System

Abstract

A system is provided.SOLUTION: A system comprising: a data collection means; a data integration and pre-processing means; a data analysis and prediction means using generative artificial intelligence; an anomaly detection means; a problem measure proposal and implementation means; a citizen response means; and an improvement and feedback means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Modern urban management involves a complex intertwining of a wide range of factors, including road traffic flow and congestion management, environmental conservation, and energy efficiency optimization. Therefore, to achieve overall efficiency, immediate analysis of massive amounts of real-time data and rapid response are required. However, existing methods struggle to process this data quickly and effectively and automatically implement appropriate measures. Solving these challenges is essential to improve the efficiency of cities overall and the happiness of their citizens. [Means for solving the problem]

[0005] The present invention provides a system including a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means. The data collection means collects real-time data from various sensors within the city, and the data integration and preprocessing means integrates and preprocesses this data. Then, generative artificial intelligence is used to perform data analysis and prediction, and the anomaly detection means detects anomalies in real time. For detected anomalies, the problem countermeasure proposal and implementation means proposes optimal countermeasures and implements them through simulation. Furthermore, the citizen response means responds to citizen inquiries in real time, and the improvement and feedback means evaluates the results of the proposed and implemented countermeasures, thereby continuously improving the system and achieving efficient and sustainable urban management.

[0006] "Data collection means" refers to a device or system that collects various real-time data from sensors and devices installed within the city.

[0007] "Data integration and pre-processing means" refers to a device or system that integrates collected data and performs pre-processing to remove incomplete data and noise.

[0008] A "data analysis and prediction means using generative artificial intelligence" is a device or system that uses generative artificial intelligence (generative AI) algorithms to analyze data and make predictions.

[0009] An "anomaly detection means" is a device or system that monitors data in real time and detects unexpected anomalies.

[0010] "Problem solution proposal and implementation means" refers to a device or system that proposes appropriate solutions using analysis results based on generative artificial intelligence, and then simulates and implements them.

[0011] A "citizen response tool" is a device or system that citizens can access and that uses generative artificial intelligence to provide optimal answers to real-time inquiries.

[0012] "Improvement and feedback measures" are devices or systems that evaluate the effectiveness of implemented measures and continuously improve the entire system based on the results. [Brief explanation of the drawings]

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

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

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

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means.

[0035] Data collection methods

[0036] The terminals collect real-time data on vehicle flow, speed, congestion, etc. from traffic sensors (cameras and devices) installed at intersections and major traffic points within the city. The collected data is then sent to a server via the internet or a dedicated network.

[0037] Data integration and preprocessing measures

[0038] The server receives traffic data sent by the terminals and integrates the data collected from different sources, and in the process performs pre-processing such as data shaping and filtering to remove incomplete data and noise.

[0039] Data analysis and prediction methods using generative artificial intelligence

[0040] Once preprocessed, the data is analyzed by a generative AI running on the server. This allows for the analysis of current traffic patterns and the creation of a traffic congestion prediction model. The generative AI also simulates future traffic conditions and displays the results on a dashboard.

[0041] Anomaly detection means

[0042] The server monitors the flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and immediately issues an alert.

[0043] Problem solving proposals and implementation methods

[0044] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. For example, specific improvement plans such as adjusting traffic light timings or reconfiguring traffic routes are proposed. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures. Based on the evaluation results, the optimal countermeasures are implemented.

[0045] The terminal updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, for example, changing the traffic light control pattern.

[0046] Citizen response measures

[0047] If users have any problems or questions about the safety or traffic conditions in the city, they can use their smartphones or other devices to access the citizen chat system and make inquiries to the operator.

[0048] The server receives inquiries from users via the chat system and generates optimal answers using generative artificial intelligence. For example, in response to a query such as "What is the current traffic situation?", the server provides real-time traffic data and prediction results.

[0049] Improvement and Feedback Vehicles

[0050] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0051] The devices continue to collect data over time, providing a baseline for detecting new patterns and anomalies.

[0052] The comprehensive combination of these capabilities will enable accurate and rapid management of smart cities using generative artificial intelligence, improving the efficiency and happiness of citizens' lives.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0056] Step 2:

[0057] The server receives the data sent from the terminal, and the received data is first preprocessed by a data integration and preprocessing means to integrate data from different sources and remove incomplete data and noise.

[0058] Step 3:

[0059] The server then supplies the preprocessed data to a generative AI, which uses the data to analyze traffic patterns and build a predictive model of traffic congestion. Based on the model, future traffic conditions are simulated and the results are displayed on a dashboard.

[0060] Step 4:

[0061] The server monitors the data in real time and uses generative artificial intelligence to detect anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[0062] Step 5:

[0063] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The proposed countermeasures (e.g., adjusting traffic signal times or reconfiguring traffic routes) are simulated to evaluate their effectiveness.

[0064] Step 6:

[0065] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0066] Step 7:

[0067] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0068] Step 8:

[0069] The server receives inquiries from users through the chat system, and the generative AI provides the best possible answer. For example, in response to a question like, "What is the current traffic situation?", it presents real-time traffic data and prediction results.

[0070] Step 9:

[0071] The server monitors the results of proposed and implemented measures and evaluates their effectiveness. Quantitative performance data is collected and used to continuously improve the generative artificial intelligence model.

[0072] Step 10:

[0073] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0074] This series of processing flows will enable smart city management using generative artificial intelligence to be realized more efficiently and effectively.

[0075] Example 1

[0076] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0077] Urban traffic management requires accurate data collection, prediction, anomaly detection, and countermeasure implementation in real time. Current systems often experience delays in data processing and analysis, making it difficult to respond quickly. They also lack the means to provide appropriate answers to citizen inquiries in real time. This results in reduced operational efficiency and hinders improvements in the efficiency and happiness of citizens' lives.

[0078] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0079] In this invention, the server includes: a data collection means that collects data such as vehicle flow rate, speed, and congestion level in real time using sensors installed at intersections and major traffic points within the city; a data integration and preprocessing means that receives the data collected by the data collection means, integrates data collected from different sources, and performs preprocessing such as data shaping and filtering; a data analysis and prediction means that uses generative artificial intelligence to analyze the data preprocessed by the data integration and preprocessing means, and analyzes traffic patterns and builds a traffic congestion prediction model; anomaly detection means that monitors the continuous flow of data in real time and detects unexpected abnormalities using generative artificial intelligence; problem solution proposal and implementation means that uses generative artificial intelligence to propose optimal measures for detected abnormalities, simulates the measures, evaluates their effectiveness, and implements them; citizen response means that allows users to access the citizen chat system via a smartphone or terminal, make inquiries to the operator, and the generative artificial intelligence generates optimal answers to the inquiries; and an improvement and feedback means that monitors the results of the implemented measures, evaluates their effectiveness, collects performance data, and continuously improves the generative artificial intelligence model. This will enable more efficient traffic management in cities, faster response to abnormalities, and real-time responses to inquiries from citizens.

[0080] "Data collection means" refers to the collection of real-time data on vehicle flow, speed, congestion, etc. using sensors installed at intersections and major traffic points within the city.

[0081] The "data integration and pre-processing means" is a means for receiving data collected by the data collection means, integrating data collected from different sources, and performing pre-processing such as shaping and filtering the data.

[0082] The "data analysis and prediction means" is a means for analyzing data preprocessed by the data integration and preprocessing means using generative artificial intelligence to analyze traffic patterns and construct a traffic congestion prediction model.

[0083] An "anomaly detection method" is a method that monitors data that is constantly flowing in real time and detects unexpected anomalies using generative artificial intelligence.

[0084] "Proposal and implementation of solutions to the problem" refers to a method in which generative artificial intelligence proposes optimal solutions to detected anomalies, simulates those solutions, evaluates their effectiveness, and then implements them.

[0085] The "citizen response means" is a means by which users access the citizen chat system via their smartphones or terminals, make inquiries to the operator, and have the generative artificial intelligence generate the optimal response to those inquiries.

[0086] "Improvement and feedback measures" are measures to monitor the results of implemented measures, evaluate their effectiveness, and collect performance data to continuously improve the generative artificial intelligence model.

[0087] This invention relates to a system for streamlining urban traffic management and improving the efficiency and happiness of civic life. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means.

[0088] Data collection methods

[0089] The devices use sensors installed at intersections and major traffic points within a city to collect real-time data on vehicle flow, speed, congestion, and other factors. For example, traffic sensors may be cameras or other devices that capture video every second and quantify the number and speed of vehicles. This data is then sent to a server via Wi-Fi or a dedicated line.

[0090] Data integration and preprocessing measures

[0091] The server receives the raw data sent from the devices and consolidates it. It then shapes and filters the data to remove missing data and noise. For example, it applies a noise filter to data collected overnight to fill in outliers and missing values. It also removes duplicate data sent from the same device.

[0092] Data analysis and prediction methods using generative artificial intelligence

[0093] The preprocessed data is then analyzed by a generative artificial intelligence (AI) running on a server. This AI model analyzes traffic patterns and builds a model to predict future traffic congestion. The AI ​​is trained using traffic data from the past few weeks, then simulates traffic flow for the next hour and displays the predicted results on a dashboard.

[0094] Prompt Sentence Examples

[0095] "Based on the data from the past hour, please display the predicted number of vehicles passing through intersection A in the next hour."

[0096] Anomaly detection means

[0097] The server monitors the continuous flow of data in real time and uses generative artificial intelligence to detect anomalies, such as a sudden increase in traffic volume that deviates from normal patterns, and immediately sends an alert to administrators.

[0098] Problem solving proposals and implementation methods

[0099] The server uses generative artificial intelligence to propose optimal countermeasures for the detected anomalies. Specific countermeasures are proposed, such as adjusting traffic light timings or reconfiguring traffic routes. Simulations are then performed to evaluate the effectiveness of these countermeasures and implement the optimal countermeasures. The terminal then updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server.

[0100] Prompt Sentence Examples

[0101] "Please display the current traffic congestion situation at intersection A in real time."

[0102] Citizen response measures

[0103] Users can access the chat system for citizens using their smartphones or other devices and make inquiries to the operator. For example, they can ask, "Please tell me the current congestion situation."

[0104] The server receives user inquiries via a chat system and generates optimal answers using generative artificial intelligence, providing real-time traffic data and prediction results.

[0105] Improvement and Feedback Vehicles

[0106] The server monitors the results of the implemented measures and evaluates their effectiveness, collecting quantitative performance data to continuously improve the generative AI model. The terminal continues to collect data over the long term, providing a foundation for detecting new patterns and anomalies.

[0107] These capabilities will enable accurate and swift management of smart cities using generative artificial intelligence, streamlining urban traffic management and improving the efficiency and happiness of citizens' lives.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1: Data collection methods

[0110] The devices use sensors installed at intersections and major traffic points within the city to collect real-time data on vehicle flow, speed, congestion, and other factors. For example, cameras are used as traffic sensors. The cameras capture video every second and quantify the number and speed of vehicles from the video data. This data is then sent to a server via Wi-Fi or a dedicated line.

[0111] Input: Raw data (video data) from sensors such as cameras

[0112] Data processing: Analyzes video data and quantifies vehicle flow, speed, and congestion

[0113] Output: digitized traffic data

[0114] Step 2: Data integration and preprocessing measures

[0115] The server receives traffic data sent from the terminals, then integrates the data collected from different sources and performs pre-processing such as data shaping and filtering, eliminating missing data, noise, and redundant data.

[0116] Input: Traffic data collected in real time

[0117] Data processing: data shaping, filtering, missing value completion, noise removal, and duplicate data removal.

[0118] Output: Preprocessed and clean traffic data

[0119] Step 3: Data analysis and prediction methods using generative artificial intelligence

[0120] Generative AI running on the server analyzes the pre-processed data. The AI ​​model analyzes traffic patterns and builds a model to predict future traffic congestion. The prediction results are displayed on a dashboard in a visually understandable format for stakeholders.

[0121] Input: Preprocessed and clean traffic data

[0122] Data Computing: Generative AI to analyze traffic patterns and build predictive models

[0123] Output: Traffic congestion prediction results (dashboard display)

[0124] Step 4: Anomaly detection methods

[0125] The server monitors the flow of data in real time and uses generative artificial intelligence to detect anomalies, such as sudden increases in traffic volume or congestion that deviate from normal patterns, and immediately issues an alert.

[0126] Input: Real-time traffic data

[0127] Data calculation: Traffic pattern anomaly detection

[0128] Output: Warning notification of abnormality detection

[0129] Step 5: Proposal and implementation of solutions

[0130] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. Specific examples include adjusting traffic light timings and reconfiguring traffic routes. The proposals are evaluated through simulations, and the optimal countermeasures are implemented. The terminal updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server.

[0131] Input: Anomaly detection data, traffic prediction model

[0132] Data calculation: Generative AI proposes countermeasures and evaluates simulation results

[0133] Output: Optimal measures to be implemented (changes to traffic lights and equipment settings)

[0134] Step 6: Public Response Measures

[0135] Users access the chat system for citizens using their smartphones or other devices and send inquiries to the operator. The server receives inquiries from users via the chat system and generates optimal answers using generative artificial intelligence. For example, in response to an inquiry such as "What is the current congestion situation?", the server provides real-time traffic data and prediction results.

[0136] Input: User query

[0137] Data calculation: Real-time traffic data analysis, answer generation using generative AI

[0138] Output: Providing answers to users (traffic congestion status, etc.)

[0139] Step 7: Improvement and feedback measures

[0140] The server monitors the results of the implemented measures and evaluates their effectiveness. It collects quantitative performance data and continuously improves the generative AI model. The device continues to collect data over the long term, providing a foundation for detecting new patterns and anomalies.

[0141] Input: Traffic data and performance data after implementing measures

[0142] Data calculation: Evaluating the effectiveness of countermeasures and improving AI models

[0143] Output: improved AI models, data for new pattern detection

[0144] (Application example 1)

[0145] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0146] The goal is to provide a system that optimizes traffic conditions by streamlining the collection and analysis of traffic data within cities, quickly detecting traffic congestion and accidents, and immediately proposing and implementing appropriate countermeasures. Another challenge is to create a system that allows users to check traffic conditions in real time and receive optimal route suggestions.

[0147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0148] In this invention, the server includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a user response means, an improvement and feedback means, a real-time traffic data collection and analysis means, a means for proposing an optimal traffic route, and a user inquiry response means using generative artificial intelligence. This allows for real-time understanding of traffic conditions within a city, enabling efficient traffic management and optimal route proposals.

[0149] A "data collection means" is a device that collects data such as vehicle flow, speed, and congestion in real time from traffic sensors (cameras and devices) installed at intersections and major traffic points within a city, and transmits this data to a server via the Internet or a dedicated network.

[0150] "Data integration and pre-processing means" refers to the server's function of receiving traffic data sent from the terminal, integrating data collected from different sources, and performing pre-processing such as shaping and filtering the data to remove incomplete data and noise.

[0151] "Data analysis and prediction means using generative artificial intelligence" refers to a server function that uses pre-processed data to analyze current traffic patterns, build a predictive model of traffic congestion, and simulate future traffic conditions.

[0152] "Anomaly detection means" is a server function that monitors the flow of data in real time, uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents), and immediately issues an alert when an anomaly is detected.

[0153] "Proposal and implementation of problem solutions" refers to the functions of the server and terminal in which generative artificial intelligence proposes optimal solutions for abnormalities detected by the server, evaluates the effectiveness of the proposed solutions, and implements the optimal solutions based on the evaluation results.

[0154] The "user response means" is a function that allows users to access the citizen chat system using a smartphone or other device, make inquiries to the operator, and the server generates the most appropriate response using generative artificial intelligence.

[0155] "Improvement and feedback measures" refers to the function by which the server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0156] "Real-time traffic data collection and analysis means" refers to the function of the server and related devices to collect data on traffic conditions within the city in real time and analyze that data.

[0157] The "means of proposing the optimal transportation route" is a function that generates and proposes the optimal route based on the user's current location information and destination information, using the transportation data collected and analyzed by the server.

[0158] "Means for responding to user inquiries using generative artificial intelligence" is a function that uses generative artificial intelligence to generate optimal answers to users' inquiries about traffic conditions and other matters, and responds in real time.

[0159] A system for realizing this invention will now be described. The main elements of the system are a server, a terminal, and a user, and these elements cooperate to collect, analyze, predict, and respond to traffic data in real time.

[0160] The server includes the following means:

[0161] 1. Data collection means: This involves collecting data such as vehicle flow, speed, and congestion in real time from traffic sensors (cameras and devices) installed at intersections and major traffic points within the city, and sending it to a server via the Internet or a dedicated network.

[0162] 2. Data integration and pre-processing means: The server receives the traffic data sent from the terminals, integrates the data collected from different sources, and performs pre-processing such as data shaping and filtering.

[0163] 3. Data analysis and prediction using generative artificial intelligence: Preprocessed data is used to analyze current traffic patterns and build predictive models of traffic congestion, simulating future traffic conditions.

[0164] 4. Anomaly detection: The server monitors the flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents), issuing an immediate alert when an anomaly is detected.

[0165] 5. Proposal and implementation of countermeasures: The generative artificial intelligence proposes optimal countermeasures for the abnormalities detected by the server, evaluates the effectiveness of the proposed countermeasures, and implements the optimal countermeasures.

[0166] 6. User response method: Users access the citizen chat system using their smartphones or devices and make inquiries to the operator, and the server generates the most appropriate response using generative artificial intelligence.

[0167] 7. Improvement and feedback measures: The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0168] 8. Real-time traffic data collection and analysis means: This has the functions of a server and related devices for collecting data on traffic conditions within the city in real time and analyzing that data.

[0169] 9. Means for suggesting optimal transportation routes: Based on the transportation data collected and analyzed by the server, the optimal route is generated and suggested based on the user's current location information and destination information.

[0170] 10. A means of responding to user inquiries using generative artificial intelligence: Generative artificial intelligence will generate optimal answers to user inquiries about traffic conditions and other matters and respond in real time.

[0171] The hardware used includes traffic sensors, cameras, devices, and smartphones, and the software used includes the Python programming language, generative AI model APIs (such as GPT-3), server-side programs (e.g., Django, Flask), and databases (e.g., PostgreSQL, MySQL).

[0172] As a concrete example, let's consider the case where a user makes a query such as "Please tell me the current congestion situation." The following is an example of a prompt for this query:

[0173] Prompt statement:

[0174] User inquiry: What is the current congestion situation?

[0175] A generative AI model would take this prompt and generate a response like this:

[0176] Currently, there is a traffic jam on the main roads of the city, especially in the western part of the city, and traffic is very congested. The best route is to avoid road X and take road Y.

[0177] In this way, the system created can analyze traffic data within a city and provide users with optimal traffic information in real time.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] The terminal collects data such as vehicle flow, speed, and congestion from traffic sensors in the city in real time and sends it to a server. The input is the raw data obtained from the traffic sensors, and the output is sending this data to the server.

[0181] Step 2:

[0182] The server receives traffic data sent from the terminal. The input is the traffic data sent from the terminal, and the output is the data received by the server. This data is collected from different sources and requires pre-processing for integration.

[0183] Step 3:

[0184] The server integrates the received raw data and performs preprocessing such as data shaping and filtering. The input is the received raw data, and the output is the shaped data after preprocessing. Specific operations include completing incomplete data and removing noise.

[0185] Step 4:

[0186] The server uses the preprocessed data to perform data analysis and predictions using generative artificial intelligence. The input is the preprocessed data, and the output is an analysis of current traffic patterns and a traffic congestion prediction model. In this case, the server uses the generative artificial intelligence model to analyze trends and patterns in the data.

[0187] Step 5:

[0188] The server monitors the continuous flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents). The input is traffic data updated in real time, and the output is the anomaly detection results. Detected anomalies can immediately trigger an alert.

[0189] Step 6:

[0190] Based on the results of the anomaly detection by the server, the generative AI proposes optimal countermeasures. The input is the anomaly detection results, and the output is countermeasure proposals. Specific countermeasures include adjusting traffic light timings and reconfiguring traffic routes.

[0191] Step 7:

[0192] The server simulates the effectiveness of the proposed measures and implements the optimal measures based on the evaluation results. The input is the proposed measures, and the output is the optimization parameters to be implemented. These optimization parameters are sent to the terminal, and the settings of traffic lights and related equipment are updated.

[0193] Step 8:

[0194] Users access the citizen chat system using their smartphones or other devices and make inquiries about traffic conditions. The input is the user's inquiry, and the output is the optimal answer generated by generative AI. For example, in response to an inquiry such as "Please tell me the current congestion situation," the system responds with real-time traffic data and forecast results.

[0195] Step 9:

[0196] The server monitors the results of the implemented measures and evaluates their effectiveness. The input is performance data of the implemented measures, and the output is an evaluation of the measures' effectiveness and feedback. This provides data for continuously improving the generative AI model.

[0197] Step 10:

[0198] The server continues to collect data over time, providing a basis for detecting new patterns and anomalies. The input is continuously collected traffic data, and the output is an improved model, which increases the accuracy and effectiveness of the system.

[0199] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0200] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a citizen response means, an improvement and feedback means, and also an emotion engine that recognizes user emotions.

[0201] Data collection methods

[0202] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0203] Data integration and preprocessing measures

[0204] The server receives the data sent by the devices and integrates the data from different sources, performing pre-processing such as data shaping and filtering to remove incomplete data and noise.

[0205] Data analysis and prediction methods using generative artificial intelligence

[0206] Once preprocessed, the data is analyzed by a generative AI running on the server. This allows for the analysis of current traffic patterns and the creation of a traffic congestion prediction model. The generative AI also simulates future traffic conditions and displays the results on a dashboard.

[0207] Anomaly detection means

[0208] The server monitors the data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and immediately issues an alert if an anomaly is detected.

[0209] Problem solving proposals and implementation methods

[0210] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. For example, specific improvement plans such as adjusting traffic light timings or reconfiguring traffic routes are proposed. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures. Based on the evaluation results, the optimal countermeasures are implemented.

[0211] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0212] Citizen response measures

[0213] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0214] The server receives inquiries from users via the chat system, and the generative AI generates the optimal answer. For example, in response to a query such as "What is the current traffic situation?", it provides real-time traffic data and prediction results.

[0215] Improvement and Feedback Vehicles

[0216] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0217] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0218] User response using an emotion engine

[0219] The emotion engine recognizes the user's emotions in real time. This emotion data is captured when the user makes a query. The emotion engine assesses the user's emotional state using voice tone, character usage patterns, facial expression recognition, etc.

[0220] The server optimizes citizen response methods based on the emotional data provided by the emotion engine. For example, if a user is feeling stressed, the generative AI will generate a more flexible and reassuring response.

[0221] For example, when a user inquires about being late for work due to traffic jams on their smartphone, the emotion engine can sense the user's impatience from their tone of voice and the way they speak. Based on this information, the server uses generative artificial intelligence to create an appropriate message that offers the best alternative route and time, and alleviates the user's anxiety.

[0222] This system will significantly improve the efficiency of city management and the happiness of its citizens. This series of system processes will enable smart cities to respond more flexibly in real time, helping to realize sustainable urban life.

[0223] The processing flow will be explained below.

[0224] Step 1:

[0225] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0226] Step 2:

[0227] The server receives the data sent from the terminal, and the data integration and pre-processing means integrates the data from different sources and pre-processes the data to remove incomplete data and noise.

[0228] Step 3:

[0229] The server then supplies the preprocessed data to a generative AI, which uses the data to analyze traffic patterns, build a predictive model for traffic congestion, and simulate future traffic conditions, displaying the results on a dashboard.

[0230] Step 4:

[0231] The server monitors the data in real time and uses generative artificial intelligence to detect anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[0232] Step 5:

[0233] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The proposed countermeasures (e.g., adjusting traffic signal times or reconfiguring traffic routes) are simulated to evaluate their effectiveness.

[0234] Step 6:

[0235] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0236] Step 7:

[0237] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0238] Step 8:

[0239] The emotion engine recognizes the user's emotional state from their voice tone, character usage patterns, facial expressions, etc. when they make a query. The recognized emotion data is sent to the server.

[0240] Step 9:

[0241] The server uses generative artificial intelligence to generate optimal responses based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, it generates a flexible and reassuring response.

[0242] Step 10:

[0243] The server then provides the generated answers to the user through a chat system. For example, in response to a question such as "What is the current traffic congestion situation?", the server provides real-time traffic data and forecast results, thereby easing the user's anxiety.

[0244] Step 11:

[0245] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0246] Step 12:

[0247] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0248] This series of processing flows will enable smart city management utilizing generative artificial intelligence and emotion engines to be realized more efficiently and effectively.

[0249] Example 2

[0250] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0251] There is a need to effectively collect, process, and analyze various data within cities to improve the quality of life and happiness of citizens. However, current systems require time-consuming data integration and preprocessing, making it difficult to quickly detect anomalies or propose optimal countermeasures. In addition, there is a lack of consideration for user emotions, making it difficult to improve user satisfaction.

[0252] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0253] In this invention, the server includes means for collecting data, means for integrating and preprocessing the collected data, means for analyzing and predicting data using generative artificial intelligence, means for detecting anomalies in real time, means for proposing and implementing countermeasures based on the results of the anomaly detection, means for responding to inquiries from users, means for providing feedback on the effectiveness of the countermeasures and improving them, and means for recognizing user emotions. This enables efficient and flexible data management and citizen support within the city, thereby improving the happiness and quality of life of citizens.

[0254] "Means for collecting data" refers to devices and systems for collecting various data from devices such as sensors and cameras installed within the city.

[0255] "Means for integrating and pre-processing collected data" refers to devices and systems that receive data sent from terminals, consolidate data from different sources, and perform shaping and filtering to remove incomplete data and noise.

[0256] "Means for analyzing and predicting data using generative artificial intelligence" refers to devices and systems that use generative artificial intelligence to analyze data and predict future situations based on collected and preprocessed data.

[0257] "Means for detecting anomalies in real time" refers to devices and systems that use generative artificial intelligence to monitor data sequentially and immediately detect unexpected anomalies.

[0258] The "means for proposing and implementing solutions to problems based on anomaly detection results" refers to devices and systems that use generative artificial intelligence to propose optimal solutions when an anomaly is detected and then implement those proposals.

[0259] "Means for responding to inquiries from users" refers to devices and systems that enable generative artificial intelligence to provide appropriate answers to inquiries made by users via their terminals.

[0260] "Means for feedback and improvement of the effectiveness of measures" refers to devices and systems for monitoring the results of implemented measures, evaluating their effectiveness, and continuously improving the system.

[0261] The "means for recognizing user emotions" refers to a device or system for evaluating and recognizing the emotional state of a user when making a query through voice tone, character patterns, facial expression recognition, etc.

[0262] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system is an apparatus and system that includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a citizen response means, an improvement and feedback means, and an emotion engine that recognizes user emotions.

[0263] Data collection methods

[0264] The terminals collect real-time traffic and environmental data from sensors, cameras, and other devices installed throughout the city. For example, cameras monitor traffic volume and sensors measure air quality. The collected data is sent to a server via the internet or a dedicated network. Cameras capture images every second, and sensors acquire data every minute.

[0265] Data integration and preprocessing measures

[0266] The server receives data sent from the devices and integrates data from different sources. It centrally manages the data using a database system (e.g., MySQL, PostgreSQL) and performs preprocessing such as data shaping and filtering. Specifically, it matches data based on timestamps to remove incomplete data and noise. This prepares a reliable dataset for subsequent analysis.

[0267] Data analysis and prediction methods using generative artificial intelligence

[0268] The server uses the preprocessed data to analyze and predict the data using generative artificial intelligence (e.g., GPT-4). This allows it to analyze current traffic patterns and build a traffic congestion prediction model. The generative artificial intelligence also simulates future traffic conditions and displays the results on a dashboard. A specific example of its operation is outputting the predicted traffic volume for a specific time period next week.

[0269] Anomaly detection means

[0270] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (such as traffic accidents or the passing of emergency vehicles). If an anomaly is detected, a warning alert is sent immediately via email or SMS.

[0271] Problem solving proposals and implementation methods

[0272] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. Specific countermeasures include adjusting traffic light timing and providing detour route instructions. These proposals are based on the results of evaluations conducted through simulations. The device automatically updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server and implements the proposed countermeasures.

[0273] Citizen response measures

[0274] Users can use their smartphones or computer terminals to access the citizen chat system and ask questions or make inquiries. The server uses generative artificial intelligence to quickly provide appropriate answers to user inquiries. As a specific example of how it works, it generates a response based on real-time data in response to the question, "What is the current traffic congestion situation?"

[0275] Improvement and Feedback Vehicles

[0276] The server constantly monitors the results of the implemented measures, evaluates their effectiveness, and continuously improves the system. By collecting performance data and updating the artificial intelligence model, the accuracy and performance of the system are improved.

[0277] User response using an emotion engine

[0278] The emotion engine evaluates and recognizes emotions in real time when a user makes an inquiry through voice tone, text patterns, and facial expression recognition. The server uses this emotional data to provide a more emotionally appropriate response through generative AI. For example, if the server detects anxiety or impatience in a response such as "I'm going to be delayed due to traffic congestion," it will calmly offer advice on alternative routes and timetables.

[0279] The following are examples of prompt sentences:

[0280] "Please predict traffic conditions in the city and suggest the best route. The current time is 2:00 PM and the destination is City Hall."

[0281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0282] Step 1: Data collection

[0283] The terminals collect traffic and environmental data from various sensors and cameras installed in the city. Specifically, cameras that count traffic volume and sensors that measure air quality are used. The input is real-time data from each sensor and camera. The output is the collected data sent to a server via a network.

[0284] Step 2: Send data

[0285] The terminal sends the collected data to a server via the Internet or a dedicated network. Specific examples of operation include sending data using the HTTP protocol or MQTT protocol. The input is the data collected on the terminal. The output is a series of data sent to the server.

[0286] Step 3: Data Integration

[0287] The server receives data sent from the devices and integrates data from different sources. As input, there is data received from the devices with different formats and timestamps. As output, there is an integrated and consistent data set. Specifically, the data is centrally managed using a database system (e.g., MySQL, PostgreSQL).

[0288] Step 4: Data Preprocessing

[0289] The server performs preprocessing on the integrated data. The input is the integrated data. The output is a preprocessed, clean dataset. Specifically, the server uses the Python pandas library to shape and filter the data, removing incomplete data and noise.

[0290] Step 5: Data analysis and prediction

[0291] The server uses the preprocessed data to analyze and make predictions using generative artificial intelligence (such as GPT-4). The input is the preprocessed, clean data. The output is a prediction of current and future traffic patterns and conditions. As a specific example of how it works, the predicted traffic volume for a specific time period next week is displayed on a dashboard.

[0292] Step 6: Anomaly detection

[0293] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies. The input is the real-time data stream. The output is detected anomalous events and warning alerts. If an anomaly is detected, relevant parties are immediately notified via email or SMS.

[0294] Step 7: Propose and implement solutions

[0295] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The input is the anomaly detection results. The output is the proposed countermeasures and the results of their implementation. Specific countermeasures include adjusting traffic light timing and setting up detour routes. The terminal updates the settings of traffic lights and related equipment based on the optimization parameters from the server.

[0296] Step 8: Public response

[0297] Users can access the citizen chat system using smartphones or computer terminals to ask questions or make inquiries. The input is the user's inquiry. The output is an answer generated by generative artificial intelligence. For example, a question such as "What is the current traffic congestion situation?" will be answered based on real-time data.

[0298] Step 9: Improve and Feedback

[0299] The server constantly monitors the results of the implemented measures, evaluates their effectiveness, and continuously improves the system. The input is a variety of performance data. The output is an updated AI model and improved system performance. Updating the AI ​​model based on the performance data improves the accuracy of the entire system.

[0300] Step 10: User interaction with emotion engine

[0301] The emotion engine evaluates and recognizes emotions in real time when a user makes an inquiry through voice tone, text patterns, facial expression recognition, etc. The input is the user's voice and text data. The output is a response that corresponds to the user's emotional state. The server takes in this emotional data and the generative artificial intelligence provides a response that is more appropriate to the emotion. As a specific example of how it works, if the server senses anxiety or impatience in response to an inquiry such as "I'm going to be late because of traffic congestion," it will provide advice on alternative routes and time in a calm tone.

[0302] (Application example 2)

[0303] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0304] With the recent advancement of urbanization, the use of self-driving vehicles has become more widespread, but problems such as traffic congestion and accidents remain unresolved. Furthermore, methods for reducing the stress and anxiety felt by users of self-driving vehicles have not yet been fully established. Therefore, there is a need for a system that can predict traffic conditions in real time, provide optimal routes, and respond appropriately to users' emotional states.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0306] In this invention, the server includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, an improvement and feedback means, an emotion recognition means for recognizing the driver's emotion, a traffic pattern analysis and traffic prediction means using generative artificial intelligence, and an anomaly detection and optimal route proposal means. This makes it possible to predict traffic conditions in real time, provide optimal routes, and take appropriate measures according to the user's emotional state.

[0307] "Data collection means" refers to the means of collecting traffic data, environmental data, etc. in real time from devices such as sensors and cameras within the city.

[0308] "Data integration and pre-processing means" means for receiving data transmitted from the terminals, integrating data from different sources, and shaping and filtering the data to remove incomplete data and noise.

[0309] "Data analysis and prediction means using generative artificial intelligence" refers to a means of analyzing pre-processed data using generative artificial intelligence to analyze current traffic patterns and construct a traffic congestion prediction model.

[0310] An "anomaly detection method" is a method that monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents).

[0311] "Proposal and implementation of solutions to the problem" refers to a method in which the generative artificial intelligence proposes optimal solutions to detected anomalies, then conducts simulations to evaluate the effectiveness of the proposed solutions, and then implements the optimal solutions.

[0312] "Citizen response means" refers to the means by which users can access the citizen chat system using their smartphones or terminals and make inquiries when they have problems or questions.

[0313] "Improvement and feedback measures" are measures to monitor the results of the measures implemented, evaluate their effectiveness, and continuously improve the generative artificial intelligence model.

[0314] The "emotion recognition means for recognizing the driver's emotions" is a means for evaluating the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc., and optimizing the response based on the emotional data.

[0315] "Means for analyzing traffic patterns and forecasting traffic using generative artificial intelligence" refers to means for analyzing current traffic patterns using generative artificial intelligence, simulating future traffic conditions, and displaying the results on a dashboard.

[0316] The "means for detecting anomalies and proposing optimal routes" is a means for detecting unexpected anomalies using generative artificial intelligence and proposing alternative routes, times, etc. as optimal countermeasures.

[0317] The present invention relates to an urban traffic information system for autonomous vehicles, which is a system that integrates and provides functions such as data collection, data integration, prediction, anomaly detection, countermeasure proposal, user response, emotion recognition, etc. This system is implemented as follows.

[0318] Data collection methods

[0319] Devices (e.g., cameras and sensors) installed in autonomous vehicles collect traffic and environmental data within the city in real time. The collected data is sent to a server using wireless communication. This process uses the Internet or a dedicated network.

[0320] Data integration and preprocessing measures

[0321] The server receives the data sent from the devices and processes it to integrate data from different sources, during which pre-processing such as data shaping and filtering is performed to remove incomplete data and noise.

[0322] Data analysis and prediction methods using generative artificial intelligence

[0323] Once preprocessing is complete, the data is analyzed by a generative AI running on the server. The generative AI analyzes traffic patterns and builds a predictive model for traffic congestion. The generative AI also simulates future traffic conditions and displays the results on a dashboard. For example, a model such as GPT-4 is used as the generative AI model.

[0324] Anomaly detection means

[0325] The server monitors the data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[0326] Problem solving proposals and implementation methods

[0327] If an anomaly is detected, the generative AI proposes optimal countermeasures, such as adjusting traffic light timings or reconfiguring traffic routes. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures and the optimal countermeasures are implemented. The optimization parameters are sent to a terminal in the vehicle and applied in real time.

[0328] Citizen response measures

[0329] Users can access the citizen chat system using their smartphones or in-car devices and make inquiries. The server receives inquiries from users via the chat system, and generative artificial intelligence generates the optimal answer. As a specific example, in response to an inquiry such as "Please tell me the current congestion situation," real-time traffic data and predicted results are provided.

[0330] Improvement and Feedback Vehicles

[0331] The server monitors the results of implemented measures and evaluates their effectiveness, collects quantitative performance data to continuously improve the generative AI model, and continues to collect data over time to provide data for detecting new patterns and anomalies.

[0332] emotion recognition means

[0333] The emotion recognition means evaluates the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc. This emotional data is acquired when the user makes an inquiry. For example, when a user makes an inquiry such as "I'm going to be late for work because of traffic jams," the emotion recognition means senses impatience and stress from the user's voice tone and choice of words. Based on this information, the generative artificial intelligence on the server generates the optimal answer and creates an appropriate message to alleviate the user's anxiety.

[0334] Prompt Sentence Examples

[0335] "Based on current city traffic data, predict the traffic situation for the next hour and indicate the possibility of traffic congestion and accidents."

[0336] This system can significantly improve operational efficiency and driver happiness in autonomous vehicles.

[0337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0338] Step 1:

[0339] The autonomous vehicle's terminal collects real-time traffic and environmental data from the vehicle's onboard cameras and sensors. The collected data is then transmitted by the terminal to a server via wireless communication. Input data at this stage include traffic volume, temperature, humidity, wind speed, etc. As an output, data packets are sent to the server.

[0340] Step 2:

[0341] The server receives the data sent from the devices and integrates the data from different sources. Specifically, pre-processing such as data shaping and filtering is performed to remove incomplete data and noise. The input at this stage is the raw data sent from the devices, and the output is shaped and clean data.

[0342] Step 3:

[0343] The server provides the preprocessed data to a generative AI, which analyzes and predicts traffic patterns. The prompt is "Analyze the current traffic patterns and predict them for the next hour." The input is preprocessed traffic data, and the generative AI outputs a traffic congestion prediction model.

[0344] Step 4:

[0345] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (traffic congestion, accidents, etc.). Real-time traffic data is used as input, and if an anomaly is detected, the type of anomaly and its location are output. The server issues a warning based on this.

[0346] Step 5:

[0347] When an anomaly is detected, the server uses generative artificial intelligence to propose optimal countermeasures. For example, it may suggest adjusting traffic light timings or recommending alternative routes. Simulations are performed to evaluate the effectiveness of the proposed countermeasures. The input at this stage is the anomaly detection information, and the output is the optimal countermeasure.

[0348] Step 6:

[0349] The server sends the optimization parameters to the terminal in the vehicle, which then applies the proposed measures in real time. The terminal updates the in-vehicle system based on the received optimization parameters and performs traffic light and route adjustments. The optimization parameters are the input, and the results of the adjustments are the output.

[0350] Step 7:

[0351] Users access the citizen chat system using their smartphones or in-car devices and make inquiries. The server receives the user's inquiry via the chat system, and the generative AI generates the optimal answer. The input is the user's inquiry information, and the output is the answer provided by the generative AI.

[0352] Step 8:

[0353] The server monitors the results of the implemented measures and evaluates their effectiveness. It continues to collect data over the long term and provides data to detect new patterns and anomalies. The input is data on the effectiveness of the implemented measures, and the output is information that will be useful for further improvements and feedback.

[0354] Step 9:

[0355] The emotion recognition means evaluates the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc. When the user makes a query, this emotional data is sent to the server. The input is the user's emotional data, and the output is an optimal answer that matches the user's emotional state.

[0356] In this way, each processing step involves data collection, integration, analysis, anomaly detection, proposals, execution, user support, improvement feedback, and emotion recognition, resulting in a system that can predict traffic conditions in real time, provide optimal routes, and respond appropriately to the driver's emotional state.

[0357] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0358] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0359] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0360] [Second embodiment]

[0361] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0362] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0363] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0364] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0365] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0366] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0367] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0368] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0369] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0370] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0371] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0372] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0373] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means.

[0374] Data collection methods

[0375] The terminals collect real-time data on vehicle flow, speed, congestion, etc. from traffic sensors (cameras and devices) installed at intersections and major traffic points within the city. The collected data is then sent to a server via the internet or a dedicated network.

[0376] Data integration and preprocessing measures

[0377] The server receives traffic data sent by the terminals and integrates the data collected from different sources, and in the process performs pre-processing such as data shaping and filtering to remove incomplete data and noise.

[0378] Data analysis and prediction methods using generative artificial intelligence

[0379] Once preprocessed, the data is analyzed by a generative AI running on the server. This allows for the analysis of current traffic patterns and the creation of a traffic congestion prediction model. The generative AI also simulates future traffic conditions and displays the results on a dashboard.

[0380] Anomaly detection means

[0381] The server monitors the flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and immediately issues an alert.

[0382] Problem solving proposals and implementation methods

[0383] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. For example, specific improvement plans such as adjusting traffic light timings or reconfiguring traffic routes are proposed. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures. Based on the evaluation results, the optimal countermeasures are implemented.

[0384] The terminal updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, for example, changing the traffic light control pattern.

[0385] Citizen response measures

[0386] If users have any problems or questions about the safety or traffic conditions in the city, they can use their smartphones or other devices to access the citizen chat system and make inquiries to the operator.

[0387] The server receives inquiries from users via the chat system and generates optimal answers using generative artificial intelligence. For example, in response to a query such as "What is the current traffic situation?", the server provides real-time traffic data and prediction results.

[0388] Improvement and Feedback Vehicles

[0389] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0390] The devices continue to collect data over time, providing a baseline for detecting new patterns and anomalies.

[0391] The comprehensive combination of these capabilities will enable accurate and rapid management of smart cities using generative artificial intelligence, improving the efficiency and happiness of citizens' lives.

[0392] The processing flow will be explained below.

[0393] Step 1:

[0394] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0395] Step 2:

[0396] The server receives the data sent from the terminal, and the received data is first preprocessed by a data integration and preprocessing means to integrate data from different sources and remove incomplete data and noise.

[0397] Step 3:

[0398] The server then supplies the preprocessed data to a generative AI, which uses the data to analyze traffic patterns and build a predictive model of traffic congestion. Based on the model, future traffic conditions are simulated and the results are displayed on a dashboard.

[0399] Step 4:

[0400] The server monitors the data in real time and uses generative artificial intelligence to detect anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[0401] Step 5:

[0402] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The proposed countermeasures (e.g., adjusting traffic signal times or reconfiguring traffic routes) are simulated to evaluate their effectiveness.

[0403] Step 6:

[0404] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0405] Step 7:

[0406] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0407] Step 8:

[0408] The server receives inquiries from users through the chat system, and the generative AI provides the best possible answer. For example, in response to a question like, "What is the current traffic situation?", it presents real-time traffic data and prediction results.

[0409] Step 9:

[0410] The server monitors the results of proposed and implemented measures and evaluates their effectiveness. Quantitative performance data is collected and used to continuously improve the generative artificial intelligence model.

[0411] Step 10:

[0412] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0413] This series of processing flows will enable smart city management using generative artificial intelligence to be realized more efficiently and effectively.

[0414] Example 1

[0415] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0416] Urban traffic management requires accurate data collection, prediction, anomaly detection, and countermeasure implementation in real time. Current systems often experience delays in data processing and analysis, making it difficult to respond quickly. They also lack the means to provide appropriate answers to citizen inquiries in real time. This results in reduced operational efficiency and hinders improvements in the efficiency and happiness of citizens' lives.

[0417] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0418] In this invention, the server includes: a data collection means that collects data such as vehicle flow rate, speed, and congestion level in real time using sensors installed at intersections and major traffic points within the city; a data integration and preprocessing means that receives the data collected by the data collection means, integrates data collected from different sources, and performs preprocessing such as data shaping and filtering; a data analysis and prediction means that uses generative artificial intelligence to analyze the data preprocessed by the data integration and preprocessing means, and analyzes traffic patterns and builds a traffic congestion prediction model; anomaly detection means that monitors the continuous flow of data in real time and detects unexpected abnormalities using generative artificial intelligence; problem solution proposal and implementation means that uses generative artificial intelligence to propose optimal measures for detected abnormalities, simulates the measures, evaluates their effectiveness, and implements them; citizen response means that allows users to access the citizen chat system via a smartphone or terminal, make inquiries to the operator, and the generative artificial intelligence generates optimal answers to the inquiries; and an improvement and feedback means that monitors the results of the implemented measures, evaluates their effectiveness, collects performance data, and continuously improves the generative artificial intelligence model. This will enable more efficient traffic management in cities, faster response to abnormalities, and real-time responses to inquiries from citizens.

[0419] "Data collection means" refers to the collection of real-time data on vehicle flow, speed, congestion, etc. using sensors installed at intersections and major traffic points within the city.

[0420] The "data integration and pre-processing means" is a means for receiving data collected by the data collection means, integrating data collected from different sources, and performing pre-processing such as shaping and filtering the data.

[0421] The "data analysis and prediction means" is a means for analyzing data preprocessed by the data integration and preprocessing means using generative artificial intelligence to analyze traffic patterns and construct a traffic congestion prediction model.

[0422] An "anomaly detection method" is a method that monitors data that is constantly flowing in real time and detects unexpected anomalies using generative artificial intelligence.

[0423] "Proposal and implementation of solutions to the problem" refers to a method in which generative artificial intelligence proposes optimal solutions to detected anomalies, simulates those solutions, evaluates their effectiveness, and then implements them.

[0424] The "citizen response means" is a means by which users access the citizen chat system via their smartphones or terminals, make inquiries to the operator, and have the generative artificial intelligence generate the optimal response to those inquiries.

[0425] "Improvement and feedback measures" are measures to monitor the results of implemented measures, evaluate their effectiveness, and collect performance data to continuously improve the generative artificial intelligence model.

[0426] This invention relates to a system for streamlining urban traffic management and improving the efficiency and happiness of civic life. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means.

[0427] Data collection methods

[0428] The devices use sensors installed at intersections and major traffic points within a city to collect real-time data on vehicle flow, speed, congestion, and other factors. For example, traffic sensors may be cameras or other devices that capture video every second and quantify the number and speed of vehicles. This data is then sent to a server via Wi-Fi or a dedicated line.

[0429] Data integration and preprocessing measures

[0430] The server receives the raw data sent from the devices and consolidates it. It then shapes and filters the data to remove missing data and noise. For example, it applies a noise filter to data collected overnight to fill in outliers and missing values. It also removes duplicate data sent from the same device.

[0431] Data analysis and prediction methods using generative artificial intelligence

[0432] The preprocessed data is then analyzed by a generative artificial intelligence (AI) running on a server. This AI model analyzes traffic patterns and builds a model to predict future traffic congestion. The AI ​​is trained using traffic data from the past few weeks, then simulates traffic flow for the next hour and displays the predicted results on a dashboard.

[0433] Prompt Sentence Examples

[0434] "Based on the data from the past hour, please display the predicted number of vehicles passing through intersection A in the next hour."

[0435] Anomaly detection means

[0436] The server monitors the continuous flow of data in real time and uses generative artificial intelligence to detect anomalies, such as a sudden increase in traffic volume that deviates from normal patterns, and immediately sends an alert to administrators.

[0437] Problem solving proposals and implementation methods

[0438] The server uses generative artificial intelligence to propose optimal countermeasures for the detected anomalies. Specific countermeasures are proposed, such as adjusting traffic light timings or reconfiguring traffic routes. Simulations are then performed to evaluate the effectiveness of these countermeasures and implement the optimal countermeasures. The terminal then updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server.

[0439] Prompt Sentence Examples

[0440] "Please display the current traffic congestion situation at intersection A in real time."

[0441] Citizen response measures

[0442] Users can access the chat system for citizens using their smartphones or other devices and make inquiries to the operator. For example, they can ask, "Please tell me the current congestion situation."

[0443] The server receives user inquiries via a chat system and generates optimal answers using generative artificial intelligence, providing real-time traffic data and prediction results.

[0444] Improvement and Feedback Vehicles

[0445] The server monitors the results of the implemented measures and evaluates their effectiveness, collecting quantitative performance data to continuously improve the generative AI model. The terminal continues to collect data over the long term, providing a foundation for detecting new patterns and anomalies.

[0446] These capabilities will enable accurate and swift management of smart cities using generative artificial intelligence, streamlining urban traffic management and improving the efficiency and happiness of citizens' lives.

[0447] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0448] Step 1: Data collection methods

[0449] The devices use sensors installed at intersections and major traffic points within the city to collect real-time data on vehicle flow, speed, congestion, and other factors. For example, cameras are used as traffic sensors. The cameras capture video every second and quantify the number and speed of vehicles from the video data. This data is then sent to a server via Wi-Fi or a dedicated line.

[0450] Input: Raw data (video data) from sensors such as cameras

[0451] Data processing: Analyzes video data and quantifies vehicle flow, speed, and congestion

[0452] Output: digitized traffic data

[0453] Step 2: Data integration and preprocessing measures

[0454] The server receives traffic data sent from the terminals, then integrates the data collected from different sources and performs pre-processing such as data shaping and filtering, eliminating missing data, noise, and redundant data.

[0455] Input: Traffic data collected in real time

[0456] Data processing: data shaping, filtering, missing value completion, noise removal, and duplicate data removal.

[0457] Output: Preprocessed and clean traffic data

[0458] Step 3: Data analysis and prediction methods using generative artificial intelligence

[0459] Generative AI running on the server analyzes the pre-processed data. The AI ​​model analyzes traffic patterns and builds a model to predict future traffic congestion. The prediction results are displayed on a dashboard in a visually understandable format for stakeholders.

[0460] Input: Preprocessed and clean traffic data

[0461] Data Computing: Generative AI to analyze traffic patterns and build predictive models

[0462] Output: Traffic congestion prediction results (dashboard display)

[0463] Step 4: Anomaly detection methods

[0464] The server monitors the flow of data in real time and uses generative artificial intelligence to detect anomalies, such as sudden increases in traffic volume or congestion that deviate from normal patterns, and immediately issues an alert.

[0465] Input: Real-time traffic data

[0466] Data calculation: Traffic pattern anomaly detection

[0467] Output: Warning notification of abnormality detection

[0468] Step 5: Proposal and implementation of solutions

[0469] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. Specific examples include adjusting traffic light timings and reconfiguring traffic routes. The proposals are evaluated through simulations, and the optimal countermeasures are implemented. The terminal updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server.

[0470] Input: Anomaly detection data, traffic prediction model

[0471] Data calculation: Generative AI proposes countermeasures and evaluates simulation results

[0472] Output: Optimal measures to be implemented (changes to traffic lights and equipment settings)

[0473] Step 6: Public Response Measures

[0474] Users access the chat system for citizens using their smartphones or other devices and send inquiries to the operator. The server receives inquiries from users via the chat system and generates optimal answers using generative artificial intelligence. For example, in response to an inquiry such as "What is the current congestion situation?", the server provides real-time traffic data and prediction results.

[0475] Input: User query

[0476] Data calculation: Real-time traffic data analysis, answer generation using generative AI

[0477] Output: Providing answers to users (traffic congestion status, etc.)

[0478] Step 7: Improvement and feedback measures

[0479] The server monitors the results of the implemented measures and evaluates their effectiveness. It collects quantitative performance data and continuously improves the generative AI model. The device continues to collect data over the long term, providing a foundation for detecting new patterns and anomalies.

[0480] Input: Traffic data and performance data after implementing measures

[0481] Data calculation: Evaluating the effectiveness of countermeasures and improving AI models

[0482] Output: improved AI models, data for new pattern detection

[0483] (Application example 1)

[0484] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0485] The goal is to provide a system that optimizes traffic conditions by streamlining the collection and analysis of traffic data within cities, quickly detecting traffic congestion and accidents, and immediately proposing and implementing appropriate countermeasures. Another challenge is to create a system that allows users to check traffic conditions in real time and receive optimal route suggestions.

[0486] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0487] In this invention, the server includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a user response means, an improvement and feedback means, a real-time traffic data collection and analysis means, a means for proposing an optimal traffic route, and a user inquiry response means using generative artificial intelligence. This allows for real-time understanding of traffic conditions within a city, enabling efficient traffic management and optimal route proposals.

[0488] A "data collection means" is a device that collects data such as vehicle flow, speed, and congestion in real time from traffic sensors (cameras and devices) installed at intersections and major traffic points within a city, and transmits this data to a server via the Internet or a dedicated network.

[0489] "Data integration and pre-processing means" refers to the server's function of receiving traffic data sent from the terminal, integrating data collected from different sources, and performing pre-processing such as shaping and filtering the data to remove incomplete data and noise.

[0490] "Data analysis and prediction means using generative artificial intelligence" refers to a server function that uses pre-processed data to analyze current traffic patterns, build a predictive model of traffic congestion, and simulate future traffic conditions.

[0491] "Anomaly detection means" is a server function that monitors the flow of data in real time, uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents), and immediately issues an alert when an anomaly is detected.

[0492] "Proposal and implementation of problem solutions" refers to the functions of the server and terminal in which generative artificial intelligence proposes optimal solutions for abnormalities detected by the server, evaluates the effectiveness of the proposed solutions, and implements the optimal solutions based on the evaluation results.

[0493] The "user response means" is a function that allows users to access the citizen chat system using a smartphone or other device, make inquiries to the operator, and the server generates the most appropriate response using generative artificial intelligence.

[0494] "Improvement and feedback measures" refers to the function by which the server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0495] "Real-time traffic data collection and analysis means" refers to the function of the server and related devices to collect data on traffic conditions within the city in real time and analyze that data.

[0496] The "means of proposing the optimal transportation route" is a function that generates and proposes the optimal route based on the user's current location information and destination information, using the transportation data collected and analyzed by the server.

[0497] "Means for responding to user inquiries using generative artificial intelligence" is a function that uses generative artificial intelligence to generate optimal answers to users' inquiries about traffic conditions and other matters, and responds in real time.

[0498] A system for realizing this invention will now be described. The main elements of the system are a server, a terminal, and a user, and these elements cooperate to collect, analyze, predict, and respond to traffic data in real time.

[0499] The server includes the following means:

[0500] 1. Data collection means: This involves collecting data such as vehicle flow, speed, and congestion in real time from traffic sensors (cameras and devices) installed at intersections and major traffic points within the city, and sending it to a server via the Internet or a dedicated network.

[0501] 2. Data integration and pre-processing means: The server receives the traffic data sent from the terminals, integrates the data collected from different sources, and performs pre-processing such as data shaping and filtering.

[0502] 3. Data analysis and prediction using generative artificial intelligence: Preprocessed data is used to analyze current traffic patterns and build predictive models of traffic congestion, simulating future traffic conditions.

[0503] 4. Anomaly detection: The server monitors the flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents), issuing an immediate alert when an anomaly is detected.

[0504] 5. Proposal and implementation of countermeasures: The generative artificial intelligence proposes optimal countermeasures for the abnormalities detected by the server, evaluates the effectiveness of the proposed countermeasures, and implements the optimal countermeasures.

[0505] 6. User response method: Users access the citizen chat system using their smartphones or devices and make inquiries to the operator, and the server generates the most appropriate response using generative artificial intelligence.

[0506] 7. Improvement and feedback measures: The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0507] 8. Real-time traffic data collection and analysis means: This has the functions of a server and related devices for collecting data on traffic conditions within the city in real time and analyzing that data.

[0508] 9. Means for suggesting optimal transportation routes: Based on the transportation data collected and analyzed by the server, the optimal route is generated and suggested based on the user's current location information and destination information.

[0509] 10. A means of responding to user inquiries using generative artificial intelligence: Generative artificial intelligence will generate optimal answers to user inquiries about traffic conditions and other matters and respond in real time.

[0510] The hardware used includes traffic sensors, cameras, devices, and smartphones, and the software used includes the Python programming language, generative AI model APIs (such as GPT-3), server-side programs (e.g., Django, Flask), and databases (e.g., PostgreSQL, MySQL).

[0511] As a concrete example, let's consider the case where a user makes a query such as "Please tell me the current congestion situation." The following is an example of a prompt for this query:

[0512] Prompt statement:

[0513] User inquiry: What is the current congestion situation?

[0514] A generative AI model would take this prompt and generate a response like this:

[0515] Currently, there is a traffic jam on the main roads of the city, especially in the western part of the city, and traffic is very congested. The best route is to avoid road X and take road Y.

[0516] In this way, the system created can analyze traffic data within a city and provide users with optimal traffic information in real time.

[0517] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0518] Step 1:

[0519] The terminal collects data such as vehicle flow, speed, and congestion from traffic sensors in the city in real time and sends it to a server. The input is the raw data obtained from the traffic sensors, and the output is sending this data to the server.

[0520] Step 2:

[0521] The server receives traffic data sent from the terminal. The input is the traffic data sent from the terminal, and the output is the data received by the server. This data is collected from different sources and requires pre-processing for integration.

[0522] Step 3:

[0523] The server integrates the received raw data and performs preprocessing such as data shaping and filtering. The input is the received raw data, and the output is the shaped data after preprocessing. Specific operations include completing incomplete data and removing noise.

[0524] Step 4:

[0525] The server uses the preprocessed data to perform data analysis and predictions using generative artificial intelligence. The input is the preprocessed data, and the output is an analysis of current traffic patterns and a traffic congestion prediction model. In this case, the server uses the generative artificial intelligence model to analyze trends and patterns in the data.

[0526] Step 5:

[0527] The server monitors the continuous flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents). The input is traffic data updated in real time, and the output is the anomaly detection results. Detected anomalies can immediately trigger an alert.

[0528] Step 6:

[0529] Based on the results of the anomaly detection by the server, the generative AI proposes optimal countermeasures. The input is the anomaly detection results, and the output is countermeasure proposals. Specific countermeasures include adjusting traffic light timings and reconfiguring traffic routes.

[0530] Step 7:

[0531] The server simulates the effectiveness of the proposed measures and implements the optimal measures based on the evaluation results. The input is the proposed measures, and the output is the optimization parameters to be implemented. These optimization parameters are sent to the terminal, and the settings of traffic lights and related equipment are updated.

[0532] Step 8:

[0533] Users access the citizen chat system using their smartphones or other devices and make inquiries about traffic conditions. The input is the user's inquiry, and the output is the optimal answer generated by generative AI. For example, in response to an inquiry such as "Please tell me the current congestion situation," the system responds with real-time traffic data and forecast results.

[0534] Step 9:

[0535] The server monitors the results of the implemented measures and evaluates their effectiveness. The input is performance data of the implemented measures, and the output is an evaluation of the measures' effectiveness and feedback. This provides data for continuously improving the generative AI model.

[0536] Step 10:

[0537] The server continues to collect data over time, providing a basis for detecting new patterns and anomalies. The input is continuously collected traffic data, and the output is an improved model, which increases the accuracy and effectiveness of the system.

[0538] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0539] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a citizen response means, an improvement and feedback means, and also an emotion engine that recognizes user emotions.

[0540] Data collection methods

[0541] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0542] Data integration and preprocessing measures

[0543] The server receives the data sent by the devices and integrates the data from different sources, performing pre-processing such as data shaping and filtering to remove incomplete data and noise.

[0544] Data analysis and prediction methods using generative artificial intelligence

[0545] Once preprocessed, the data is analyzed by a generative AI running on the server. This allows for the analysis of current traffic patterns and the creation of a traffic congestion prediction model. The generative AI also simulates future traffic conditions and displays the results on a dashboard.

[0546] Anomaly detection means

[0547] The server monitors the data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and immediately issues an alert if an anomaly is detected.

[0548] Problem solving proposals and implementation methods

[0549] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. For example, specific improvement plans such as adjusting traffic light timings or reconfiguring traffic routes are proposed. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures. Based on the evaluation results, the optimal countermeasures are implemented.

[0550] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0551] Citizen response measures

[0552] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0553] The server receives inquiries from users via the chat system, and the generative AI generates the optimal answer. For example, in response to a query such as "What is the current traffic situation?", it provides real-time traffic data and prediction results.

[0554] Improvement and Feedback Vehicles

[0555] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0556] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0557] User response using an emotion engine

[0558] The emotion engine recognizes the user's emotions in real time. This emotion data is captured when the user makes a query. The emotion engine assesses the user's emotional state using voice tone, character usage patterns, facial expression recognition, etc.

[0559] The server optimizes citizen response methods based on the emotional data provided by the emotion engine. For example, if a user is feeling stressed, the generative AI will generate a more flexible and reassuring response.

[0560] For example, when a user inquires about being late for work due to traffic jams on their smartphone, the emotion engine can sense the user's impatience from their tone of voice and the way they speak. Based on this information, the server uses generative artificial intelligence to create an appropriate message that offers the best alternative route and time, and alleviates the user's anxiety.

[0561] This system will significantly improve the efficiency of city management and the happiness of its citizens. This series of system processes will enable smart cities to respond more flexibly in real time, helping to realize sustainable urban life.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0565] Step 2:

[0566] The server receives the data sent from the terminal, and the data integration and pre-processing means integrates the data from different sources and pre-processes the data to remove incomplete data and noise.

[0567] Step 3:

[0568] The server then supplies the preprocessed data to a generative AI, which uses the data to analyze traffic patterns, build a predictive model for traffic congestion, and simulate future traffic conditions, displaying the results on a dashboard.

[0569] Step 4:

[0570] The server monitors the data in real time and uses generative artificial intelligence to detect anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[0571] Step 5:

[0572] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The proposed countermeasures (e.g., adjusting traffic signal times or reconfiguring traffic routes) are simulated to evaluate their effectiveness.

[0573] Step 6:

[0574] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0575] Step 7:

[0576] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0577] Step 8:

[0578] The emotion engine recognizes the user's emotional state from their voice tone, character usage patterns, facial expressions, etc. when they make a query. The recognized emotion data is sent to the server.

[0579] Step 9:

[0580] The server uses generative artificial intelligence to generate optimal responses based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, it generates a flexible and reassuring response.

[0581] Step 10:

[0582] The server then provides the generated answers to the user through a chat system. For example, in response to a question such as "What is the current traffic congestion situation?", the server provides real-time traffic data and forecast results, thereby easing the user's anxiety.

[0583] Step 11:

[0584] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0585] Step 12:

[0586] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0587] This series of processing flows will enable smart city management utilizing generative artificial intelligence and emotion engines to be realized more efficiently and effectively.

[0588] Example 2

[0589] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0590] There is a need to effectively collect, process, and analyze various data within cities to improve the quality of life and happiness of citizens. However, current systems require time-consuming data integration and preprocessing, making it difficult to quickly detect anomalies or propose optimal countermeasures. In addition, there is a lack of consideration for user emotions, making it difficult to improve user satisfaction.

[0591] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0592] In this invention, the server includes means for collecting data, means for integrating and preprocessing the collected data, means for analyzing and predicting data using generative artificial intelligence, means for detecting anomalies in real time, means for proposing and implementing countermeasures based on the results of the anomaly detection, means for responding to inquiries from users, means for providing feedback on the effectiveness of the countermeasures and improving them, and means for recognizing user emotions. This enables efficient and flexible data management and citizen support within the city, thereby improving the happiness and quality of life of citizens.

[0593] "Means for collecting data" refers to devices and systems for collecting various data from devices such as sensors and cameras installed within the city.

[0594] "Means for integrating and pre-processing collected data" refers to devices and systems that receive data sent from terminals, consolidate data from different sources, and perform shaping and filtering to remove incomplete data and noise.

[0595] "Means for analyzing and predicting data using generative artificial intelligence" refers to devices and systems that use generative artificial intelligence to analyze data and predict future situations based on collected and preprocessed data.

[0596] "Means for detecting anomalies in real time" refers to devices and systems that use generative artificial intelligence to monitor data sequentially and immediately detect unexpected anomalies.

[0597] The "means for proposing and implementing solutions to problems based on anomaly detection results" refers to devices and systems that use generative artificial intelligence to propose optimal solutions when an anomaly is detected and then implement those proposals.

[0598] "Means for responding to inquiries from users" refers to devices and systems that enable generative artificial intelligence to provide appropriate answers to inquiries made by users via their terminals.

[0599] "Means for feedback and improvement of the effectiveness of measures" refers to devices and systems for monitoring the results of implemented measures, evaluating their effectiveness, and continuously improving the system.

[0600] The "means for recognizing user emotions" refers to a device or system for evaluating and recognizing the emotional state of a user when making a query through voice tone, character patterns, facial expression recognition, etc.

[0601] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system is an apparatus and system that includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a citizen response means, an improvement and feedback means, and an emotion engine that recognizes user emotions.

[0602] Data collection methods

[0603] The terminals collect real-time traffic and environmental data from sensors, cameras, and other devices installed throughout the city. For example, cameras monitor traffic volume and sensors measure air quality. The collected data is sent to a server via the internet or a dedicated network. Cameras capture images every second, and sensors acquire data every minute.

[0604] Data integration and preprocessing measures

[0605] The server receives data sent from the devices and integrates data from different sources. It centrally manages the data using a database system (e.g., MySQL, PostgreSQL) and performs preprocessing such as data shaping and filtering. Specifically, it matches data based on timestamps to remove incomplete data and noise. This prepares a reliable dataset for subsequent analysis.

[0606] Data analysis and prediction methods using generative artificial intelligence

[0607] The server uses the preprocessed data to analyze and predict the data using generative artificial intelligence (e.g., GPT-4). This allows it to analyze current traffic patterns and build a traffic congestion prediction model. The generative artificial intelligence also simulates future traffic conditions and displays the results on a dashboard. A specific example of its operation is outputting the predicted traffic volume for a specific time period next week.

[0608] Anomaly detection means

[0609] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (such as traffic accidents or the passing of emergency vehicles). If an anomaly is detected, a warning alert is sent immediately via email or SMS.

[0610] Problem solving proposals and implementation methods

[0611] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. Specific countermeasures include adjusting traffic light timing and providing detour route instructions. These proposals are based on the results of evaluations conducted through simulations. The device automatically updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server and implements the proposed countermeasures.

[0612] Citizen response measures

[0613] Users can use their smartphones or computer terminals to access the citizen chat system and ask questions or make inquiries. The server uses generative artificial intelligence to quickly provide appropriate answers to user inquiries. As a specific example of how it works, it generates a response based on real-time data in response to the question, "What is the current traffic congestion situation?"

[0614] Improvement and Feedback Vehicles

[0615] The server constantly monitors the results of the implemented measures, evaluates their effectiveness, and continuously improves the system. By collecting performance data and updating the artificial intelligence model, the accuracy and performance of the system are improved.

[0616] User response using an emotion engine

[0617] The emotion engine evaluates and recognizes emotions in real time when a user makes an inquiry through voice tone, text patterns, and facial expression recognition. The server uses this emotional data to provide a more emotionally appropriate response through generative AI. For example, if the server detects anxiety or impatience in a response such as "I'm going to be delayed due to traffic congestion," it will calmly offer advice on alternative routes and timetables.

[0618] The following are examples of prompt sentences:

[0619] "Please predict traffic conditions in the city and suggest the best route. The current time is 2:00 PM and the destination is City Hall."

[0620] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0621] Step 1: Data collection

[0622] The terminals collect traffic and environmental data from various sensors and cameras installed in the city. Specifically, cameras that count traffic volume and sensors that measure air quality are used. The input is real-time data from each sensor and camera. The output is the collected data sent to a server via a network.

[0623] Step 2: Send data

[0624] The terminal sends the collected data to a server via the Internet or a dedicated network. Specific examples of operation include sending data using the HTTP protocol or MQTT protocol. The input is the data collected on the terminal. The output is a series of data sent to the server.

[0625] Step 3: Data Integration

[0626] The server receives data sent from the devices and integrates data from different sources. As input, there is data received from the devices with different formats and timestamps. As output, there is an integrated and consistent data set. Specifically, the data is centrally managed using a database system (e.g., MySQL, PostgreSQL).

[0627] Step 4: Data Preprocessing

[0628] The server performs preprocessing on the integrated data. The input is the integrated data. The output is a preprocessed, clean dataset. Specifically, the server uses the Python pandas library to shape and filter the data, removing incomplete data and noise.

[0629] Step 5: Data analysis and prediction

[0630] The server uses the preprocessed data to analyze and make predictions using generative artificial intelligence (such as GPT-4). The input is the preprocessed, clean data. The output is a prediction of current and future traffic patterns and conditions. As a specific example of how it works, the predicted traffic volume for a specific time period next week is displayed on a dashboard.

[0631] Step 6: Anomaly detection

[0632] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies. The input is the real-time data stream. The output is detected anomalous events and warning alerts. If an anomaly is detected, relevant parties are immediately notified via email or SMS.

[0633] Step 7: Propose and implement solutions

[0634] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The input is the anomaly detection results. The output is the proposed countermeasures and the results of their implementation. Specific countermeasures include adjusting traffic light timing and setting up detour routes. The terminal updates the settings of traffic lights and related equipment based on the optimization parameters from the server.

[0635] Step 8: Public response

[0636] Users can access the citizen chat system using smartphones or computer terminals to ask questions or make inquiries. The input is the user's inquiry. The output is an answer generated by generative artificial intelligence. For example, a question such as "What is the current traffic congestion situation?" will be answered based on real-time data.

[0637] Step 9: Improve and Feedback

[0638] The server constantly monitors the results of the implemented measures, evaluates their effectiveness, and continuously improves the system. The input is a variety of performance data. The output is an updated AI model and improved system performance. Updating the AI ​​model based on the performance data improves the accuracy of the entire system.

[0639] Step 10: User interaction with emotion engine

[0640] The emotion engine evaluates and recognizes emotions in real time when a user makes an inquiry through voice tone, text patterns, facial expression recognition, etc. The input is the user's voice and text data. The output is a response that corresponds to the user's emotional state. The server takes in this emotional data and the generative artificial intelligence provides a response that is more appropriate to the emotion. As a specific example of how it works, if the server senses anxiety or impatience in response to an inquiry such as "I'm going to be late because of traffic congestion," it will provide advice on alternative routes and time in a calm tone.

[0641] (Application example 2)

[0642] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0643] With the recent advancement of urbanization, the use of self-driving vehicles has become more widespread, but problems such as traffic congestion and accidents remain unresolved. Furthermore, methods for reducing the stress and anxiety felt by users of self-driving vehicles have not yet been fully established. Therefore, there is a need for a system that can predict traffic conditions in real time, provide optimal routes, and respond appropriately to users' emotional states.

[0644] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0645] In this invention, the server includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, an improvement and feedback means, an emotion recognition means for recognizing the driver's emotion, a traffic pattern analysis and traffic prediction means using generative artificial intelligence, and an anomaly detection and optimal route proposal means. This makes it possible to predict traffic conditions in real time, provide optimal routes, and take appropriate measures according to the user's emotional state.

[0646] "Data collection means" refers to the means of collecting traffic data, environmental data, etc. in real time from devices such as sensors and cameras within the city.

[0647] "Data integration and pre-processing means" means for receiving data transmitted from the terminals, integrating data from different sources, and shaping and filtering the data to remove incomplete data and noise.

[0648] "Data analysis and prediction means using generative artificial intelligence" refers to a means of analyzing pre-processed data using generative artificial intelligence to analyze current traffic patterns and construct a traffic congestion prediction model.

[0649] An "anomaly detection method" is a method that monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents).

[0650] "Proposal and implementation of solutions to the problem" refers to a method in which the generative artificial intelligence proposes optimal solutions to detected anomalies, then conducts simulations to evaluate the effectiveness of the proposed solutions, and then implements the optimal solutions.

[0651] "Citizen response means" refers to the means by which users can access the citizen chat system using their smartphones or terminals and make inquiries when they have problems or questions.

[0652] "Improvement and feedback measures" are measures to monitor the results of the measures implemented, evaluate their effectiveness, and continuously improve the generative artificial intelligence model.

[0653] The "emotion recognition means for recognizing the driver's emotions" is a means for evaluating the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc., and optimizing the response based on the emotional data.

[0654] "Means for analyzing traffic patterns and forecasting traffic using generative artificial intelligence" refers to means for analyzing current traffic patterns using generative artificial intelligence, simulating future traffic conditions, and displaying the results on a dashboard.

[0655] The "means for detecting anomalies and proposing optimal routes" is a means for detecting unexpected anomalies using generative artificial intelligence and proposing alternative routes, times, etc. as optimal countermeasures.

[0656] The present invention relates to an urban traffic information system for autonomous vehicles, which is a system that integrates and provides functions such as data collection, data integration, prediction, anomaly detection, countermeasure proposal, user response, emotion recognition, etc. This system is implemented as follows.

[0657] Data collection methods

[0658] Devices (e.g., cameras and sensors) installed in autonomous vehicles collect traffic and environmental data within the city in real time. The collected data is sent to a server using wireless communication. This process uses the Internet or a dedicated network.

[0659] Data integration and preprocessing measures

[0660] The server receives the data sent from the devices and processes it to integrate data from different sources, during which pre-processing such as data shaping and filtering is performed to remove incomplete data and noise.

[0661] Data analysis and prediction methods using generative artificial intelligence

[0662] Once preprocessing is complete, the data is analyzed by a generative AI running on the server. The generative AI analyzes traffic patterns and builds a predictive model for traffic congestion. The generative AI also simulates future traffic conditions and displays the results on a dashboard. For example, a model such as GPT-4 is used as the generative AI model.

[0663] Anomaly detection means

[0664] The server monitors the data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[0665] Problem solving proposals and implementation methods

[0666] If an anomaly is detected, the generative AI proposes optimal countermeasures, such as adjusting traffic light timings or reconfiguring traffic routes. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures and the optimal countermeasures are implemented. The optimization parameters are sent to a terminal in the vehicle and applied in real time.

[0667] Citizen response measures

[0668] Users can access the citizen chat system using their smartphones or in-car devices and make inquiries. The server receives inquiries from users via the chat system, and generative artificial intelligence generates the optimal answer. As a specific example, in response to an inquiry such as "Please tell me the current congestion situation," real-time traffic data and predicted results are provided.

[0669] Improvement and Feedback Vehicles

[0670] The server monitors the results of implemented measures and evaluates their effectiveness, collects quantitative performance data to continuously improve the generative AI model, and continues to collect data over time to provide data for detecting new patterns and anomalies.

[0671] emotion recognition means

[0672] The emotion recognition means evaluates the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc. This emotional data is acquired when the user makes an inquiry. For example, when a user makes an inquiry such as "I'm going to be late for work because of traffic jams," the emotion recognition means senses impatience and stress from the user's voice tone and choice of words. Based on this information, the generative artificial intelligence on the server generates the optimal answer and creates an appropriate message to alleviate the user's anxiety.

[0673] Prompt Sentence Examples

[0674] "Based on current city traffic data, predict the traffic situation for the next hour and indicate the possibility of traffic congestion and accidents."

[0675] This system can significantly improve operational efficiency and driver happiness in autonomous vehicles.

[0676] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0677] Step 1:

[0678] The autonomous vehicle's terminal collects real-time traffic and environmental data from the vehicle's onboard cameras and sensors. The collected data is then transmitted by the terminal to a server via wireless communication. Input data at this stage include traffic volume, temperature, humidity, wind speed, etc. As an output, data packets are sent to the server.

[0679] Step 2:

[0680] The server receives the data sent from the devices and integrates the data from different sources. Specifically, pre-processing such as data shaping and filtering is performed to remove incomplete data and noise. The input at this stage is the raw data sent from the devices, and the output is shaped and clean data.

[0681] Step 3:

[0682] The server provides the preprocessed data to a generative AI, which analyzes and predicts traffic patterns. The prompt is "Analyze the current traffic patterns and predict them for the next hour." The input is preprocessed traffic data, and the generative AI outputs a traffic congestion prediction model.

[0683] Step 4:

[0684] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (traffic congestion, accidents, etc.). Real-time traffic data is used as input, and if an anomaly is detected, the type of anomaly and its location are output. The server issues a warning based on this.

[0685] Step 5:

[0686] When an anomaly is detected, the server uses generative artificial intelligence to propose optimal countermeasures. For example, it may suggest adjusting traffic light timings or recommending alternative routes. Simulations are performed to evaluate the effectiveness of the proposed countermeasures. The input at this stage is the anomaly detection information, and the output is the optimal countermeasure.

[0687] Step 6:

[0688] The server sends the optimization parameters to the terminal in the vehicle, which then applies the proposed measures in real time. The terminal updates the in-vehicle system based on the received optimization parameters and performs traffic light and route adjustments. The optimization parameters are the input, and the results of the adjustments are the output.

[0689] Step 7:

[0690] Users access the citizen chat system using their smartphones or in-car devices and make inquiries. The server receives the user's inquiry via the chat system, and the generative AI generates the optimal answer. The input is the user's inquiry information, and the output is the answer provided by the generative AI.

[0691] Step 8:

[0692] The server monitors the results of the implemented measures and evaluates their effectiveness. It continues to collect data over the long term and provides data to detect new patterns and anomalies. The input is data on the effectiveness of the implemented measures, and the output is information that will be useful for further improvements and feedback.

[0693] Step 9:

[0694] The emotion recognition means evaluates the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc. When the user makes a query, this emotional data is sent to the server. The input is the user's emotional data, and the output is an optimal answer that matches the user's emotional state.

[0695] In this way, each processing step involves data collection, integration, analysis, anomaly detection, proposals, execution, user support, improvement feedback, and emotion recognition, resulting in a system that can predict traffic conditions in real time, provide optimal routes, and respond appropriately to the driver's emotional state.

[0696] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0697] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0698] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0699] [Third embodiment]

[0700] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0701] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0702] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0703] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0704] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0705] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0706] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0707] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0708] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0709] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0710] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0711] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0712] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means.

[0713] Data collection methods

[0714] The terminals collect real-time data on vehicle flow, speed, congestion, etc. from traffic sensors (cameras and devices) installed at intersections and major traffic points within the city. The collected data is then sent to a server via the internet or a dedicated network.

[0715] Data integration and preprocessing measures

[0716] The server receives traffic data sent by the terminals and integrates the data collected from different sources, and in the process performs pre-processing such as data shaping and filtering to remove incomplete data and noise.

[0717] Data analysis and prediction methods using generative artificial intelligence

[0718] Once preprocessed, the data is analyzed by a generative AI running on the server. This allows for the analysis of current traffic patterns and the creation of a traffic congestion prediction model. The generative AI also simulates future traffic conditions and displays the results on a dashboard.

[0719] Anomaly detection means

[0720] The server monitors the flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and immediately issues an alert.

[0721] Problem solving proposals and implementation methods

[0722] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. For example, specific improvement plans such as adjusting traffic light timings or reconfiguring traffic routes are proposed. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures. Based on the evaluation results, the optimal countermeasures are implemented.

[0723] The terminal updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, for example, changing the traffic light control pattern.

[0724] Citizen response measures

[0725] If users have any problems or questions about the safety or traffic conditions in the city, they can use their smartphones or other devices to access the citizen chat system and make inquiries to the operator.

[0726] The server receives inquiries from users via the chat system and generates optimal answers using generative artificial intelligence. For example, in response to a query such as "What is the current traffic situation?", the server provides real-time traffic data and prediction results.

[0727] Improvement and Feedback Vehicles

[0728] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0729] The devices continue to collect data over time, providing a baseline for detecting new patterns and anomalies.

[0730] The comprehensive combination of these capabilities will enable accurate and rapid management of smart cities using generative artificial intelligence, improving the efficiency and happiness of citizens' lives.

[0731] The processing flow will be explained below.

[0732] Step 1:

[0733] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0734] Step 2:

[0735] The server receives the data sent from the terminal, and the received data is first preprocessed by a data integration and preprocessing means to integrate data from different sources and remove incomplete data and noise.

[0736] Step 3:

[0737] The server then supplies the preprocessed data to a generative AI, which uses the data to analyze traffic patterns and build a predictive model of traffic congestion. Based on the model, future traffic conditions are simulated and the results are displayed on a dashboard.

[0738] Step 4:

[0739] The server monitors the data in real time and uses generative artificial intelligence to detect anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[0740] Step 5:

[0741] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The proposed countermeasures (e.g., adjusting traffic signal times or reconfiguring traffic routes) are simulated to evaluate their effectiveness.

[0742] Step 6:

[0743] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0744] Step 7:

[0745] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0746] Step 8:

[0747] The server receives inquiries from users through the chat system, and the generative AI provides the best possible answer. For example, in response to a question like, "What is the current traffic situation?", it presents real-time traffic data and prediction results.

[0748] Step 9:

[0749] The server monitors the results of proposed and implemented measures and evaluates their effectiveness. Quantitative performance data is collected and used to continuously improve the generative artificial intelligence model.

[0750] Step 10:

[0751] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0752] This series of processing flows will enable smart city management using generative artificial intelligence to be realized more efficiently and effectively.

[0753] Example 1

[0754] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0755] Urban traffic management requires accurate data collection, prediction, anomaly detection, and countermeasure implementation in real time. Current systems often experience delays in data processing and analysis, making it difficult to respond quickly. They also lack the means to provide appropriate answers to citizen inquiries in real time. This results in reduced operational efficiency and hinders improvements in the efficiency and happiness of citizens' lives.

[0756] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0757] In this invention, the server includes: a data collection means that collects data such as vehicle flow rate, speed, and congestion level in real time using sensors installed at intersections and major traffic points within the city; a data integration and preprocessing means that receives the data collected by the data collection means, integrates data collected from different sources, and performs preprocessing such as data shaping and filtering; a data analysis and prediction means that uses generative artificial intelligence to analyze the data preprocessed by the data integration and preprocessing means, and analyzes traffic patterns and builds a traffic congestion prediction model; anomaly detection means that monitors the continuous flow of data in real time and detects unexpected abnormalities using generative artificial intelligence; problem solution proposal and implementation means that uses generative artificial intelligence to propose optimal measures for detected abnormalities, simulates the measures, evaluates their effectiveness, and implements them; citizen response means that allows users to access the citizen chat system via a smartphone or terminal, make inquiries to the operator, and the generative artificial intelligence generates optimal answers to the inquiries; and an improvement and feedback means that monitors the results of the implemented measures, evaluates their effectiveness, collects performance data, and continuously improves the generative artificial intelligence model. This will enable more efficient traffic management in cities, faster response to abnormalities, and real-time responses to inquiries from citizens.

[0758] "Data collection means" refers to the collection of real-time data on vehicle flow, speed, congestion, etc. using sensors installed at intersections and major traffic points within the city.

[0759] The "data integration and pre-processing means" is a means for receiving data collected by the data collection means, integrating data collected from different sources, and performing pre-processing such as shaping and filtering the data.

[0760] The "data analysis and prediction means" is a means for analyzing data preprocessed by the data integration and preprocessing means using generative artificial intelligence to analyze traffic patterns and construct a traffic congestion prediction model.

[0761] An "anomaly detection method" is a method that monitors data that is constantly flowing in real time and detects unexpected anomalies using generative artificial intelligence.

[0762] "Proposal and implementation of solutions to the problem" refers to a method in which generative artificial intelligence proposes optimal solutions to detected anomalies, simulates those solutions, evaluates their effectiveness, and then implements them.

[0763] The "citizen response means" is a means by which users access the citizen chat system via their smartphones or terminals, make inquiries to the operator, and have the generative artificial intelligence generate the optimal response to those inquiries.

[0764] "Improvement and feedback measures" are measures to monitor the results of implemented measures, evaluate their effectiveness, and collect performance data to continuously improve the generative artificial intelligence model.

[0765] This invention relates to a system for streamlining urban traffic management and improving the efficiency and happiness of civic life. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means.

[0766] Data collection methods

[0767] The devices use sensors installed at intersections and major traffic points within a city to collect real-time data on vehicle flow, speed, congestion, and other factors. For example, traffic sensors may be cameras or other devices that capture video every second and quantify the number and speed of vehicles. This data is then sent to a server via Wi-Fi or a dedicated line.

[0768] Data integration and preprocessing measures

[0769] The server receives the raw data sent from the devices and consolidates it. It then shapes and filters the data to remove missing data and noise. For example, it applies a noise filter to data collected overnight to fill in outliers and missing values. It also removes duplicate data sent from the same device.

[0770] Data analysis and prediction methods using generative artificial intelligence

[0771] The preprocessed data is then analyzed by a generative artificial intelligence (AI) running on a server. This AI model analyzes traffic patterns and builds a model to predict future traffic congestion. The AI ​​is trained using traffic data from the past few weeks, then simulates traffic flow for the next hour and displays the predicted results on a dashboard.

[0772] Prompt Sentence Examples

[0773] "Based on the data from the past hour, please display the predicted number of vehicles passing through intersection A in the next hour."

[0774] Anomaly detection means

[0775] The server monitors the continuous flow of data in real time and uses generative artificial intelligence to detect anomalies, such as a sudden increase in traffic volume that deviates from normal patterns, and immediately sends an alert to administrators.

[0776] Problem solving proposals and implementation methods

[0777] The server uses generative artificial intelligence to propose optimal countermeasures for the detected anomalies. Specific countermeasures are proposed, such as adjusting traffic light timings or reconfiguring traffic routes. Simulations are then performed to evaluate the effectiveness of these countermeasures and implement the optimal countermeasures. The terminal then updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server.

[0778] Prompt Sentence Examples

[0779] "Please display the current traffic congestion situation at intersection A in real time."

[0780] Citizen response measures

[0781] Users can access the chat system for citizens using their smartphones or other devices and make inquiries to the operator. For example, they can ask, "Please tell me the current congestion situation."

[0782] The server receives user inquiries via a chat system and generates optimal answers using generative artificial intelligence, providing real-time traffic data and prediction results.

[0783] Improvement and Feedback Vehicles

[0784] The server monitors the results of the implemented measures and evaluates their effectiveness, collecting quantitative performance data to continuously improve the generative AI model. The terminal continues to collect data over the long term, providing a foundation for detecting new patterns and anomalies.

[0785] These capabilities will enable accurate and swift management of smart cities using generative artificial intelligence, streamlining urban traffic management and improving the efficiency and happiness of citizens' lives.

[0786] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0787] Step 1: Data collection methods

[0788] The devices use sensors installed at intersections and major traffic points within the city to collect real-time data on vehicle flow, speed, congestion, and other factors. For example, cameras are used as traffic sensors. The cameras capture video every second and quantify the number and speed of vehicles from the video data. This data is then sent to a server via Wi-Fi or a dedicated line.

[0789] Input: Raw data (video data) from sensors such as cameras

[0790] Data processing: Analyzes video data and quantifies vehicle flow, speed, and congestion

[0791] Output: digitized traffic data

[0792] Step 2: Data integration and preprocessing measures

[0793] The server receives traffic data sent from the terminals, then integrates the data collected from different sources and performs pre-processing such as data shaping and filtering, eliminating missing data, noise, and redundant data.

[0794] Input: Traffic data collected in real time

[0795] Data processing: data shaping, filtering, missing value completion, noise removal, and duplicate data removal.

[0796] Output: Preprocessed and clean traffic data

[0797] Step 3: Data analysis and prediction methods using generative artificial intelligence

[0798] Generative AI running on the server analyzes the pre-processed data. The AI ​​model analyzes traffic patterns and builds a model to predict future traffic congestion. The prediction results are displayed on a dashboard in a visually understandable format for stakeholders.

[0799] Input: Preprocessed and clean traffic data

[0800] Data Computing: Generative AI to analyze traffic patterns and build predictive models

[0801] Output: Traffic congestion prediction results (dashboard display)

[0802] Step 4: Anomaly detection methods

[0803] The server monitors the flow of data in real time and uses generative artificial intelligence to detect anomalies, such as sudden increases in traffic volume or congestion that deviate from normal patterns, and immediately issues an alert.

[0804] Input: Real-time traffic data

[0805] Data calculation: Traffic pattern anomaly detection

[0806] Output: Warning notification of abnormality detection

[0807] Step 5: Proposal and implementation of solutions

[0808] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. Specific examples include adjusting traffic light timings and reconfiguring traffic routes. The proposals are evaluated through simulations, and the optimal countermeasures are implemented. The terminal updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server.

[0809] Input: Anomaly detection data, traffic prediction model

[0810] Data calculation: Generative AI proposes countermeasures and evaluates simulation results

[0811] Output: Optimal measures to be implemented (changes to traffic lights and equipment settings)

[0812] Step 6: Public Response Measures

[0813] Users access the chat system for citizens using their smartphones or other devices and send inquiries to the operator. The server receives inquiries from users via the chat system and generates optimal answers using generative artificial intelligence. For example, in response to an inquiry such as "What is the current congestion situation?", the server provides real-time traffic data and prediction results.

[0814] Input: User query

[0815] Data calculation: Real-time traffic data analysis, answer generation using generative AI

[0816] Output: Providing answers to users (traffic congestion status, etc.)

[0817] Step 7: Improvement and feedback measures

[0818] The server monitors the results of the implemented measures and evaluates their effectiveness. It collects quantitative performance data and continuously improves the generative AI model. The device continues to collect data over the long term, providing a foundation for detecting new patterns and anomalies.

[0819] Input: Traffic data and performance data after implementing measures

[0820] Data calculation: Evaluating the effectiveness of countermeasures and improving AI models

[0821] Output: improved AI models, data for new pattern detection

[0822] (Application example 1)

[0823] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0824] The goal is to provide a system that optimizes traffic conditions by streamlining the collection and analysis of traffic data within cities, quickly detecting traffic congestion and accidents, and immediately proposing and implementing appropriate countermeasures. Another challenge is to create a system that allows users to check traffic conditions in real time and receive optimal route suggestions.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0826] In this invention, the server includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a user response means, an improvement and feedback means, a real-time traffic data collection and analysis means, a means for proposing an optimal traffic route, and a user inquiry response means using generative artificial intelligence. This allows for real-time understanding of traffic conditions within a city, enabling efficient traffic management and optimal route proposals.

[0827] A "data collection means" is a device that collects data such as vehicle flow, speed, and congestion in real time from traffic sensors (cameras and devices) installed at intersections and major traffic points within a city, and transmits this data to a server via the Internet or a dedicated network.

[0828] "Data integration and pre-processing means" refers to the server's function of receiving traffic data sent from the terminal, integrating data collected from different sources, and performing pre-processing such as shaping and filtering the data to remove incomplete data and noise.

[0829] "Data analysis and prediction means using generative artificial intelligence" refers to a server function that uses pre-processed data to analyze current traffic patterns, build a predictive model of traffic congestion, and simulate future traffic conditions.

[0830] "Anomaly detection means" is a server function that monitors the flow of data in real time, uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents), and immediately issues an alert when an anomaly is detected.

[0831] "Proposal and implementation of problem solutions" refers to the functions of the server and terminal in which generative artificial intelligence proposes optimal solutions for abnormalities detected by the server, evaluates the effectiveness of the proposed solutions, and implements the optimal solutions based on the evaluation results.

[0832] The "user response means" is a function that allows users to access the citizen chat system using a smartphone or other device, make inquiries to the operator, and the server generates the most appropriate response using generative artificial intelligence.

[0833] "Improvement and feedback measures" refers to the function by which the server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0834] "Real-time traffic data collection and analysis means" refers to the function of the server and related devices to collect data on traffic conditions within the city in real time and analyze that data.

[0835] The "means of proposing the optimal transportation route" is a function that generates and proposes the optimal route based on the user's current location information and destination information, using the transportation data collected and analyzed by the server.

[0836] "Means for responding to user inquiries using generative artificial intelligence" is a function that uses generative artificial intelligence to generate optimal answers to users' inquiries about traffic conditions and other matters, and responds in real time.

[0837] A system for realizing this invention will now be described. The main elements of the system are a server, a terminal, and a user, and these elements cooperate to collect, analyze, predict, and respond to traffic data in real time.

[0838] The server includes the following means:

[0839] 1. Data collection means: This involves collecting data such as vehicle flow, speed, and congestion in real time from traffic sensors (cameras and devices) installed at intersections and major traffic points within the city, and sending it to a server via the Internet or a dedicated network.

[0840] 2. Data integration and pre-processing means: The server receives the traffic data sent from the terminals, integrates the data collected from different sources, and performs pre-processing such as data shaping and filtering.

[0841] 3. Data analysis and prediction using generative artificial intelligence: Preprocessed data is used to analyze current traffic patterns and build predictive models of traffic congestion, simulating future traffic conditions.

[0842] 4. Anomaly detection: The server monitors the flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents), issuing an immediate alert when an anomaly is detected.

[0843] 5. Proposal and implementation of countermeasures: The generative artificial intelligence proposes optimal countermeasures for the abnormalities detected by the server, evaluates the effectiveness of the proposed countermeasures, and implements the optimal countermeasures.

[0844] 6. User response method: Users access the citizen chat system using their smartphones or devices and make inquiries to the operator, and the server generates the most appropriate response using generative artificial intelligence.

[0845] 7. Improvement and feedback measures: The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0846] 8. Real-time traffic data collection and analysis means: This has the functions of a server and related devices for collecting data on traffic conditions within the city in real time and analyzing that data.

[0847] 9. Means for suggesting optimal transportation routes: Based on the transportation data collected and analyzed by the server, the optimal route is generated and suggested based on the user's current location information and destination information.

[0848] 10. A means of responding to user inquiries using generative artificial intelligence: Generative artificial intelligence will generate optimal answers to user inquiries about traffic conditions and other matters and respond in real time.

[0849] The hardware used includes traffic sensors, cameras, devices, and smartphones, and the software used includes the Python programming language, generative AI model APIs (such as GPT-3), server-side programs (e.g., Django, Flask), and databases (e.g., PostgreSQL, MySQL).

[0850] As a concrete example, let's consider the case where a user makes a query such as "Please tell me the current congestion situation." The following is an example of a prompt for this query:

[0851] Prompt statement:

[0852] User inquiry: What is the current congestion situation?

[0853] A generative AI model would take this prompt and generate a response like this:

[0854] Currently, there is a traffic jam on the main roads of the city, especially in the western part of the city, and traffic is very congested. The best route is to avoid road X and take road Y.

[0855] In this way, the system created can analyze traffic data within a city and provide users with optimal traffic information in real time.

[0856] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0857] Step 1:

[0858] The terminal collects data such as vehicle flow, speed, and congestion from traffic sensors in the city in real time and sends it to a server. The input is the raw data obtained from the traffic sensors, and the output is sending this data to the server.

[0859] Step 2:

[0860] The server receives traffic data sent from the terminal. The input is the traffic data sent from the terminal, and the output is the data received by the server. This data is collected from different sources and requires pre-processing for integration.

[0861] Step 3:

[0862] The server integrates the received raw data and performs preprocessing such as data shaping and filtering. The input is the received raw data, and the output is the shaped data after preprocessing. Specific operations include completing incomplete data and removing noise.

[0863] Step 4:

[0864] The server uses the preprocessed data to perform data analysis and predictions using generative artificial intelligence. The input is the preprocessed data, and the output is an analysis of current traffic patterns and a traffic congestion prediction model. In this case, the server uses the generative artificial intelligence model to analyze trends and patterns in the data.

[0865] Step 5:

[0866] The server monitors the continuous flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents). The input is traffic data updated in real time, and the output is the anomaly detection results. Detected anomalies can immediately trigger an alert.

[0867] Step 6:

[0868] Based on the results of the anomaly detection by the server, the generative AI proposes optimal countermeasures. The input is the anomaly detection results, and the output is countermeasure proposals. Specific countermeasures include adjusting traffic light timings and reconfiguring traffic routes.

[0869] Step 7:

[0870] The server simulates the effectiveness of the proposed measures and implements the optimal measures based on the evaluation results. The input is the proposed measures, and the output is the optimization parameters to be implemented. These optimization parameters are sent to the terminal, and the settings of traffic lights and related equipment are updated.

[0871] Step 8:

[0872] Users access the citizen chat system using their smartphones or other devices and make inquiries about traffic conditions. The input is the user's inquiry, and the output is the optimal answer generated by generative AI. For example, in response to an inquiry such as "Please tell me the current congestion situation," the system responds with real-time traffic data and forecast results.

[0873] Step 9:

[0874] The server monitors the results of the implemented measures and evaluates their effectiveness. The input is performance data of the implemented measures, and the output is an evaluation of the measures' effectiveness and feedback. This provides data for continuously improving the generative AI model.

[0875] Step 10:

[0876] The server continues to collect data over time, providing a basis for detecting new patterns and anomalies. The input is continuously collected traffic data, and the output is an improved model, which increases the accuracy and effectiveness of the system.

[0877] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0878] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a citizen response means, an improvement and feedback means, and also an emotion engine that recognizes user emotions.

[0879] Data collection methods

[0880] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0881] Data integration and preprocessing measures

[0882] The server receives the data sent by the devices and integrates the data from different sources, performing pre-processing such as data shaping and filtering to remove incomplete data and noise.

[0883] Data analysis and prediction methods using generative artificial intelligence

[0884] Once preprocessed, the data is analyzed by a generative AI running on the server. This allows for the analysis of current traffic patterns and the creation of a traffic congestion prediction model. The generative AI also simulates future traffic conditions and displays the results on a dashboard.

[0885] Anomaly detection means

[0886] The server monitors the data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and immediately issues an alert if an anomaly is detected.

[0887] Problem solving proposals and implementation methods

[0888] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. For example, specific improvement plans such as adjusting traffic light timings or reconfiguring traffic routes are proposed. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures. Based on the evaluation results, the optimal countermeasures are implemented.

[0889] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0890] Citizen response measures

[0891] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0892] The server receives inquiries from users via the chat system, and the generative AI generates the optimal answer. For example, in response to a query such as "What is the current traffic situation?", it provides real-time traffic data and prediction results.

[0893] Improvement and Feedback Vehicles

[0894] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0895] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0896] User response using an emotion engine

[0897] The emotion engine recognizes the user's emotions in real time. This emotion data is captured when the user makes a query. The emotion engine assesses the user's emotional state using voice tone, character usage patterns, facial expression recognition, etc.

[0898] The server optimizes citizen response methods based on the emotional data provided by the emotion engine. For example, if a user is feeling stressed, the generative AI will generate a more flexible and reassuring response.

[0899] For example, when a user inquires about being late for work due to traffic jams on their smartphone, the emotion engine can sense the user's impatience from their tone of voice and the way they speak. Based on this information, the server uses generative artificial intelligence to create an appropriate message that offers the best alternative route and time, and alleviates the user's anxiety.

[0900] This system will significantly improve the efficiency of city management and the happiness of its citizens. This series of system processes will enable smart cities to respond more flexibly in real time, helping to realize sustainable urban life.

[0901] The processing flow will be explained below.

[0902] Step 1:

[0903] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[0904] Step 2:

[0905] The server receives the data sent from the terminal, and the data integration and pre-processing means integrates the data from different sources and pre-processes the data to remove incomplete data and noise.

[0906] Step 3:

[0907] The server then supplies the preprocessed data to a generative AI, which uses the data to analyze traffic patterns, build a predictive model for traffic congestion, and simulate future traffic conditions, displaying the results on a dashboard.

[0908] Step 4:

[0909] The server monitors the data in real time and uses generative artificial intelligence to detect anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[0910] Step 5:

[0911] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The proposed countermeasures (e.g., adjusting traffic signal times or reconfiguring traffic routes) are simulated to evaluate their effectiveness.

[0912] Step 6:

[0913] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[0914] Step 7:

[0915] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[0916] Step 8:

[0917] The emotion engine recognizes the user's emotional state from their voice tone, character usage patterns, facial expressions, etc. when they make a query. The recognized emotion data is sent to the server.

[0918] Step 9:

[0919] The server uses generative artificial intelligence to generate optimal responses based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, it generates a flexible and reassuring response.

[0920] Step 10:

[0921] The server then provides the generated answers to the user through a chat system. For example, in response to a question such as "What is the current traffic congestion situation?", the server provides real-time traffic data and forecast results, thereby easing the user's anxiety.

[0922] Step 11:

[0923] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[0924] Step 12:

[0925] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[0926] This series of processing flows will enable smart city management utilizing generative artificial intelligence and emotion engines to be realized more efficiently and effectively.

[0927] Example 2

[0928] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0929] There is a need to effectively collect, process, and analyze various data within cities to improve the quality of life and happiness of citizens. However, current systems require time-consuming data integration and preprocessing, making it difficult to quickly detect anomalies or propose optimal countermeasures. In addition, there is a lack of consideration for user emotions, making it difficult to improve user satisfaction.

[0930] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0931] In this invention, the server includes means for collecting data, means for integrating and preprocessing the collected data, means for analyzing and predicting data using generative artificial intelligence, means for detecting anomalies in real time, means for proposing and implementing countermeasures based on the results of the anomaly detection, means for responding to inquiries from users, means for providing feedback on the effectiveness of the countermeasures and improving them, and means for recognizing user emotions. This enables efficient and flexible data management and citizen support within the city, thereby improving the happiness and quality of life of citizens.

[0932] "Means for collecting data" refers to devices and systems for collecting various data from devices such as sensors and cameras installed within the city.

[0933] "Means for integrating and pre-processing collected data" refers to devices and systems that receive data sent from terminals, consolidate data from different sources, and perform shaping and filtering to remove incomplete data and noise.

[0934] "Means for analyzing and predicting data using generative artificial intelligence" refers to devices and systems that use generative artificial intelligence to analyze data and predict future situations based on collected and preprocessed data.

[0935] "Means for detecting anomalies in real time" refers to devices and systems that use generative artificial intelligence to monitor data sequentially and immediately detect unexpected anomalies.

[0936] The "means for proposing and implementing solutions to problems based on anomaly detection results" refers to devices and systems that use generative artificial intelligence to propose optimal solutions when an anomaly is detected and then implement those proposals.

[0937] "Means for responding to inquiries from users" refers to devices and systems that enable generative artificial intelligence to provide appropriate answers to inquiries made by users via their terminals.

[0938] "Means for feedback and improvement of the effectiveness of measures" refers to devices and systems for monitoring the results of implemented measures, evaluating their effectiveness, and continuously improving the system.

[0939] The "means for recognizing user emotions" refers to a device or system for evaluating and recognizing the emotional state of a user when making a query through voice tone, character patterns, facial expression recognition, etc.

[0940] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system is an apparatus and system that includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a citizen response means, an improvement and feedback means, and an emotion engine that recognizes user emotions.

[0941] Data collection methods

[0942] The terminals collect real-time traffic and environmental data from sensors, cameras, and other devices installed throughout the city. For example, cameras monitor traffic volume and sensors measure air quality. The collected data is sent to a server via the internet or a dedicated network. Cameras capture images every second, and sensors acquire data every minute.

[0943] Data integration and preprocessing measures

[0944] The server receives data sent from the devices and integrates data from different sources. It centrally manages the data using a database system (e.g., MySQL, PostgreSQL) and performs preprocessing such as data shaping and filtering. Specifically, it matches data based on timestamps to remove incomplete data and noise. This prepares a reliable dataset for subsequent analysis.

[0945] Data analysis and prediction methods using generative artificial intelligence

[0946] The server uses the preprocessed data to analyze and predict the data using generative artificial intelligence (e.g., GPT-4). This allows it to analyze current traffic patterns and build a traffic congestion prediction model. The generative artificial intelligence also simulates future traffic conditions and displays the results on a dashboard. A specific example of its operation is outputting the predicted traffic volume for a specific time period next week.

[0947] Anomaly detection means

[0948] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (such as traffic accidents or the passing of emergency vehicles). If an anomaly is detected, a warning alert is sent immediately via email or SMS.

[0949] Problem solving proposals and implementation methods

[0950] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. Specific countermeasures include adjusting traffic light timing and providing detour route instructions. These proposals are based on the results of evaluations conducted through simulations. The device automatically updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server and implements the proposed countermeasures.

[0951] Citizen response measures

[0952] Users can use their smartphones or computer terminals to access the citizen chat system and ask questions or make inquiries. The server uses generative artificial intelligence to quickly provide appropriate answers to user inquiries. As a specific example of how it works, it generates a response based on real-time data in response to the question, "What is the current traffic congestion situation?"

[0953] Improvement and Feedback Vehicles

[0954] The server constantly monitors the results of the implemented measures, evaluates their effectiveness, and continuously improves the system. By collecting performance data and updating the artificial intelligence model, the accuracy and performance of the system are improved.

[0955] User response using an emotion engine

[0956] The emotion engine evaluates and recognizes emotions in real time when a user makes an inquiry through voice tone, text patterns, and facial expression recognition. The server uses this emotional data to provide a more emotionally appropriate response through generative AI. For example, if the server detects anxiety or impatience in a response such as "I'm going to be delayed due to traffic congestion," it will calmly offer advice on alternative routes and timetables.

[0957] The following are examples of prompt sentences:

[0958] "Please predict traffic conditions in the city and suggest the best route. The current time is 2:00 PM and the destination is City Hall."

[0959] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0960] Step 1: Data collection

[0961] The terminals collect traffic and environmental data from various sensors and cameras installed in the city. Specifically, cameras that count traffic volume and sensors that measure air quality are used. The input is real-time data from each sensor and camera. The output is the collected data sent to a server via a network.

[0962] Step 2: Send data

[0963] The terminal sends the collected data to a server via the Internet or a dedicated network. Specific examples of operation include sending data using the HTTP protocol or MQTT protocol. The input is the data collected on the terminal. The output is a series of data sent to the server.

[0964] Step 3: Data Integration

[0965] The server receives data sent from the devices and integrates data from different sources. As input, there is data received from the devices with different formats and timestamps. As output, there is an integrated and consistent data set. Specifically, the data is centrally managed using a database system (e.g., MySQL, PostgreSQL).

[0966] Step 4: Data Preprocessing

[0967] The server performs preprocessing on the integrated data. The input is the integrated data. The output is a preprocessed, clean dataset. Specifically, the server uses the Python pandas library to shape and filter the data, removing incomplete data and noise.

[0968] Step 5: Data analysis and prediction

[0969] The server uses the preprocessed data to analyze and make predictions using generative artificial intelligence (such as GPT-4). The input is the preprocessed, clean data. The output is a prediction of current and future traffic patterns and conditions. As a specific example of how it works, the predicted traffic volume for a specific time period next week is displayed on a dashboard.

[0970] Step 6: Anomaly detection

[0971] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies. The input is the real-time data stream. The output is detected anomalous events and warning alerts. If an anomaly is detected, relevant parties are immediately notified via email or SMS.

[0972] Step 7: Propose and implement solutions

[0973] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The input is the anomaly detection results. The output is the proposed countermeasures and the results of their implementation. Specific countermeasures include adjusting traffic light timing and setting up detour routes. The terminal updates the settings of traffic lights and related equipment based on the optimization parameters from the server.

[0974] Step 8: Public response

[0975] Users can access the citizen chat system using smartphones or computer terminals to ask questions or make inquiries. The input is the user's inquiry. The output is an answer generated by generative artificial intelligence. For example, a question such as "What is the current traffic congestion situation?" will be answered based on real-time data.

[0976] Step 9: Improve and Feedback

[0977] The server constantly monitors the results of the implemented measures, evaluates their effectiveness, and continuously improves the system. The input is a variety of performance data. The output is an updated AI model and improved system performance. Updating the AI ​​model based on the performance data improves the accuracy of the entire system.

[0978] Step 10: User interaction with emotion engine

[0979] The emotion engine evaluates and recognizes emotions in real time when a user makes an inquiry through voice tone, text patterns, facial expression recognition, etc. The input is the user's voice and text data. The output is a response that corresponds to the user's emotional state. The server takes in this emotional data and the generative artificial intelligence provides a response that is more appropriate to the emotion. As a specific example of how it works, if the server senses anxiety or impatience in response to an inquiry such as "I'm going to be late because of traffic congestion," it will provide advice on alternative routes and time in a calm tone.

[0980] (Application example 2)

[0981] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0982] With the recent advancement of urbanization, the use of self-driving vehicles has become more widespread, but problems such as traffic congestion and accidents remain unresolved. Furthermore, methods for reducing the stress and anxiety felt by users of self-driving vehicles have not yet been fully established. Therefore, there is a need for a system that can predict traffic conditions in real time, provide optimal routes, and respond appropriately to users' emotional states.

[0983] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0984] In this invention, the server includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, an improvement and feedback means, an emotion recognition means for recognizing the driver's emotion, a traffic pattern analysis and traffic prediction means using generative artificial intelligence, and an anomaly detection and optimal route proposal means. This makes it possible to predict traffic conditions in real time, provide optimal routes, and take appropriate measures according to the user's emotional state.

[0985] "Data collection means" refers to the means of collecting traffic data, environmental data, etc. in real time from devices such as sensors and cameras within the city.

[0986] "Data integration and pre-processing means" means for receiving data transmitted from the terminals, integrating data from different sources, and shaping and filtering the data to remove incomplete data and noise.

[0987] "Data analysis and prediction means using generative artificial intelligence" refers to a means of analyzing pre-processed data using generative artificial intelligence to analyze current traffic patterns and construct a traffic congestion prediction model.

[0988] An "anomaly detection method" is a method that monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents).

[0989] "Proposal and implementation of solutions to the problem" refers to a method in which the generative artificial intelligence proposes optimal solutions to detected anomalies, then conducts simulations to evaluate the effectiveness of the proposed solutions, and then implements the optimal solutions.

[0990] "Citizen response means" refers to the means by which users can access the citizen chat system using their smartphones or terminals and make inquiries when they have problems or questions.

[0991] "Improvement and feedback measures" are measures to monitor the results of the measures implemented, evaluate their effectiveness, and continuously improve the generative artificial intelligence model.

[0992] The "emotion recognition means for recognizing the driver's emotions" is a means for evaluating the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc., and optimizing the response based on the emotional data.

[0993] "Means for analyzing traffic patterns and forecasting traffic using generative artificial intelligence" refers to means for analyzing current traffic patterns using generative artificial intelligence, simulating future traffic conditions, and displaying the results on a dashboard.

[0994] The "means for detecting anomalies and proposing optimal routes" is a means for detecting unexpected anomalies using generative artificial intelligence and proposing alternative routes, times, etc. as optimal countermeasures.

[0995] The present invention relates to an urban traffic information system for autonomous vehicles, which is a system that integrates and provides functions such as data collection, data integration, prediction, anomaly detection, countermeasure proposal, user response, emotion recognition, etc. This system is implemented as follows.

[0996] Data collection methods

[0997] Devices (e.g., cameras and sensors) installed in autonomous vehicles collect traffic and environmental data within the city in real time. The collected data is sent to a server using wireless communication. This process uses the Internet or a dedicated network.

[0998] Data integration and preprocessing measures

[0999] The server receives the data sent from the devices and processes it to integrate data from different sources, during which pre-processing such as data shaping and filtering is performed to remove incomplete data and noise.

[1000] Data analysis and prediction methods using generative artificial intelligence

[1001] Once preprocessing is complete, the data is analyzed by a generative AI running on the server. The generative AI analyzes traffic patterns and builds a predictive model for traffic congestion. The generative AI also simulates future traffic conditions and displays the results on a dashboard. For example, a model such as GPT-4 is used as the generative AI model.

[1002] Anomaly detection means

[1003] The server monitors the data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[1004] Problem solving proposals and implementation methods

[1005] If an anomaly is detected, the generative AI proposes optimal countermeasures, such as adjusting traffic light timings or reconfiguring traffic routes. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures and the optimal countermeasures are implemented. The optimization parameters are sent to a terminal in the vehicle and applied in real time.

[1006] Citizen response measures

[1007] Users can access the citizen chat system using their smartphones or in-car devices and make inquiries. The server receives inquiries from users via the chat system, and generative artificial intelligence generates the optimal answer. As a specific example, in response to an inquiry such as "Please tell me the current congestion situation," real-time traffic data and predicted results are provided.

[1008] Improvement and Feedback Vehicles

[1009] The server monitors the results of implemented measures and evaluates their effectiveness, collects quantitative performance data to continuously improve the generative AI model, and continues to collect data over time to provide data for detecting new patterns and anomalies.

[1010] emotion recognition means

[1011] The emotion recognition means evaluates the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc. This emotional data is acquired when the user makes an inquiry. For example, when a user makes an inquiry such as "I'm going to be late for work because of traffic jams," the emotion recognition means senses impatience and stress from the user's voice tone and choice of words. Based on this information, the generative artificial intelligence on the server generates the optimal answer and creates an appropriate message to alleviate the user's anxiety.

[1012] Prompt Sentence Examples

[1013] "Based on current city traffic data, predict the traffic situation for the next hour and indicate the possibility of traffic congestion and accidents."

[1014] This system can significantly improve operational efficiency and driver happiness in autonomous vehicles.

[1015] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1016] Step 1:

[1017] The autonomous vehicle's terminal collects real-time traffic and environmental data from the vehicle's onboard cameras and sensors. The collected data is then transmitted by the terminal to a server via wireless communication. Input data at this stage include traffic volume, temperature, humidity, wind speed, etc. As an output, data packets are sent to the server.

[1018] Step 2:

[1019] The server receives the data sent from the devices and integrates the data from different sources. Specifically, pre-processing such as data shaping and filtering is performed to remove incomplete data and noise. The input at this stage is the raw data sent from the devices, and the output is shaped and clean data.

[1020] Step 3:

[1021] The server provides the preprocessed data to a generative AI, which analyzes and predicts traffic patterns. The prompt is "Analyze the current traffic patterns and predict them for the next hour." The input is preprocessed traffic data, and the generative AI outputs a traffic congestion prediction model.

[1022] Step 4:

[1023] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (traffic congestion, accidents, etc.). Real-time traffic data is used as input, and if an anomaly is detected, the type of anomaly and its location are output. The server issues a warning based on this.

[1024] Step 5:

[1025] When an anomaly is detected, the server uses generative artificial intelligence to propose optimal countermeasures. For example, it may suggest adjusting traffic light timings or recommending alternative routes. Simulations are performed to evaluate the effectiveness of the proposed countermeasures. The input at this stage is the anomaly detection information, and the output is the optimal countermeasure.

[1026] Step 6:

[1027] The server sends the optimization parameters to the terminal in the vehicle, which then applies the proposed measures in real time. The terminal updates the in-vehicle system based on the received optimization parameters and performs traffic light and route adjustments. The optimization parameters are the input, and the results of the adjustments are the output.

[1028] Step 7:

[1029] Users access the citizen chat system using their smartphones or in-car devices and make inquiries. The server receives the user's inquiry via the chat system, and the generative AI generates the optimal answer. The input is the user's inquiry information, and the output is the answer provided by the generative AI.

[1030] Step 8:

[1031] The server monitors the results of the implemented measures and evaluates their effectiveness. It continues to collect data over the long term and provides data to detect new patterns and anomalies. The input is data on the effectiveness of the implemented measures, and the output is information that will be useful for further improvements and feedback.

[1032] Step 9:

[1033] The emotion recognition means evaluates the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc. When the user makes a query, this emotional data is sent to the server. The input is the user's emotional data, and the output is an optimal answer that matches the user's emotional state.

[1034] In this way, each processing step involves data collection, integration, analysis, anomaly detection, proposals, execution, user support, improvement feedback, and emotion recognition, resulting in a system that can predict traffic conditions in real time, provide optimal routes, and respond appropriately to the driver's emotional state.

[1035] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1036] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1037] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1038] [Fourth embodiment]

[1039] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1040] 7, a 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.

[1041] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1042] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1043] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1044] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1045] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1046] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1047] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1048] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1049] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1050] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1051] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1052] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means.

[1053] Data collection methods

[1054] The terminals collect real-time data on vehicle flow, speed, congestion, etc. from traffic sensors (cameras and devices) installed at intersections and major traffic points within the city. The collected data is then sent to a server via the internet or a dedicated network.

[1055] Data integration and preprocessing measures

[1056] The server receives traffic data sent by the terminals and integrates the data collected from different sources, and in the process performs pre-processing such as data shaping and filtering to remove incomplete data and noise.

[1057] Data analysis and prediction methods using generative artificial intelligence

[1058] Once preprocessed, the data is analyzed by a generative AI running on the server. This allows for the analysis of current traffic patterns and the creation of a traffic congestion prediction model. The generative AI also simulates future traffic conditions and displays the results on a dashboard.

[1059] Anomaly detection means

[1060] The server monitors the flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and immediately issues an alert.

[1061] Problem solving proposals and implementation methods

[1062] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. For example, specific improvement plans such as adjusting traffic light timings or reconfiguring traffic routes are proposed. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures. Based on the evaluation results, the optimal countermeasures are implemented.

[1063] The terminal updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, for example, changing the traffic light control pattern.

[1064] Citizen response measures

[1065] If users have any problems or questions about the safety or traffic conditions in the city, they can use their smartphones or other devices to access the citizen chat system and make inquiries to the operator.

[1066] The server receives inquiries from users via the chat system and generates optimal answers using generative artificial intelligence. For example, in response to a query such as "What is the current traffic situation?", the server provides real-time traffic data and prediction results.

[1067] Improvement and Feedback Vehicles

[1068] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[1069] The devices continue to collect data over time, providing a baseline for detecting new patterns and anomalies.

[1070] The comprehensive combination of these capabilities will enable accurate and rapid management of smart cities using generative artificial intelligence, improving the efficiency and happiness of citizens' lives.

[1071] The processing flow will be explained below.

[1072] Step 1:

[1073] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[1074] Step 2:

[1075] The server receives the data sent from the terminal, and the received data is first preprocessed by a data integration and preprocessing means to integrate data from different sources and remove incomplete data and noise.

[1076] Step 3:

[1077] The server then supplies the preprocessed data to a generative AI, which uses the data to analyze traffic patterns and build a predictive model of traffic congestion. Based on the model, future traffic conditions are simulated and the results are displayed on a dashboard.

[1078] Step 4:

[1079] The server monitors the data in real time and uses generative artificial intelligence to detect anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[1080] Step 5:

[1081] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The proposed countermeasures (e.g., adjusting traffic signal times or reconfiguring traffic routes) are simulated to evaluate their effectiveness.

[1082] Step 6:

[1083] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[1084] Step 7:

[1085] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[1086] Step 8:

[1087] The server receives inquiries from users through the chat system, and the generative AI provides the best possible answer. For example, in response to a question like, "What is the current traffic situation?", it presents real-time traffic data and prediction results.

[1088] Step 9:

[1089] The server monitors the results of proposed and implemented measures and evaluates their effectiveness. Quantitative performance data is collected and used to continuously improve the generative artificial intelligence model.

[1090] Step 10:

[1091] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[1092] This series of processing flows will enable smart city management using generative artificial intelligence to be realized more efficiently and effectively.

[1093] Example 1

[1094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1095] Urban traffic management requires accurate data collection, prediction, anomaly detection, and countermeasure implementation in real time. Current systems often experience delays in data processing and analysis, making it difficult to respond quickly. They also lack the means to provide appropriate answers to citizen inquiries in real time. This results in reduced operational efficiency and hinders improvements in the efficiency and happiness of citizens' lives.

[1096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1097] In this invention, the server includes: a data collection means that collects data such as vehicle flow rate, speed, and congestion level in real time using sensors installed at intersections and major traffic points within the city; a data integration and preprocessing means that receives the data collected by the data collection means, integrates data collected from different sources, and performs preprocessing such as data shaping and filtering; a data analysis and prediction means that uses generative artificial intelligence to analyze the data preprocessed by the data integration and preprocessing means, and analyzes traffic patterns and builds a traffic congestion prediction model; anomaly detection means that monitors the continuous flow of data in real time and detects unexpected abnormalities using generative artificial intelligence; problem solution proposal and implementation means that uses generative artificial intelligence to propose optimal measures for detected abnormalities, simulates the measures, evaluates their effectiveness, and implements them; citizen response means that allows users to access the citizen chat system via a smartphone or terminal, make inquiries to the operator, and the generative artificial intelligence generates optimal answers to the inquiries; and an improvement and feedback means that monitors the results of the implemented measures, evaluates their effectiveness, collects performance data, and continuously improves the generative artificial intelligence model. This will enable more efficient traffic management in cities, faster response to abnormalities, and real-time responses to inquiries from citizens.

[1098] "Data collection means" refers to the collection of real-time data on vehicle flow, speed, congestion, etc. using sensors installed at intersections and major traffic points within the city.

[1099] The "data integration and pre-processing means" is a means for receiving data collected by the data collection means, integrating data collected from different sources, and performing pre-processing such as shaping and filtering the data.

[1100] The "data analysis and prediction means" is a means for analyzing data preprocessed by the data integration and preprocessing means using generative artificial intelligence to analyze traffic patterns and construct a traffic congestion prediction model.

[1101] An "anomaly detection method" is a method that monitors data that is constantly flowing in real time and detects unexpected anomalies using generative artificial intelligence.

[1102] "Proposal and implementation of solutions to the problem" refers to a method in which generative artificial intelligence proposes optimal solutions to detected anomalies, simulates those solutions, evaluates their effectiveness, and then implements them.

[1103] The "citizen response means" is a means by which users access the citizen chat system via their smartphones or terminals, make inquiries to the operator, and have the generative artificial intelligence generate the optimal response to those inquiries.

[1104] "Improvement and feedback measures" are measures to monitor the results of implemented measures, evaluate their effectiveness, and collect performance data to continuously improve the generative artificial intelligence model.

[1105] This invention relates to a system for streamlining urban traffic management and improving the efficiency and happiness of civic life. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, and an improvement and feedback means.

[1106] Data collection methods

[1107] The devices use sensors installed at intersections and major traffic points within a city to collect real-time data on vehicle flow, speed, congestion, and other factors. For example, traffic sensors may be cameras or other devices that capture video every second and quantify the number and speed of vehicles. This data is then sent to a server via Wi-Fi or a dedicated line.

[1108] Data integration and preprocessing measures

[1109] The server receives the raw data sent from the devices and consolidates it. It then shapes and filters the data to remove missing data and noise. For example, it applies a noise filter to data collected overnight to fill in outliers and missing values. It also removes duplicate data sent from the same device.

[1110] Data analysis and prediction methods using generative artificial intelligence

[1111] The preprocessed data is then analyzed by a generative artificial intelligence (AI) running on a server. This AI model analyzes traffic patterns and builds a model to predict future traffic congestion. The AI ​​is trained using traffic data from the past few weeks, then simulates traffic flow for the next hour and displays the predicted results on a dashboard.

[1112] Prompt Sentence Examples

[1113] "Based on the data from the past hour, please display the predicted number of vehicles passing through intersection A in the next hour."

[1114] Anomaly detection means

[1115] The server monitors the continuous flow of data in real time and uses generative artificial intelligence to detect anomalies, such as a sudden increase in traffic volume that deviates from normal patterns, and immediately sends an alert to administrators.

[1116] Problem solving proposals and implementation methods

[1117] The server uses generative artificial intelligence to propose optimal countermeasures for the detected anomalies. Specific countermeasures are proposed, such as adjusting traffic light timings or reconfiguring traffic routes. Simulations are then performed to evaluate the effectiveness of these countermeasures and implement the optimal countermeasures. The terminal then updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server.

[1118] Prompt Sentence Examples

[1119] "Please display the current traffic congestion situation at intersection A in real time."

[1120] Citizen response measures

[1121] Users can access the chat system for citizens using their smartphones or other devices and make inquiries to the operator. For example, they can ask, "Please tell me the current congestion situation."

[1122] The server receives user inquiries via a chat system and generates optimal answers using generative artificial intelligence, providing real-time traffic data and prediction results.

[1123] Improvement and Feedback Vehicles

[1124] The server monitors the results of the implemented measures and evaluates their effectiveness, collecting quantitative performance data to continuously improve the generative AI model. The terminal continues to collect data over the long term, providing a foundation for detecting new patterns and anomalies.

[1125] These capabilities will enable accurate and swift management of smart cities using generative artificial intelligence, streamlining urban traffic management and improving the efficiency and happiness of citizens' lives.

[1126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1127] Step 1: Data collection methods

[1128] The devices use sensors installed at intersections and major traffic points within the city to collect real-time data on vehicle flow, speed, congestion, and other factors. For example, cameras are used as traffic sensors. The cameras capture video every second and quantify the number and speed of vehicles from the video data. This data is then sent to a server via Wi-Fi or a dedicated line.

[1129] Input: Raw data (video data) from sensors such as cameras

[1130] Data processing: Analyzes video data and quantifies vehicle flow, speed, and congestion

[1131] Output: digitized traffic data

[1132] Step 2: Data integration and preprocessing measures

[1133] The server receives traffic data sent from the terminals, then integrates the data collected from different sources and performs pre-processing such as data shaping and filtering, eliminating missing data, noise, and redundant data.

[1134] Input: Traffic data collected in real time

[1135] Data processing: data shaping, filtering, missing value completion, noise removal, and duplicate data removal.

[1136] Output: Preprocessed and clean traffic data

[1137] Step 3: Data analysis and prediction methods using generative artificial intelligence

[1138] Generative AI running on the server analyzes the pre-processed data. The AI ​​model analyzes traffic patterns and builds a model to predict future traffic congestion. The prediction results are displayed on a dashboard in a visually understandable format for stakeholders.

[1139] Input: Preprocessed and clean traffic data

[1140] Data Computing: Generative AI to analyze traffic patterns and build predictive models

[1141] Output: Traffic congestion prediction results (dashboard display)

[1142] Step 4: Anomaly detection methods

[1143] The server monitors the flow of data in real time and uses generative artificial intelligence to detect anomalies, such as sudden increases in traffic volume or congestion that deviate from normal patterns, and immediately issues an alert.

[1144] Input: Real-time traffic data

[1145] Data calculation: Traffic pattern anomaly detection

[1146] Output: Warning notification of abnormality detection

[1147] Step 5: Proposal and implementation of solutions

[1148] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. Specific examples include adjusting traffic light timings and reconfiguring traffic routes. The proposals are evaluated through simulations, and the optimal countermeasures are implemented. The terminal updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server.

[1149] Input: Anomaly detection data, traffic prediction model

[1150] Data calculation: Generative AI proposes countermeasures and evaluates simulation results

[1151] Output: Optimal measures to be implemented (changes to traffic lights and equipment settings)

[1152] Step 6: Public Response Measures

[1153] Users access the chat system for citizens using their smartphones or other devices and send inquiries to the operator. The server receives inquiries from users via the chat system and generates optimal answers using generative artificial intelligence. For example, in response to an inquiry such as "What is the current congestion situation?", the server provides real-time traffic data and prediction results.

[1154] Input: User query

[1155] Data calculation: Real-time traffic data analysis, answer generation using generative AI

[1156] Output: Providing answers to users (traffic congestion status, etc.)

[1157] Step 7: Improvement and feedback measures

[1158] The server monitors the results of the implemented measures and evaluates their effectiveness. It collects quantitative performance data and continuously improves the generative AI model. The device continues to collect data over the long term, providing a foundation for detecting new patterns and anomalies.

[1159] Input: Traffic data and performance data after implementing measures

[1160] Data calculation: Evaluating the effectiveness of countermeasures and improving AI models

[1161] Output: improved AI models, data for new pattern detection

[1162] (Application example 1)

[1163] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1164] The goal is to provide a system that optimizes traffic conditions by streamlining the collection and analysis of traffic data within cities, quickly detecting traffic congestion and accidents, and immediately proposing and implementing appropriate countermeasures. Another challenge is to create a system that allows users to check traffic conditions in real time and receive optimal route suggestions.

[1165] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1166] In this invention, the server includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a user response means, an improvement and feedback means, a real-time traffic data collection and analysis means, a means for proposing an optimal traffic route, and a user inquiry response means using generative artificial intelligence. This allows for real-time understanding of traffic conditions within a city, enabling efficient traffic management and optimal route proposals.

[1167] A "data collection means" is a device that collects data such as vehicle flow, speed, and congestion in real time from traffic sensors (cameras and devices) installed at intersections and major traffic points within a city, and transmits this data to a server via the Internet or a dedicated network.

[1168] "Data integration and pre-processing means" refers to the server's function of receiving traffic data sent from the terminal, integrating data collected from different sources, and performing pre-processing such as shaping and filtering the data to remove incomplete data and noise.

[1169] "Data analysis and prediction means using generative artificial intelligence" refers to a server function that uses pre-processed data to analyze current traffic patterns, build a predictive model of traffic congestion, and simulate future traffic conditions.

[1170] "Anomaly detection means" is a server function that monitors the flow of data in real time, uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents), and immediately issues an alert when an anomaly is detected.

[1171] "Proposal and implementation of problem solutions" refers to the functions of the server and terminal in which generative artificial intelligence proposes optimal solutions for abnormalities detected by the server, evaluates the effectiveness of the proposed solutions, and implements the optimal solutions based on the evaluation results.

[1172] The "user response means" is a function that allows users to access the citizen chat system using a smartphone or other device, make inquiries to the operator, and the server generates the most appropriate response using generative artificial intelligence.

[1173] "Improvement and feedback measures" refers to the function by which the server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[1174] "Real-time traffic data collection and analysis means" refers to the function of the server and related devices to collect data on traffic conditions within the city in real time and analyze that data.

[1175] The "means of proposing the optimal transportation route" is a function that generates and proposes the optimal route based on the user's current location information and destination information, using the transportation data collected and analyzed by the server.

[1176] "Means for responding to user inquiries using generative artificial intelligence" is a function that uses generative artificial intelligence to generate optimal answers to users' inquiries about traffic conditions and other matters, and responds in real time.

[1177] A system for realizing this invention will now be described. The main elements of the system are a server, a terminal, and a user, and these elements cooperate to collect, analyze, predict, and respond to traffic data in real time.

[1178] The server includes the following means:

[1179] 1. Data collection means: This involves collecting data such as vehicle flow, speed, and congestion in real time from traffic sensors (cameras and devices) installed at intersections and major traffic points within the city, and sending it to a server via the Internet or a dedicated network.

[1180] 2. Data integration and pre-processing means: The server receives the traffic data sent from the terminals, integrates the data collected from different sources, and performs pre-processing such as data shaping and filtering.

[1181] 3. Data analysis and prediction using generative artificial intelligence: Preprocessed data is used to analyze current traffic patterns and build predictive models of traffic congestion, simulating future traffic conditions.

[1182] 4. Anomaly detection: The server monitors the flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents), issuing an immediate alert when an anomaly is detected.

[1183] 5. Proposal and implementation of countermeasures: The generative artificial intelligence proposes optimal countermeasures for the abnormalities detected by the server, evaluates the effectiveness of the proposed countermeasures, and implements the optimal countermeasures.

[1184] 6. User response method: Users access the citizen chat system using their smartphones or devices and make inquiries to the operator, and the server generates the most appropriate response using generative artificial intelligence.

[1185] 7. Improvement and feedback measures: The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[1186] 8. Real-time traffic data collection and analysis means: This has the functions of a server and related devices for collecting data on traffic conditions within the city in real time and analyzing that data.

[1187] 9. Means for suggesting optimal transportation routes: Based on the transportation data collected and analyzed by the server, the optimal route is generated and suggested based on the user's current location information and destination information.

[1188] 10. A means of responding to user inquiries using generative artificial intelligence: Generative artificial intelligence will generate optimal answers to user inquiries about traffic conditions and other matters and respond in real time.

[1189] The hardware used includes traffic sensors, cameras, devices, and smartphones, and the software used includes the Python programming language, generative AI model APIs (such as GPT-3), server-side programs (e.g., Django, Flask), and databases (e.g., PostgreSQL, MySQL).

[1190] As a concrete example, let's consider the case where a user makes a query such as "Please tell me the current congestion situation." The following is an example of a prompt for this query:

[1191] Prompt statement:

[1192] User inquiry: What is the current congestion situation?

[1193] A generative AI model would take this prompt and generate a response like this:

[1194] Currently, there is a traffic jam on the main roads of the city, especially in the western part of the city, and traffic is very congested. The best route is to avoid road X and take road Y.

[1195] In this way, the system created can analyze traffic data within a city and provide users with optimal traffic information in real time.

[1196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1197] Step 1:

[1198] The terminal collects data such as vehicle flow, speed, and congestion from traffic sensors in the city in real time and sends it to a server. The input is the raw data obtained from the traffic sensors, and the output is sending this data to the server.

[1199] Step 2:

[1200] The server receives traffic data sent from the terminal. The input is the traffic data sent from the terminal, and the output is the data received by the server. This data is collected from different sources and requires pre-processing for integration.

[1201] Step 3:

[1202] The server integrates the received raw data and performs preprocessing such as data shaping and filtering. The input is the received raw data, and the output is the shaped data after preprocessing. Specific operations include completing incomplete data and removing noise.

[1203] Step 4:

[1204] The server uses the preprocessed data to perform data analysis and predictions using generative artificial intelligence. The input is the preprocessed data, and the output is an analysis of current traffic patterns and a traffic congestion prediction model. In this case, the server uses the generative artificial intelligence model to analyze trends and patterns in the data.

[1205] Step 5:

[1206] The server monitors the continuous flow of data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents). The input is traffic data updated in real time, and the output is the anomaly detection results. Detected anomalies can immediately trigger an alert.

[1207] Step 6:

[1208] Based on the results of the anomaly detection by the server, the generative AI proposes optimal countermeasures. The input is the anomaly detection results, and the output is countermeasure proposals. Specific countermeasures include adjusting traffic light timings and reconfiguring traffic routes.

[1209] Step 7:

[1210] The server simulates the effectiveness of the proposed measures and implements the optimal measures based on the evaluation results. The input is the proposed measures, and the output is the optimization parameters to be implemented. These optimization parameters are sent to the terminal, and the settings of traffic lights and related equipment are updated.

[1211] Step 8:

[1212] Users access the citizen chat system using their smartphones or other devices and make inquiries about traffic conditions. The input is the user's inquiry, and the output is the optimal answer generated by generative AI. For example, in response to an inquiry such as "Please tell me the current congestion situation," the system responds with real-time traffic data and forecast results.

[1213] Step 9:

[1214] The server monitors the results of the implemented measures and evaluates their effectiveness. The input is performance data of the implemented measures, and the output is an evaluation of the measures' effectiveness and feedback. This provides data for continuously improving the generative AI model.

[1215] Step 10:

[1216] The server continues to collect data over time, providing a basis for detecting new patterns and anomalies. The input is continuously collected traffic data, and the output is an improved model, which increases the accuracy and effectiveness of the system.

[1217] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1218] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a citizen response means, an improvement and feedback means, and also an emotion engine that recognizes user emotions.

[1219] Data collection methods

[1220] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[1221] Data integration and preprocessing measures

[1222] The server receives the data sent by the devices and integrates the data from different sources, performing pre-processing such as data shaping and filtering to remove incomplete data and noise.

[1223] Data analysis and prediction methods using generative artificial intelligence

[1224] Once preprocessed, the data is analyzed by a generative AI running on the server. This allows for the analysis of current traffic patterns and the creation of a traffic congestion prediction model. The generative AI also simulates future traffic conditions and displays the results on a dashboard.

[1225] Anomaly detection means

[1226] The server monitors the data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and immediately issues an alert if an anomaly is detected.

[1227] Problem solving proposals and implementation methods

[1228] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. For example, specific improvement plans such as adjusting traffic light timings or reconfiguring traffic routes are proposed. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures. Based on the evaluation results, the optimal countermeasures are implemented.

[1229] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[1230] Citizen response measures

[1231] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[1232] The server receives inquiries from users via the chat system, and the generative AI generates the optimal answer. For example, in response to a query such as "What is the current traffic situation?", it provides real-time traffic data and prediction results.

[1233] Improvement and Feedback Vehicles

[1234] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[1235] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[1236] User response using an emotion engine

[1237] The emotion engine recognizes the user's emotions in real time. This emotion data is captured when the user makes a query. The emotion engine assesses the user's emotional state using voice tone, character usage patterns, facial expression recognition, etc.

[1238] The server optimizes citizen response methods based on the emotional data provided by the emotion engine. For example, if a user is feeling stressed, the generative AI will generate a more flexible and reassuring response.

[1239] For example, when a user inquires about being late for work due to traffic jams on their smartphone, the emotion engine can sense the user's impatience from their tone of voice and the way they speak. Based on this information, the server uses generative artificial intelligence to create an appropriate message that offers the best alternative route and time, and alleviates the user's anxiety.

[1240] This system will significantly improve the efficiency of city management and the happiness of its citizens. This series of system processes will enable smart cities to respond more flexibly in real time, helping to realize sustainable urban life.

[1241] The processing flow will be explained below.

[1242] Step 1:

[1243] The terminals collect traffic and environmental data in real time from sensors, cameras, and other devices within the city, and the collected data is sent to a server via the internet or a dedicated network.

[1244] Step 2:

[1245] The server receives the data sent from the terminal, and the data integration and pre-processing means integrates the data from different sources and pre-processes the data to remove incomplete data and noise.

[1246] Step 3:

[1247] The server then supplies the preprocessed data to a generative AI, which uses the data to analyze traffic patterns, build a predictive model for traffic congestion, and simulate future traffic conditions, displaying the results on a dashboard.

[1248] Step 4:

[1249] The server monitors the data in real time and uses generative artificial intelligence to detect anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[1250] Step 5:

[1251] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The proposed countermeasures (e.g., adjusting traffic signal times or reconfiguring traffic routes) are simulated to evaluate their effectiveness.

[1252] Step 6:

[1253] The device updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server, thereby implementing the proposed measures.

[1254] Step 7:

[1255] If users have any problems or questions, they can use their smartphones or devices to access the citizen chat system and make inquiries.

[1256] Step 8:

[1257] The emotion engine recognizes the user's emotional state from their voice tone, character usage patterns, facial expressions, etc. when they make a query. The recognized emotion data is sent to the server.

[1258] Step 9:

[1259] The server uses generative artificial intelligence to generate optimal responses based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, it generates a flexible and reassuring response.

[1260] Step 10:

[1261] The server then provides the generated answers to the user through a chat system. For example, in response to a question such as "What is the current traffic congestion situation?", the server provides real-time traffic data and forecast results, thereby easing the user's anxiety.

[1262] Step 11:

[1263] The server monitors the results of the implemented measures, evaluates their effectiveness, collects quantitative performance data, and continuously improves the generative artificial intelligence model.

[1264] Step 12:

[1265] The devices continue to collect data over time, providing data to detect new patterns and anomalies, which are then used for overall system improvement and feedback.

[1266] This series of processing flows will enable smart city management utilizing generative artificial intelligence and emotion engines to be realized more efficiently and effectively.

[1267] Example 2

[1268] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1269] There is a need to effectively collect, process, and analyze various data within cities to improve the quality of life and happiness of citizens. However, current systems require time-consuming data integration and preprocessing, making it difficult to quickly detect anomalies or propose optimal countermeasures. In addition, there is a lack of consideration for user emotions, making it difficult to improve user satisfaction.

[1270] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1271] In this invention, the server includes means for collecting data, means for integrating and preprocessing the collected data, means for analyzing and predicting data using generative artificial intelligence, means for detecting anomalies in real time, means for proposing and implementing countermeasures based on the results of the anomaly detection, means for responding to inquiries from users, means for providing feedback on the effectiveness of the countermeasures and improving them, and means for recognizing user emotions. This enables efficient and flexible data management and citizen support within the city, thereby improving the happiness and quality of life of citizens.

[1272] "Means for collecting data" refers to devices and systems for collecting various data from devices such as sensors and cameras installed within the city.

[1273] "Means for integrating and pre-processing collected data" refers to devices and systems that receive data sent from terminals, consolidate data from different sources, and perform shaping and filtering to remove incomplete data and noise.

[1274] "Means for analyzing and predicting data using generative artificial intelligence" refers to devices and systems that use generative artificial intelligence to analyze data and predict future situations based on collected and preprocessed data.

[1275] "Means for detecting anomalies in real time" refers to devices and systems that use generative artificial intelligence to monitor data sequentially and immediately detect unexpected anomalies.

[1276] The "means for proposing and implementing solutions to problems based on anomaly detection results" refers to devices and systems that use generative artificial intelligence to propose optimal solutions when an anomaly is detected and then implement those proposals.

[1277] "Means for responding to inquiries from users" refers to devices and systems that enable generative artificial intelligence to provide appropriate answers to inquiries made by users via their terminals.

[1278] "Means for feedback and improvement of the effectiveness of measures" refers to devices and systems for monitoring the results of implemented measures, evaluating their effectiveness, and continuously improving the system.

[1279] The "means for recognizing user emotions" refers to a device or system for evaluating and recognizing the emotional state of a user when making a query through voice tone, character patterns, facial expression recognition, etc.

[1280] The present invention relates to a system for collecting, processing, and analyzing various data within a city to improve the efficiency and happiness of citizens' lives. The system is an apparatus and system that includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem solution proposal and implementation means, a citizen response means, an improvement and feedback means, and an emotion engine that recognizes user emotions.

[1281] Data collection methods

[1282] The terminals collect real-time traffic and environmental data from sensors, cameras, and other devices installed throughout the city. For example, cameras monitor traffic volume and sensors measure air quality. The collected data is sent to a server via the internet or a dedicated network. Cameras capture images every second, and sensors acquire data every minute.

[1283] Data integration and preprocessing measures

[1284] The server receives data sent from the devices and integrates data from different sources. It centrally manages the data using a database system (e.g., MySQL, PostgreSQL) and performs preprocessing such as data shaping and filtering. Specifically, it matches data based on timestamps to remove incomplete data and noise. This prepares a reliable dataset for subsequent analysis.

[1285] Data analysis and prediction methods using generative artificial intelligence

[1286] The server uses the preprocessed data to analyze and predict the data using generative artificial intelligence (e.g., GPT-4). This allows it to analyze current traffic patterns and build a traffic congestion prediction model. The generative artificial intelligence also simulates future traffic conditions and displays the results on a dashboard. A specific example of its operation is outputting the predicted traffic volume for a specific time period next week.

[1287] Anomaly detection means

[1288] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (such as traffic accidents or the passing of emergency vehicles). If an anomaly is detected, a warning alert is sent immediately via email or SMS.

[1289] Problem solving proposals and implementation methods

[1290] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. Specific countermeasures include adjusting traffic light timing and providing detour route instructions. These proposals are based on the results of evaluations conducted through simulations. The device automatically updates the settings of traffic lights and related equipment based on the optimization parameters sent from the server and implements the proposed countermeasures.

[1291] Citizen response measures

[1292] Users can use their smartphones or computer terminals to access the citizen chat system and ask questions or make inquiries. The server uses generative artificial intelligence to quickly provide appropriate answers to user inquiries. As a specific example of how it works, it generates a response based on real-time data in response to the question, "What is the current traffic congestion situation?"

[1293] Improvement and Feedback Vehicles

[1294] The server constantly monitors the results of the implemented measures, evaluates their effectiveness, and continuously improves the system. By collecting performance data and updating the artificial intelligence model, the accuracy and performance of the system are improved.

[1295] User response using an emotion engine

[1296] The emotion engine evaluates and recognizes emotions in real time when a user makes an inquiry through voice tone, text patterns, and facial expression recognition. The server uses this emotional data to provide a more emotionally appropriate response through generative AI. For example, if the server detects anxiety or impatience in a response such as "I'm going to be delayed due to traffic congestion," it will calmly offer advice on alternative routes and timetables.

[1297] The following are examples of prompt sentences:

[1298] "Please predict traffic conditions in the city and suggest the best route. The current time is 2:00 PM and the destination is City Hall."

[1299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1300] Step 1: Data collection

[1301] The terminals collect traffic and environmental data from various sensors and cameras installed in the city. Specifically, cameras that count traffic volume and sensors that measure air quality are used. The input is real-time data from each sensor and camera. The output is the collected data sent to a server via a network.

[1302] Step 2: Send data

[1303] The terminal sends the collected data to a server via the Internet or a dedicated network. Specific examples of operation include sending data using the HTTP protocol or MQTT protocol. The input is the data collected on the terminal. The output is a series of data sent to the server.

[1304] Step 3: Data Integration

[1305] The server receives data sent from the devices and integrates data from different sources. As input, there is data received from the devices with different formats and timestamps. As output, there is an integrated and consistent data set. Specifically, the data is centrally managed using a database system (e.g., MySQL, PostgreSQL).

[1306] Step 4: Data Preprocessing

[1307] The server performs preprocessing on the integrated data. The input is the integrated data. The output is a preprocessed, clean dataset. Specifically, the server uses the Python pandas library to shape and filter the data, removing incomplete data and noise.

[1308] Step 5: Data analysis and prediction

[1309] The server uses the preprocessed data to analyze and make predictions using generative artificial intelligence (such as GPT-4). The input is the preprocessed, clean data. The output is a prediction of current and future traffic patterns and conditions. As a specific example of how it works, the predicted traffic volume for a specific time period next week is displayed on a dashboard.

[1310] Step 6: Anomaly detection

[1311] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies. The input is the real-time data stream. The output is detected anomalous events and warning alerts. If an anomaly is detected, relevant parties are immediately notified via email or SMS.

[1312] Step 7: Propose and implement solutions

[1313] The server uses generative artificial intelligence to propose optimal countermeasures for detected anomalies. The input is the anomaly detection results. The output is the proposed countermeasures and the results of their implementation. Specific countermeasures include adjusting traffic light timing and setting up detour routes. The terminal updates the settings of traffic lights and related equipment based on the optimization parameters from the server.

[1314] Step 8: Public response

[1315] Users can access the citizen chat system using smartphones or computer terminals to ask questions or make inquiries. The input is the user's inquiry. The output is an answer generated by generative artificial intelligence. For example, a question such as "What is the current traffic congestion situation?" will be answered based on real-time data.

[1316] Step 9: Improve and Feedback

[1317] The server constantly monitors the results of the implemented measures, evaluates their effectiveness, and continuously improves the system. The input is a variety of performance data. The output is an updated AI model and improved system performance. Updating the AI ​​model based on the performance data improves the accuracy of the entire system.

[1318] Step 10: User interaction with emotion engine

[1319] The emotion engine evaluates and recognizes emotions in real time when a user makes an inquiry through voice tone, text patterns, facial expression recognition, etc. The input is the user's voice and text data. The output is a response that corresponds to the user's emotional state. The server takes in this emotional data and the generative artificial intelligence provides a response that is more appropriate to the emotion. As a specific example of how it works, if the server senses anxiety or impatience in response to an inquiry such as "I'm going to be late because of traffic congestion," it will provide advice on alternative routes and time in a calm tone.

[1320] (Application example 2)

[1321] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1322] With the recent advancement of urbanization, the use of self-driving vehicles has become more widespread, but problems such as traffic congestion and accidents remain unresolved. Furthermore, methods for reducing the stress and anxiety felt by users of self-driving vehicles have not yet been fully established. Therefore, there is a need for a system that can predict traffic conditions in real time, provide optimal routes, and respond appropriately to users' emotional states.

[1323] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1324] In this invention, the server includes a data collection means, a data integration and preprocessing means, a data analysis and prediction means using generative artificial intelligence, an anomaly detection means, a problem countermeasure proposal and implementation means, a citizen response means, an improvement and feedback means, an emotion recognition means for recognizing the driver's emotion, a traffic pattern analysis and traffic prediction means using generative artificial intelligence, and an anomaly detection and optimal route proposal means. This makes it possible to predict traffic conditions in real time, provide optimal routes, and take appropriate measures according to the user's emotional state.

[1325] "Data collection means" refers to the means of collecting traffic data, environmental data, etc. in real time from devices such as sensors and cameras within the city.

[1326] "Data integration and pre-processing means" means for receiving data transmitted from the terminals, integrating data from different sources, and shaping and filtering the data to remove incomplete data and noise.

[1327] "Data analysis and prediction means using generative artificial intelligence" refers to a means of analyzing pre-processed data using generative artificial intelligence to analyze current traffic patterns and construct a traffic congestion prediction model.

[1328] An "anomaly detection method" is a method that monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic congestion or accidents).

[1329] "Proposal and implementation of solutions to the problem" refers to a method in which the generative artificial intelligence proposes optimal solutions to detected anomalies, then conducts simulations to evaluate the effectiveness of the proposed solutions, and then implements the optimal solutions.

[1330] "Citizen response means" refers to the means by which users can access the citizen chat system using their smartphones or terminals and make inquiries when they have problems or questions.

[1331] "Improvement and feedback measures" are measures to monitor the results of the measures implemented, evaluate their effectiveness, and continuously improve the generative artificial intelligence model.

[1332] The "emotion recognition means for recognizing the driver's emotions" is a means for evaluating the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc., and optimizing the response based on the emotional data.

[1333] "Means for analyzing traffic patterns and forecasting traffic using generative artificial intelligence" refers to means for analyzing current traffic patterns using generative artificial intelligence, simulating future traffic conditions, and displaying the results on a dashboard.

[1334] The "means for detecting anomalies and proposing optimal routes" is a means for detecting unexpected anomalies using generative artificial intelligence and proposing alternative routes, times, etc. as optimal countermeasures.

[1335] The present invention relates to an urban traffic information system for autonomous vehicles, which is a system that integrates and provides functions such as data collection, data integration, prediction, anomaly detection, countermeasure proposal, user response, emotion recognition, etc. This system is implemented as follows.

[1336] Data collection methods

[1337] Devices (e.g., cameras and sensors) installed in autonomous vehicles collect traffic and environmental data within the city in real time. The collected data is sent to a server using wireless communication. This process uses the Internet or a dedicated network.

[1338] Data integration and preprocessing measures

[1339] The server receives the data sent from the devices and processes it to integrate data from different sources, during which pre-processing such as data shaping and filtering is performed to remove incomplete data and noise.

[1340] Data analysis and prediction methods using generative artificial intelligence

[1341] Once preprocessing is complete, the data is analyzed by a generative AI running on the server. The generative AI analyzes traffic patterns and builds a predictive model for traffic congestion. The generative AI also simulates future traffic conditions and displays the results on a dashboard. For example, a model such as GPT-4 is used as the generative AI model.

[1342] Anomaly detection means

[1343] The server monitors the data in real time and uses generative artificial intelligence to detect unexpected anomalies (e.g., traffic jams or accidents), and if an anomaly is detected, it issues an immediate alert.

[1344] Problem solving proposals and implementation methods

[1345] If an anomaly is detected, the generative AI proposes optimal countermeasures, such as adjusting traffic light timings or reconfiguring traffic routes. Simulations are then performed to evaluate the effectiveness of the proposed countermeasures and the optimal countermeasures are implemented. The optimization parameters are sent to a terminal in the vehicle and applied in real time.

[1346] Citizen response measures

[1347] Users can access the citizen chat system using their smartphones or in-car devices and make inquiries. The server receives inquiries from users via the chat system, and generative artificial intelligence generates the optimal answer. As a specific example, in response to an inquiry such as "Please tell me the current congestion situation," real-time traffic data and predicted results are provided.

[1348] Improvement and Feedback Vehicles

[1349] The server monitors the results of implemented measures and evaluates their effectiveness, collects quantitative performance data to continuously improve the generative AI model, and continues to collect data over time to provide data for detecting new patterns and anomalies.

[1350] emotion recognition means

[1351] The emotion recognition means evaluates the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc. This emotional data is acquired when the user makes an inquiry. For example, when a user makes an inquiry such as "I'm going to be late for work because of traffic jams," the emotion recognition means senses impatience and stress from the user's voice tone and choice of words. Based on this information, the generative artificial intelligence on the server generates the optimal answer and creates an appropriate message to alleviate the user's anxiety.

[1352] Prompt Sentence Examples

[1353] "Based on current city traffic data, predict the traffic situation for the next hour and indicate the possibility of traffic congestion and accidents."

[1354] This system can significantly improve operational efficiency and driver happiness in autonomous vehicles.

[1355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1356] Step 1:

[1357] The autonomous vehicle's terminal collects real-time traffic and environmental data from the vehicle's onboard cameras and sensors. The collected data is then transmitted by the terminal to a server via wireless communication. Input data at this stage include traffic volume, temperature, humidity, wind speed, etc. As an output, data packets are sent to the server.

[1358] Step 2:

[1359] The server receives the data sent from the devices and integrates the data from different sources. Specifically, pre-processing such as data shaping and filtering is performed to remove incomplete data and noise. The input at this stage is the raw data sent from the devices, and the output is shaped and clean data.

[1360] Step 3:

[1361] The server provides the preprocessed data to a generative AI, which analyzes and predicts traffic patterns. The prompt is "Analyze the current traffic patterns and predict them for the next hour." The input is preprocessed traffic data, and the generative AI outputs a traffic congestion prediction model.

[1362] Step 4:

[1363] The server monitors data in real time and uses generative artificial intelligence to detect unexpected anomalies (traffic congestion, accidents, etc.). Real-time traffic data is used as input, and if an anomaly is detected, the type of anomaly and its location are output. The server issues a warning based on this.

[1364] Step 5:

[1365] When an anomaly is detected, the server uses generative artificial intelligence to propose optimal countermeasures. For example, it may suggest adjusting traffic light timings or recommending alternative routes. Simulations are performed to evaluate the effectiveness of the proposed countermeasures. The input at this stage is the anomaly detection information, and the output is the optimal countermeasure.

[1366] Step 6:

[1367] The server sends the optimization parameters to the terminal in the vehicle, which then applies the proposed measures in real time. The terminal updates the in-vehicle system based on the received optimization parameters and performs traffic light and route adjustments. The optimization parameters are the input, and the results of the adjustments are the output.

[1368] Step 7:

[1369] Users access the citizen chat system using their smartphones or in-car devices and make inquiries. The server receives the user's inquiry via the chat system, and the generative AI generates the optimal answer. The input is the user's inquiry information, and the output is the answer provided by the generative AI.

[1370] Step 8:

[1371] The server monitors the results of the implemented measures and evaluates their effectiveness. It continues to collect data over the long term and provides data to detect new patterns and anomalies. The input is data on the effectiveness of the implemented measures, and the output is information that will be useful for further improvements and feedback.

[1372] Step 9:

[1373] The emotion recognition means evaluates the user's emotional state using the user's voice tone, character usage patterns, facial expression recognition, etc. When the user makes a query, this emotional data is sent to the server. The input is the user's emotional data, and the output is an optimal answer that matches the user's emotional state.

[1374] In this way, each processing step involves data collection, integration, analysis, anomaly detection, proposals, execution, user support, improvement feedback, and emotion recognition, resulting in a system that can predict traffic conditions in real time, provide optimal routes, and respond appropriately to the driver's emotional state.

[1375] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1376] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1377] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1378] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1379] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1380] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1381] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1382] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1383] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1384] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1385] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1386] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1387] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1388] 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.

[1389] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1390] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1391] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1392] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1393] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1394] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1395] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1396] The following is further disclosed regarding the above embodiment.

[1397] (Claim 1)

[1398] data collection means;

[1399] data integration and preprocessing means;

[1400] A data analysis and prediction method using generative artificial intelligence;

[1401] An anomaly detection means;

[1402] Proposals for solutions to the problem and implementation measures;

[1403] citizen response measures, and

[1404] Improvement and feedback measures;

[1405] A system including:

[1406] (Claim 2)

[1407] The system of claim 1, wherein the generative artificial intelligence detects anomalies through the data analysis and proposes countermeasures.

[1408] (Claim 3)

[1409] 10. The system of claim 1, wherein the system responds to citizen inquiries in real time via citizen-oriented communication means.

[1410] "Example 1"

[1411] (Claim 1)

[1412] A data collection method that uses sensors installed at intersections and major traffic points within the city to collect data on vehicle flow, speed, congestion, etc. in real time;

[1413] a data integration and pre-processing means for receiving the data collected by the data collection means, integrating the data collected from different sources, and performing pre-processing such as shaping and filtering the data;

[1414] a data analysis and prediction means for analyzing the data preprocessed by the data integration and preprocessing means using generative artificial intelligence to analyze traffic patterns and construct a traffic congestion prediction model;

[1415] An anomaly detection method that monitors data flowing in real time and detects unexpected anomalies using generative artificial intelligence;

[1416] A generative artificial intelligence proposes optimal countermeasures for the detected anomalies, simulates the countermeasures, evaluates their effectiveness, and implements the countermeasures; and

[1417] A citizen response means in which a user accesses the citizen chat system via a smartphone or terminal, makes an inquiry to the operator, and a generative artificial intelligence generates an optimal response to the inquiry;

[1418] Improvement and feedback measures to monitor the results of the implemented measures, evaluate their effectiveness, and collect performance data to continuously improve the generative artificial intelligence model; and

[1419] A system including:

[1420] (Claim 2)

[1421] The system of claim 1, wherein the generative artificial intelligence detects anomalies through t...

Claims

1. data collection means; data integration and preprocessing means; A data analysis and prediction method using generative artificial intelligence; An anomaly detection means; Proposals for solutions to the problem and implementation measures; citizen response measures, and Improvement and feedback measures; A system including:

2. The system according to claim 1 , wherein the generative artificial intelligence detects anomalies through the data analysis and proposes countermeasures.

3. 10. The system of claim 1, wherein the system responds to inquiries from citizens in real time via citizen-oriented communication means.

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