow, and predicts future congestion points and the timing of traffic jams. Specifically, it inputs data into a model trained on past traffic patterns using Python and TensorFlow, and outputs prediction results. At this stage, normalized traffic data is used as input, and the output is the predicted traffic trend.

[0716] Step 3:

[0717] The server generates traffic control signals based on the prediction results. Specifically, it optimizes signal parameters to control the timing of traffic signals and transmits them to the traffic signaling equipment. Here, the predicted traffic flow results are used as input, and the output is a set of adjusted signal parameters.

[0718] Step 4:

[0719] The terminal transmits optimized route information provided by the server to the automated vehicle. This process involves receiving the route information, integrating it into the internal navigation system, and updating the operational plan. The input is route information from the server, and the output is the automated vehicle's real-time operational route.

[0720] Step 5:

[0721] The user monitors the movement of the automated vehicle based on navigation information received during operation. Furthermore, they send feedback to the server based on the operation results, contributing to the improvement of the analysis model. The input is historical information of the route operated, and the output is feedback information.

[0722] Step 6:

[0723] The server analyzes user feedback to update the generated AI model and build a highly accurate traffic prediction model. Based on this feedback, it optimizes model parameters to improve the accuracy of the next prediction. The input is user feedback data, and the output is the updated AI model.

[0724] 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.

[0725] The system implementing this invention not only improves the efficiency of urban traffic management but also enables mobility support that takes into account the emotional state of users. This system consists of a detection device, a server, a terminal, and an emotion engine.

[0726] First, detection devices are installed within the city to acquire real-time data such as traffic flow and vehicle speed, and transmit this data to a server. User terminals support the acquisition of detailed traffic data by collecting anonymized location information and transmitting it to the server.

[0727] Next, the server centralizes the received traffic data and uses generating AI for analysis. This includes real-time traffic situation recognition and future traffic flow prediction. Furthermore, it dynamically optimizes traffic conditions by generating personalized and optimal traffic control signals for each user and adjusting each traffic signal device.

[0728] On the other hand, the device is equipped with an emotion engine that uses voice and image analysis to determine the user's emotional state. This emotion engine can evaluate the user's stress level and satisfaction level in real time and adjust the suggested travel route based on its output. This function allows, for example, if the user is feeling anxious, to suggest a more relaxing route.

[0729] Users receive optimized travel routes and traffic information from a server via an app on their device, along with information tailored to their emotions. This allows users to enjoy an efficient and comfortable travel experience. After their trip, feedback is sent from their device to the server and used to improve the analysis model and emotion engine.

[0730] As an example, when a user travels through a city during peak hours, the emotion engine detects their stress level and suggests the most suitable route for that state. By choosing a relaxing route and traveling efficiently along a congested path, the user's comfort level is improved. In this way, the system simultaneously optimizes urban traffic and enhances the mental satisfaction of individual users.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The terminal acquires data such as traffic flow and vehicle speed in real time via IoT sensors within the city and transmits it to a server. At the same time, the smartphone app prepares to collect the user's location information anonymized.

[0734] Step 2:

[0735] An emotion engine operates on the device, performing sentiment analysis using the user's voice data and camera images. This allows for real-time determination of the user's stress level and satisfaction level. This information is then used to individually adjust the presentation method of traffic information.

[0736] Step 3:

[0737] The server aggregates traffic data transmitted from terminals to create a unified dataset. This allows for the integration of data from different sources, preparing it for analysis.

[0738] Step 4:

[0739] The server utilizes AI to analyze integrated data, grasp real-time traffic conditions, and predict future traffic flow by referencing past patterns. This creates a foundation for optimizing traffic flow.

[0740] Step 5:

[0741] The server generates optimal traffic control signals based on predicted traffic conditions. These signals are transmitted to the traffic signaling system, which automatically adjusts the signal timing. As the traffic signals change, the traffic flow at the site is dynamically optimized.

[0742] Step 6:

[0743] The server integrates the emotion engine's output with traffic analysis results to calculate a suitable travel route for the user. This route information is adjusted to take the user's emotional state into account and then sent to the terminal.

[0744] Step 7:

[0745] The user checks an optimized travel route on a smartphone app on their device and travels according to the suggested route. During travel, the device continuously monitors the user's emotional changes using an emotion engine and receives instructions from the server as needed.

[0746] Step 8:

[0747] After arriving at their destination, users submit feedback about their travel experience through the app. This feedback is collected on a server and contributes to improving the analysis model and emotion engine.

[0748] (Example 2)

[0749] 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".

[0750] In modern urban environments, traffic congestion and the resulting stress and discomfort place a significant burden on users. Therefore, there is a need to efficiently manage traffic conditions while suggesting travel routes that take into account users' emotional states. However, existing traffic management systems do not adequately consider individual emotional states when suggesting travel routes, and there is room for improvement. To solve this problem, a new system is needed that combines the analysis of traffic data with the analysis of users' emotional states.

[0751] 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.

[0752] In this invention, the server includes means for acquiring traffic data from a plurality of detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, and means for evaluating the emotional state of users using acoustic and video analysis and optimizing travel routes based on the evaluation. This enables efficient traffic management as well as optimal travel suggestions that are appropriate to the emotional state of users.

[0753] A "detection device" is a device placed in an urban environment to acquire data such as traffic flow and vehicle speed.

[0754] "Traffic data" refers to a collection of information that represents traffic conditions, such as traffic volume and vehicle speed.

[0755] "Traffic flow" refers to the phenomenon of movement of vehicles and people within a certain area.

[0756] A "traffic control signal" is a signal transmitted to operate traffic signaling equipment and is used to regulate the flow of traffic.

[0757] "Acoustic and visual analysis" refers to analytical methods used to evaluate a user's emotional state from audio and visual data.

[0758] "Emotional state" refers to the mental state experienced by users, including stress levels and satisfaction levels.

[0759] "Feedback" refers to opinions and evaluations based on the travel experience provided by users.

[0760] An "analytical model" is a computational method or algorithm that analyzes traffic data and emotional states to predict traffic conditions and travel routes.

[0761] The "emotion engine" is a component within the system that evaluates the user's emotional state in real time through acoustic and visual analysis and reflects this in the suggestion of travel routes.

[0762] The system implementing this invention aims to streamline traffic management in urban environments and support travel that takes into account the emotional state of users. This system consists of a detection device, a server, a terminal, and an emotion engine.

[0763] The server receives traffic data from multiple detection devices located throughout the city. These detection devices include optical devices for measuring traffic flow and vehicle speed, as well as environmental monitoring devices. The server centrally manages this data and analyzes traffic flow in real time using a generative AI model. This analysis includes recognizing the current traffic situation and predicting future traffic. Based on the analysis data, the server generates traffic control signals and dynamically operates traffic signaling systems.

[0764] The device incorporates an emotion engine that analyzes the user's voice and video data in real time. This process allows the device to assess the user's emotional state and determine their stress level and satisfaction level. Based on this, the server suggests a travel route optimized for the user. For example, a user experiencing stress might be offered a relaxing route.

[0765] The user receives an optimized travel route provided by the server via their device and begins their journey. This process ensures a smooth and comfortable travel experience. After the journey, feedback based on the user's experience is collected and sent to the server. This feedback is used to improve the analysis model and the emotion engine.

[0766] As a concrete example, when a user enters a prompt such as "Please suggest a route that takes my current emotional state into account" into their device, the system uses acoustic and visual analysis to evaluate the user's emotions and then suggests the optimal travel route based on that evaluation. In this way, the system simultaneously achieves efficient traffic management and improved individual satisfaction.

[0767] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0768] Step 1:

[0769] The server collects traffic data from detection devices placed throughout the city. It receives traffic flow and vehicle speed data transmitted from these devices as input. This data is acquired from optical and environmental monitoring devices and transmitted to the server in real time. The server centralizes this data and converts it into a format necessary for future analysis. The output is an integrated traffic dataset.

[0770] Step 2:

[0771] The server uses aggregated traffic data to perform analysis with a generative AI model. It uses an integrated traffic dataset as input to analyze traffic flow. This analysis includes current traffic awareness and future traffic prediction. The AI ​​model applies pattern recognition algorithms to predict changes in traffic flow from the data. The output provides real-time traffic awareness results and predicted data.

[0772] Step 3:

[0773] The server generates traffic control signals based on the analysis results and transmits them to the traffic signal equipment. It takes traffic prediction data output by the AI ​​model as input. Based on this data, it optimizes the signal plan and generates control signals to operate the traffic signal equipment. The output is dynamically adjusted traffic control signals.

[0774] Step 4:

[0775] The device analyzes the user's voice and video data and uses an emotion engine to evaluate the user's emotional state. It receives voice recordings and video capture data as input. The emotion engine analyzes this data and calculates emotional metrics such as stress levels and satisfaction levels in real time. The output is an evaluation of the user's emotional state.

[0776] Step 5:

[0777] The server proposes an optimized travel route that takes the user's emotional state into consideration. It takes real-time traffic recognition results and user emotional state evaluation results as input. It then integrates these and runs an algorithm to calculate the optimal travel route for the user. The output is a travel route adjusted based on emotions.

[0778] Step 6:

[0779] The user receives suggested routes via their terminal and then proceeds. As input, route suggestions from the server are displayed on the terminal. The user reviews and selects the suggested route. As output, navigation begins, assisting the user's movement.

[0780] Step 7:

[0781] Users provide feedback on their experience after their journey. As input, they enter their opinions and evaluations of the journey experience into a terminal. This feedback is sent to the server and used to improve the analysis model and sentiment engine. As output, data is obtained that contributes to updating the server's algorithms and models.

[0782] (Application Example 2)

[0783] 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".

[0784] Traffic management in modern cities requires not only ensuring efficient traffic flow but also providing mobility support that considers the mental comfort of users. However, conventional traffic management systems primarily focus on generating traffic control signals based on real-time traffic data and optimizing traffic flow, and fail to provide personalized travel experiences that take into account the emotional state of users. Therefore, it is necessary to provide users with optimal travel routes that avoid traffic congestion and stressful routes, allowing them to relax.

[0785] 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.

[0786] In this invention, the server includes means for acquiring traffic data from a plurality of detection devices placed in an urban environment, means for analyzing the traffic data to predict traffic flow, means for generating traffic control signals and operating traffic signal devices based on the prediction, means for detecting the emotional state of the user and adjusting the travel route according to that state, and means for collecting feedback from the user and improving the analysis model. This makes it possible not only to optimize traffic flow in an urban environment but also to provide a personalized travel experience that responds to the user's emotions.

[0787] A "detection device" is a device installed in an urban environment to acquire traffic data, and includes optical cameras, environmental sensors, voice acquisition devices, and data communication devices.

[0788] "Traffic data" refers to various types of information related to traffic flow, such as traffic volume and vehicle speed.

[0789] "Traffic flow" refers to the state and patterns of movement of vehicles and people in an urban environment.

[0790] A "traffic control signal" is a signal generated to regulate traffic flow by operating traffic signaling equipment.

[0791] "Emotional state" refers to the psychological or emotional state of a user, such as their stress level or satisfaction level.

[0792] "Travel route" refers to the path a user takes to reach their destination.

[0793] "Personalization" refers to adjusting or optimizing a service according to the individual characteristics and circumstances of the user.

[0794] "Feedback" refers to information such as responses, reactions, and opinions collected from users.

[0795] The system implementing this invention simultaneously achieves improved efficiency in urban traffic management and mobility support tailored to the emotional state of users.

[0796] The server acquires traffic data from multiple detection devices placed throughout the city and predicts traffic flow in real time. These detection devices include optical cameras, environmental sensors, and voice acquisition devices, which collect data such as traffic volume and vehicle speed. This data is transmitted to the server via data communication devices, where it is analyzed using a generative AI model. Based on the analysis results, the server generates traffic control signals to be sent to each traffic signal device, dynamically optimizing traffic flow.

[0797] Meanwhile, the terminal is equipped with an emotion engine to evaluate the user's emotional state. This engine uses camera-based facial recognition technology (utilizing OpenCV and TensorFlow) and speech analysis technology (using a speech acquisition device and IBM Watson's Speech to Text API) to evaluate the user's stress level and satisfaction level in real time. Based on the evaluation results, the server provides the user with the optimal travel route and presents it on the user's information terminal.

[0798] Users can move efficiently and comfortably within the city by following the optimized travel routes provided. After the journey, feedback from the user is sent to the server via an information terminal and used to improve the analysis model. For example, if the user is feeling stressed, the server uses generative AI to analyze emotional state data and provides a recommended route using a prompt message such as, "Based on the user's emotional data, please suggest the most relaxing route."

[0799] Thus, this system incorporates advanced data analysis and personalization features that respond to the emotional state of users, embodying a novel technology that balances smooth traffic flow with the mental satisfaction of individual users.

[0800] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0801] Step 1:

[0802] The server receives traffic data from detection devices placed throughout the city. The input consists of traffic flow and vehicle speed data acquired via optical cameras, environmental sensors, and voice acquisition devices. This data is transmitted to the server via a data communication device to generate and store an initial traffic condition dataset.

[0803] Step 2:

[0804] The server uses received traffic data to predict traffic flow. It performs an analysis process using a generative AI model on the input traffic data, thereby generating predicted data for traffic flow and congestion.

[0805] Step 3:

[0806] The server generates traffic control signals based on predicted traffic flow. The input is predicted traffic flow data, and the server outputs signals to operate traffic signaling devices, such as optical or electronic signals, based on the analysis results. This attempts to optimize traffic flow.

[0807] Step 4:

[0808] The device collects user emotion data. It uses image data acquired from the camera and audio data from the microphone as input, and analyzes the images and audio using OpenCV, TensorFlow, and IBM Watson's Speech to Text API. This allows it to output data indicating the user's emotional state.

[0809] Step 5:

[0810] The server provides the optimal travel route based on the user's emotional state. The input is the user's emotional state data, and a generative AI model is used to generate the prompt message "Based on the user's emotional data, please suggest the most relaxing route." Based on this prompt, the server identifies a travel route suitable for the user and outputs that information.

[0811] Step 6:

[0812] The user begins moving according to the provided route. Based on the route information transmitted from the server, the user moves according to the navigation displayed on their information terminal. During this process, feedback on the movement is collected as input by the terminal, this feedback data is output, and finally sent to the server.

[0813] Step 7:

[0814] The server uses the feedback obtained after travel to improve its analysis models. It analyzes the feedback data as input and retrains the generative AI model and emotion engine to improve their performance. This enables more accurate traffic prediction and personalization.

[0815] 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.

[0816] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] A means of acquiring traffic data from multiple detection devices placed in an urban environment,

[0839] A means for analyzing the aforementioned traffic data to predict traffic flow,

[0840] A means for generating traffic control signals based on the aforementioned prediction and operating a traffic signal device,

[0841] Means for providing the user's information terminal with a travel route optimized by the traffic control signal,

[0842] A means for collecting feedback from the aforementioned users and improving the analysis model,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the plurality of detection devices include an optical camera, an environmental sensor, and a mobile communication device.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the traffic control signal includes dynamically adjustable signal parameters and is configured to optimize traffic flow in an urban environment.

[0848] "Example 1"

[0849] (Claim 1)

[0850] A means for acquiring dynamic data from multiple detection devices placed in an urban environment,

[0851] A means of using a generative AI model that analyzes the aforementioned dynamic data and location information data from the terminal to predict trends,

[0852] A means for generating control signals based on the aforementioned trend prediction and operating control equipment,

[0853] Means for providing the user's communication terminal with a travel path optimized by the aforementioned control signal,

[0854] A means for collecting feedback from the aforementioned users and improving the analysis model,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the plurality of detection devices include optical instruments, detection sensors, and communication devices.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the control signal includes dynamically adjustable parameters and is set to optimize the dynamics of the urban environment.

[0860] "Application Example 1"

[0861] (Claim 1)

[0862] A means of acquiring traffic data from multiple detection devices placed in an urban environment,

[0863] A means for analyzing the aforementioned traffic data to predict traffic flow,

[0864] A means for generating traffic control signals based on the aforementioned prediction and operating a traffic signal device,

[0865] A means for providing an optimized travel route to an information terminal using the aforementioned traffic control signal and for transmitting navigation information to an automated vehicle,

[0866] A means for collecting feedback from the aforementioned users, improving the analysis model, and autonomously optimizing the vehicle's movement pattern,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, wherein the detection device includes an optical camera, an environmental sensor, and a wireless communication device.

[0870] (Claim 3)

[0871] The system according to claim 1, wherein the traffic control signal includes dynamically adjusted signal parameters and is configured to optimize traffic flow in an urban environment and enable an automated mobile vehicle to select the optimal route in real time.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] A means of acquiring traffic data from multiple detection devices placed in an urban environment,

[0875] A means for analyzing the aforementioned traffic data to predict traffic flow,

[0876] A means for generating traffic control signals based on the aforementioned prediction and operating a traffic signal device,

[0877] A means for evaluating the user's emotional state using acoustic and video analysis and optimizing the travel path based on the said evaluation,

[0878] Means for providing a user's information terminal with a travel route optimized based on the traffic control signal and emotional state,

[0879] A means for collecting feedback from the aforementioned users and improving the analysis model and emotion engine,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, wherein the plurality of detection devices include an optical device, an environmental monitor, and a mobile communication device.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the traffic control signal includes dynamically adjusted signal variables and is configured to optimize traffic flow in an urban environment and provide a travel experience that takes into account the emotional state of users.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] A means of acquiring traffic data from multiple detection devices placed in an urban environment,

[0888] A means for analyzing the aforementioned traffic data to predict traffic flow,

[0889] A means for generating traffic control signals based on the aforementioned prediction and operating a traffic signal device,

[0890] Means for providing the user's information terminal with a travel route optimized by the traffic control signal,

[0891] A means for detecting the emotional state of the user and adjusting the travel route according to that state,

[0892] A means for collecting feedback from the aforementioned users and improving the analysis model,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, wherein the plurality of detection devices include an optical camera, an environmental sensor, a voice acquisition device, and a data communication device.

[0896] (Claim 3)

[0897] The system according to claim 1, wherein the traffic control signal includes dynamically adjustable signal parameters and is configured to optimize traffic flow in an urban environment while taking into account personalization effects based on the emotional state of users. [Explanation of Symbols]

[0898] 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 of acquiring traffic data from multiple detection devices placed in an urban environment, A means for analyzing the aforementioned traffic data to predict traffic flow, A means for generating traffic control signals based on the aforementioned prediction and operating a traffic signal device, Means for providing the user's information terminal with a travel route optimized by the traffic control signal, A means for collecting feedback from the aforementioned users and improving the analysis model, A system that includes this.

2. The system according to claim 1, wherein the plurality of detection devices include an optical camera, an environmental sensor, and a mobile communication device.

3. The system according to claim 1, wherein the traffic control signal includes dynamically adjustable signal parameters and is configured to optimize traffic flow in an urban environment.

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