system

Abstract

We provide the system. [Solution] A means of collecting information from data acquisition mechanisms placed in the urban environment, A means of performing anomaly detection and future prediction using generative artificial intelligence based on collected information, A means for creating an optimized resource allocation plan based on the results of the anomaly detection and future prediction, Means for notifying the management organization of the aforementioned optimization plan, A means of providing information to public devices and promoting environmental protection and resource conservation actions, A means for citizens to receive real-time data and receive advice to support the efficient use of resources and urban functions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an urban environment, there are complex problems such as infrastructure aging, traffic congestion, inefficient use of energy, air pollution, and the risk of disasters. These affect the quality of life of residents and are factors that reduce the sustainability of urban operations. In particular, in order to address these problems, real-time data monitoring and efficient resource management are essential. However, in reality, it is difficult to collect these data centrally and process them quickly and appropriately. Therefore, there is a need for a system that comprehensively manages the situation of the entire city, efficiently allocates resources, and improves the environmental awareness of residents.

Means for Solving the Problems

[0005] This invention provides a system that collects information from various data acquisition devices in urban environments and analyzes it in real time using generative artificial intelligence. Specifically, it includes information collection means, analysis means for anomaly detection and future prediction, planning means for creating an optimal resource allocation plan based on the analysis results, notification means for management devices, and means for providing information to resident devices, thereby supporting the sustainable operation of cities. Furthermore, it enhances urban safety and security by sensing the risks of natural disasters and abnormal situations and enabling rapid response. In this way, this invention provides an effective and efficient solution to the problems of urban management.

[0006] "Information gathering means" refers to devices and software used to collect information from various data acquisition devices placed in the urban environment.

[0007] "Generative artificial intelligence" is a form of artificial intelligence that uses machine learning techniques to identify patterns in data and generate new information.

[0008] "Anomaly detection" refers to the process of identifying patterns or data that deviate from normal conditions and issuing warnings.

[0009] "Future prediction" refers to predicting future trends and events based on past and present data.

[0010] "Analysis means" refers to devices and software used to process collected data according to a specific purpose and obtain meaningful information.

[0011] A "resource allocation plan" refers to a plan for efficiently allocating available resources within a city.

[0012] "Planning tools" refer to devices or software used to formulate specific action plans based on analysis results.

[0013] "Notification means" refers to devices and software used to transmit information to management devices and related organizations.

[0014] The "resident device" refers to devices and applications for providing information to residents and conducting interactions.

[0015] The "response plan" refers to a plan formulated in advance with action guidelines for risks and abnormal situations.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying out the Invention

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

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

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

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0037] This invention is a system that supports sustainable urban management through the collection and analysis of real-time data in urban environments. This system consists of multiple components.

[0038] First, the server collects information from various data acquisition devices within the city, such as traffic sensors, energy consumption monitors, water meters, and air quality monitors. This information is diverse and includes data such as traffic volume, energy usage, water resource usage, and air pollution levels. The server stores the collected data in a cloud database and prepares it for analysis.

[0039] Next, the server uses generative artificial intelligence to analyze the collected data in real time. This analysis enables the detection of anomalies and the forecasting of future demand. For example, it can identify congestion patterns on specific roads from traffic data and predict peak demand from energy data.

[0040] Based on the analysis results, the server develops a resource optimization plan. For example, it might adjust traffic signals appropriately to improve traffic flow or optimize energy supply to reduce waste, and notify the management system of this plan.

[0041] Furthermore, if a risk of natural disaster or an abnormal situation is detected, the server can immediately generate a response plan and notify the relevant management devices and emergency services, enabling rapid countermeasures.

[0042] For residents, devices (smartphones and personal computers) play a role in providing information. Users can, for example, receive information about peak energy consumption times and air quality, and adjust their daily activities accordingly. This allows users to contribute to environmental protection and resource conservation.

[0043] As described above, the system of the present invention makes multifaceted use of data in the urban environment, enabling efficient and sustainable urban management. This specific example demonstrates how this system functions and contributes to cities and their residents.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects data in real time from various data acquisition devices located throughout the city. This includes traffic volume data from traffic sensors, energy consumption data from smart meters, water usage data from water meters, and environmental data from air quality monitors.

[0047] Step 2:

[0048] The server stores the collected data in a cloud database and performs preprocessing to standardize the data format and make it analyzable. This centralizes information from different data sources.

[0049] Step 3:

[0050] The server analyzes pre-processed data in real time using generative artificial intelligence. This analysis detects abnormal traffic patterns, sudden increases in energy consumption, and abnormal increases in pollutants.

[0051] Step 4:

[0052] The server predicts future demand based on the analysis results. This includes pattern prediction to avoid traffic congestion and forecasting peak energy demand. Based on this, it develops a resource optimization plan.

[0053] Step 5:

[0054] The server notifies management devices and relevant organizations of the optimization plan. Specifically, this includes adjusting traffic signals, adjusting public transport schedules, and balancing energy supply.

[0055] Step 6:

[0056] When the server detects the risk of natural disasters or other abnormal situations, it quickly generates a response plan and notifies emergency services and government agencies. This helps to minimize damage.

[0057] Step 7:

[0058] The device provides users with information based on analysis results. Specifically, it notifies them of peak energy consumption times, safe travel routes, and local air quality information. Users can then adjust their daily activities based on this information.

[0059] Step 8:

[0060] The server aggregates data from across the city to conduct sustainability assessments. Based on this, it generates detailed reports on the city's environmental impact and resource utilization efficiency, which are then provided to government agencies and businesses.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In today's world, where sustainable resource management and efficient operation are essential in cities, it is crucial to collect and process complex and intertwined data in real time and generate effective countermeasures. However, conventional systems struggle to appropriately utilize this data and quickly provide useful information to users. In addition, there is a need for rapid and effective responses to natural disasters and other emergencies.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting data from various sensors placed in urban areas, means for storing the collected data in a cloud environment, and means for detecting anomaly patterns using generative artificial intelligence based on the collected data and predicting future demand. This enables efficient and sustainable operation of cities.

[0066] An "urban area" refers to a region where people live in close proximity and where commercial activities and transportation are active.

[0067] A "sensor" is a device or apparatus that detects specific phenomena or environmental conditions and transmits that data to a measuring device or control system.

[0068] "Data" refers to information and events that are observed, measured, or acquired, expressed numerically or symbolically, and serves as the basis for analysis and decision-making.

[0069] A "cloud environment" is a platform that provides information technology resources via the internet, and is an online infrastructure for storing and processing data.

[0070] "Generative artificial intelligence" refers to a group of algorithms or technologies that automatically generate information based on input data and provide new insights and suggestions based on the analysis results.

[0071] An "anomalous pattern" refers to a data trend or characteristic that indicates a phenomenon or state that differs from what was predicted or the normal state.

[0072] "Demand forecasting" is the act of estimating future consumption and usage patterns based on the analysis of past and present data.

[0073] This invention provides a system that enables data collection, analysis, and information provision in urban areas. In this system, a server plays a primary role.

[0074] The server collects data from various sensors, such as traffic flow sensors, energy consumption monitoring devices, water usage meters, and air quality monitors. This data is temporarily stored in a cloud environment for later processing.

[0075] Based on the collected data, the server uses generative artificial intelligence to analyze it. For example, it combines traffic data with historical data to detect abnormal congestion patterns and predict peak times for each road. It can also use energy consumption data to forecast future consumption peaks and formulate optimal resource allocation plans.

[0076] This information is provided to the user through a device, such as a smartphone or personal computer. Based on the information provided, the user can take more effective actions. For example, they can receive notifications about times when energy prices are high and choose to conserve energy.

[0077] As a concrete example, the server predicts congestion levels during specific time periods based on traffic data and sends a plan to promote efficient traffic flow by adjusting the timing of traffic signals. A benefit for users is that they can adjust the timing of outdoor activities based on air quality information displayed on their devices.

[0078] An example of a prompt message would be: "Based on real-time traffic data for the city, predict the peak congestion times for the next 24 hours. Also, identify areas where congestion is particularly expected."

[0079] In this way, the present invention supports sustainable management in urban environments and enables the efficient use of resources.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server collects data from various sensors placed within urban areas. Specifically, it receives data from traffic sensors, energy consumption monitors, water meters, and air quality monitors. This data includes, for example, traffic volume, energy consumption, water resource usage, and air pollution levels. The server standardizes the format of the received data and prepares it for storage in the cloud environment.

[0083] Step 2:

[0084] The server stores the collected data in a cloud environment. During this process, the data is organized chronologically and by location, and tagged in a way that facilitates searching. Raw data acquired from sensors is provided as input, and structured data stored in a cloud database is obtained as output.

[0085] Step 3:

[0086] The server uses generative artificial intelligence to analyze data stored in the cloud. The input is structured data retrieved from a cloud database, and the generative AI model detects anomaly patterns and forecasts demand. Through data analysis, it extracts congestion patterns on specific roads and predicts peak energy consumption times. A report of the analysis results is generated as output.

[0087] Step 4:

[0088] The server develops a resource optimization plan based on the analysis results. For example, it might plan to adjust traffic signal timing to minimize congestion or create a schedule to optimize energy supply. The input is a report of the analysis results, and the output is a specific optimization plan.

[0089] Step 5:

[0090] The server sends the optimization plan to the relevant control equipment and executes the instructions. A specific example is issuing instructions to a traffic signal control system to change the signal. The input is the optimization plan, and the output is the execution of the control instructions.

[0091] Step 6:

[0092] The terminal provides users with information based on analysis results and optimization plans. Through the terminal, users can receive information on peak energy consumption times and air quality, allowing them to adjust their daily activities. Input is information from the server, and output is notifications to the user.

[0093] This process supports the efficient and sustainable management of cities.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In urban environments, it is crucial to simultaneously achieve efficient and sustainable resource use and environmental protection. However, the current situation is that rapid responses and optimization plans based on real-time data are not adequately presented, leading to inefficiencies in urban functions and increased environmental burden. It is necessary to address this challenge and support sustainable urban management by providing information that directly benefits residents' actions.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for collecting information from data acquisition mechanisms placed in the urban environment, means for performing anomaly detection and future prediction using generative artificial intelligence based on the collected information, and means for creating an optimized resource allocation plan based on the results of the anomaly detection and future prediction. This makes it possible for citizens to receive real-time data and receive advice to support the efficient use of resources and urban functions.

[0099] The term "urban environment" refers to the space in which humans live, where various factors such as transportation, energy, water resources, and air quality are intricately intertwined.

[0100] A "data acquisition mechanism" is a group of devices that collect information in real time in urban environments, such as traffic sensors, energy monitors, water meters, and air quality monitors.

[0101] "Generative artificial intelligence" is an advanced computer program used to identify patterns and relationships from large amounts of data, and to perform anomaly detection and future predictions.

[0102] "Anomaly detection" is the process of identifying unusual situations or changes in the urban environment in real time.

[0103] "Future forecasting" is the process of predicting future demand and changes based on collected data.

[0104] A "resource allocation optimization plan" is a plan to efficiently allocate resources such as transportation, energy, and water within a city to achieve maximum convenience and minimum environmental impact.

[0105] A "management mechanism" is a central system for implementing optimization plans and managing various issues in the urban environment.

[0106] "Citizen devices" refer to terminals such as smartphones and computers that residents use to receive real-time information about the city and to improve their daily lives.

[0107] "Real-time data" refers to information that is collected and analyzed instantaneously, reflecting the situation at that particular moment.

[0108] "Providing advice" means recommending appropriate actions and choices to citizens based on the data collected.

[0109] In this invention, a server, terminal, and user collaborate to build a system. The server is located in the cloud and collects information from various data acquisition mechanisms in the urban environment. This includes traffic sensors, energy monitors, water meters, air quality monitors, etc. The collected data is analyzed on the server using generative artificial intelligence. This analysis uses backend programming languages ​​such as Python and Django, and a cloud database from Google Cloud Platform. For the generative AI, for example, a model from OpenAI® is applied.

[0110] The server analyzes the results to identify abnormal situations and forecast demand in the urban environment. Based on this information, an optimization plan for resource allocation is formulated and notified to the management organization. Information is also provided to citizens in real time through terminals. These terminals include smartphones and personal computers, and React Native is used for the user interface. The terminals present users with information on the city's resource usage and advice for efficiency, promoting environmental protection and resource conservation.

[0111] For example, when a terminal receives information about an area with very high traffic volume, a message is displayed to the user encouraging them to use public transport or choose an alternative route. This allows the user to take actions that support the efficiency of urban functions. Additionally, the generating AI model is input with a prompt message such as, "Based on the current traffic peak information for the specified area, suggest the optimal route and use of public transport for the user," and receives appropriate output.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server collects information from various data acquisition mechanisms, such as traffic sensors, energy monitors, water meters, and air quality monitors. Input data includes various measurements of traffic volume, energy consumption, water usage, and air quality. The collected data is stored in a cloud database on the Google Cloud Platform, preparing it for subsequent analysis processes.

[0115] Step 2:

[0116] The server retrieves data stored in a cloud database and performs analysis using generative artificial intelligence. The input data is real-time information collected in Step 1. In this analysis process, programs using Python or Django process the data and perform anomaly detection and future prediction. Specifically, they extract outliers and perform future demand forecasts based on past trends. The output results are used to optimize resources.

[0117] Step 3:

[0118] The server formulates an optimization plan for resource allocation based on the analysis results output by the generative AI. The input is the analysis results from step 2, and based on this, an optimal traffic allocation and energy supply adjustment plan is formulated. The server sends this to the management mechanism and generates resource control messages to automate the necessary adjustments.

[0119] Step 4:

[0120] The terminal receives optimization plans and real-time information sent from the server and disseminates it to citizens. While input is notifications from the server, the terminal displays the information in a user-friendly interface using React Native. Users can utilize resources more efficiently by receiving advice on avoiding traffic congestion and suggestions for reducing energy consumption.

[0121] Step 5:

[0122] Users adjust their daily activities using their devices. Specific inputs are information and advice displayed on the device, and based on this, users choose actions that contribute to the efficiency of urban functions, such as using public transportation or changing car routes. The output includes reducing the burden on the urban environment and improving individual convenience.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention is a system that combines the collection and analysis of real-time data in urban environments with an emotion engine that recognizes user emotions, thereby supporting sustainable urban management and improving the user experience.

[0125] First, the server collects information on traffic, energy, water use, and air quality from various data acquisition devices placed throughout the city and stores it in a cloud database. Next, this data is analyzed in real time using generative artificial intelligence to detect abnormal situations in the urban environment, forecast future demand, and formulate optimal resource allocation plans. This analysis makes it possible to alleviate traffic congestion and optimize energy consumption.

[0126] The device plays a crucial role in providing information to the user. Specifically, it notifies the user of peak energy consumption times and air quality levels, and provides advice to promote environmental protection and resource conservation in daily life. It also uses an emotion engine to recognize the user's emotions and dynamically adjusts the content and format of the information provided based on that information. This operation makes it possible to provide a comfortable user experience, such as avoiding information overload when the user is feeling stressed.

[0127] For example, when a user receives a notification about energy consumption via their smartphone, if the emotion engine detects an "anxious" state, the application can provide concise information along with positive feedback and encouraging messages.

[0128] In addition, the server accumulates data based on feedback from the emotion engine to improve urban management policies and notification content, thereby contributing to increased operational efficiency throughout the city. In this way, this system, which combines emotion recognition, helps to realize sustainable urban development with the cooperation of residents.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The server collects data in real time from various devices installed in the urban environment, such as traffic sensors, energy meters, water meters, and air quality monitors. This data is immediately integrated into a cloud database after collection.

[0132] Step 2:

[0133] The server centrally manages the collected data and performs analysis using generative artificial intelligence. This analysis includes anomaly detection, traffic pattern prediction, and energy demand forecasting. Based on the analysis results, an optimization plan for resource allocation is formulated.

[0134] Step 3:

[0135] The terminal receives analysis results sent from the server and provides information to the user. This information includes advice on improving energy efficiency and reports on the current state of air quality.

[0136] Step 4:

[0137] The device uses its built-in emotion engine to analyze the user's emotions in real time. This allows it to understand the user's feedback and emotional state, and fine-tune how information is delivered.

[0138] Step 5:

[0139] The server analyzes user emotion data obtained from the emotion engine and uses it to improve notification content and user approaches. For example, if a user is feeling stressed, the information provided will be simplified and encouraging messages will be added.

[0140] Step 6:

[0141] Users can adjust their daily behaviors appropriately based on analytical information and personalized advice provided by their devices. This allows them to actively participate in environmental protection and the efficient use of resources.

[0142] Step 7:

[0143] The server will aggregate all feedback data and complete the process of using it to improve city management and policies. The data will be regularly evaluated to help improve the city's sustainability and the quality of life for its residents.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] In modern urban environments, various urban problems such as traffic congestion, inefficient energy use, and environmental pollution are increasing. Under these circumstances, achieving sustainable urban management and improving the lives of residents requires real-time decision-making utilizing city-wide data, along with information provision that takes residents' feelings into consideration. However, current systems are not adequately addressing these challenges.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for collecting data from observation devices placed in the city, means for detecting anomalies and predicting future demand using a generative model based on the collected data, and means for evaluating the emotional state of users using an emotion recognition engine and adjusting the information provided based on those emotions. This enables sustainable operation by combining real-time data analysis and emotion recognition of the city.

[0149] "Observation equipment" refers to devices used to collect diverse data in urban environments, such as traffic flow, energy consumption, weather, and air quality.

[0150] A "generative model" is an artificial intelligence technology used in data analysis to perform anomaly detection and future predictions based on collected data.

[0151] Anomaly detection is the process of identifying phenomena or events that deviate from normal patterns.

[0152] "Predicting future demand" is a method of predicting future trends and needs by analyzing past and present data.

[0153] A "resource optimization plan" is a strategic plan for the efficient allocation of resources such as transportation, energy, and water in a city.

[0154] A "control device" refers to the various devices and systems that receive and execute the generated optimization plan.

[0155] "User devices" refer to terminals that allow individual residents to receive information and provide feedback on urban management.

[0156] An "emotion recognition engine" is a technology that detects the emotional state of a user and adjusts the way information is provided based on that state.

[0157] "Feedback" refers to information collected from users, such as responses and opinions, that is used to improve the system.

[0158] This system aims for sustainable urban management by combining real-time data analysis and emotion recognition in urban environments. The implementation details are described below.

[0159] The server collects data from observation devices placed throughout the city. These include sensors that monitor traffic flow, meters that measure energy use, and sensors that monitor air quality and water usage. The data obtained from these observation devices is stored in a cloud database in real time. Based on this data, the server uses generative AI models to perform anomaly detection and future predictions. Through the analysis of data patterns, the generative AI models enable, for example, predictions of traffic congestion and energy demand.

[0160] The device plays a crucial role in providing information to the user. This includes, for example, sending notifications when energy demand is at its peak or when air quality issues occur. The device is equipped with an emotion recognition engine that uses the camera and microphone to determine the user's emotional state. Based on this, the device can adjust the content and format of the information to match the user's emotions, providing the most appropriate information for the user.

[0161] As a concrete example, when a user receives an app notification on their smartphone, if the emotion recognition engine detects an "anxious state," the device will simplify the notification content and add a positive message. In this way, it becomes possible to provide information that is less likely to cause stress to the user.

[0162] Furthermore, the server contributes to improving urban management policies by accumulating user feedback and sentiment data. For example, if it is found that residents experience high levels of anxiety during a specific time period, the notification methods and content for that time period can be reviewed. This makes it possible to improve the overall operational efficiency of the city.

[0163] An example of a prompt to input into a generative AI model could be: "Please tell me about specific methods for urban management that utilize emotion recognition, and how to improve feedback to residents based on those methods."

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server collects real-time data from observation devices installed in the city. Specifically, it obtains vehicle count and speed data from traffic sensors, consumption data from energy meters, and environmental data such as PM2.5 and CO2 concentration from air quality sensors. This data is stored in a cloud database and forms the basis for analysis. The input is various data from observation devices, and the output is the centralization of this data and its storage in the database.

[0167] Step 2:

[0168] The server passes data stored in a cloud database to a generative AI model for anomaly detection and future prediction. The generative AI model learns anomaly patterns from historical data and analyzes real-time data. This allows it to predict traffic congestion and peak energy demand. The input is raw data stored in a cloud database, and the output is anomaly detection information and demand forecasts as analysis results.

[0169] Step 3:

[0170] The server formulates an optimal resource allocation plan based on the analysis results from the generated AI model. This process includes route suggestions to optimize traffic flow and the development of energy-saving measures. Inputs include anomaly detection information and demand forecasts, while the output is a specific proposal for the optimal allocation plan.

[0171] Step 4:

[0172] The terminal notifies the user of information based on an optimal allocation plan sent from the server. Here, an emotion recognition engine is used to analyze the user's emotional state and adjust the notification content accordingly. For example, when the user is feeling stressed, a concise message with reassuring content is provided. The inputs are the allocation plan from the server and emotion data from the emotion recognition engine, while the output is the adjusted information notification.

[0173] Step 5:

[0174] Users receive information notifications from their devices. If necessary, users input their status and feedback into their devices and send it to the server. The server then uses the collected feedback to improve future strategies. Input consists of information notifications from the device and user feedback, while output is feedback data sent to the server.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] Current urban environments face problems such as traffic congestion, inefficient energy consumption, and deteriorating air quality. Furthermore, residents often experience stress due to information overload. Conventional systems fail to not only collect and analyze this environmental data in real time, but also to provide information tailored to users' emotions, thereby failing to improve residents' living environments. Therefore, there is a need to develop a system that not only optimizes the urban environment but also provides information tailored to users' emotional states, offering a comfortable user experience while supporting sustainable urban management.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for collecting information from various data acquisition devices placed in urban spaces, means for performing anomaly detection and future prediction using generative artificial intelligence based on the collected information, and means for analyzing the emotional state of users and dynamically adjusting the content and format of the information provided based on that emotional state. This makes it possible not only to analyze data from the urban environment to detect anomalies and predict future demand, but also to realize a comfortable living environment through the provision of information that responds to the emotions of users, while simultaneously supporting sustainable urban management.

[0180] "Various data acquisition devices placed in urban spaces" refers to a general term for multiple sensors and devices installed within a city to collect information such as traffic, energy consumption, water use, and air quality.

[0181] "Generative artificial intelligence" is an artificial intelligence technology that learns patterns based on accumulated data to detect anomalies and predict the future.

[0182] "Means for anomaly detection and future prediction" refers to a process that analyzes urban environment data to detect current anomalies and predict future trends based on that data.

[0183] "Means for creating an optimized resource allocation plan" refers to a method for formulating a plan to efficiently and effectively allocate the available resources of a city based on the results of anomaly detection and future prediction.

[0184] "Means characterized by notifying control equipment" refers to a method of transmitting the created resource allocation optimization plan as commands or information to a management system or control device.

[0185] "User devices" refer to terminals used to provide information to people living in cities, such as smartphones and computers.

[0186] "Means for analyzing a user's emotional state and dynamically adjusting the content and format of information provided based on that emotional state" refers to using an emotion recognition engine to determine the emotions of individual users in real time and changing the type and method of information provided accordingly.

[0187] To implement this invention, a server first collects information from various data acquisition devices placed in urban spaces. This includes data such as traffic conditions, energy consumption, water usage, and air quality. This data is collected through sensors and IoT devices and integrated and managed by the server. The server inputs the collected data into generative artificial intelligence in real time to perform anomaly detection and future prediction. The generative artificial intelligence uses pattern recognition technology to analyze the data and predict signs of abnormal situations and future resource demands.

[0188] Based on this analysis, the server automatically generates an optimized resource allocation plan. This plan details how to effectively distribute resources within the city and is linked to the planning decision system. This plan is also communicated to control devices, enabling specific operations and adjustments.

[0189] Residents receive information from the server through their user devices (e.g., smartphones or tablets). The devices display notifications to encourage environmental protection and resource conservation. Furthermore, the user's emotional state is analyzed by an emotion recognition engine via the user device's camera and microphone. For example, if a user is feeling stressed, the device simplifies the presentation of information and delivers a positive, encouraging message.

[0190] As a concrete example of the present invention, consider a scenario where a user receives an air quality notification on their smartphone on a sunny day. If the air quality is poor, the emotion engine recognizes that the user has an anxious expression. At this time, the device suggests moving to a nearby park and sends a reassuring message to support the user in making a comfortable choice.

[0191] An example of a prompt message would be, "Generate the most appropriate notification message based on the user's current emotional state and urban environment data." By inputting this into the AI ​​model, the most relevant information will be provided.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server collects information on traffic, energy consumption, water use, and air quality from data acquisition devices placed throughout the urban space. This information is acquired in real time from various sensors and aggregated on the server as a dataset representing the state of the urban environment. This data serves as foundational data for subsequent analysis.

[0195] Step 2:

[0196] The server inputs the collected data into generative artificial intelligence to detect anomalies and make future predictions. Based on the input data, the AI ​​applies pattern recognition and machine learning algorithms to identify the occurrence of anomalies and make precise predictions about future resource demands. The calculated anomaly detection results and prediction data form the basis of the resource allocation plan.

[0197] Step 3:

[0198] The server creates an optimized resource allocation plan based on anomaly detection and future prediction results. Using the analysis results as input, it calculates efficient resource utilization methods and develops specific plans to optimize the allocation of different urban resources. The generated plans are output as commands to control equipment or as adjustable suggestions.

[0199] Step 4:

[0200] The terminal receives information from the server through the user's device and provides it to the user. This information is provided as notifications, such as peak energy consumption times and air quality levels, encouraging improvements to the living environment and a review of behavioral changes. Based on the received data, the terminal determines what information should be displayed and provides notifications in the most beneficial format for the user.

[0201] Step 5:

[0202] The device uses its camera and microphone to analyze the user's emotional state in real time. An emotion recognition engine analyzes the user's emotions from the input video and audio data to determine if they are experiencing stress. The analysis results are used to adjust the information presented.

[0203] Step 6:

[0204] The device dynamically adjusts the content and format of information provided based on the results of emotional state analysis. Specifically, if the user indicates stress, the information is simplified and a positive message is created and delivered. This allows the user to receive information in the most optimal state.

[0205] Step 7:

[0206] As an example of a prompt, the AI ​​model is input with the message, "Generate the optimal notification message based on the user's current emotional state and urban environment data," and generates appropriate notification content for each user. The generated notification message is presented in the most suitable format for the user.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention is a system that supports sustainable urban management through the collection and analysis of real-time data in urban environments. This system consists of multiple components.

[0224] First, the server collects information from various data acquisition devices within the city, such as traffic sensors, energy consumption monitors, water meters, and air quality monitors. This information is diverse and includes data such as traffic volume, energy usage, water resource usage, and air pollution levels. The server stores the collected data in a cloud database and prepares it for analysis.

[0225] Next, the server uses generative artificial intelligence to analyze the collected data in real time. This analysis enables the detection of anomalies and the forecasting of future demand. For example, it can identify congestion patterns on specific roads from traffic data and predict peak demand from energy data.

[0226] Based on the analysis results, the server develops a resource optimization plan. For example, it might adjust traffic signals appropriately to improve traffic flow or optimize energy supply to reduce waste, and notify the management system of this plan.

[0227] Furthermore, if a risk of natural disaster or an abnormal situation is detected, the server can immediately generate a response plan and notify the relevant management devices and emergency services, enabling rapid countermeasures.

[0228] For residents, devices (smartphones and personal computers) play a role in providing information. Users can, for example, receive information about peak energy consumption times and air quality, and adjust their daily activities accordingly. This allows users to contribute to environmental protection and resource conservation.

[0229] As described above, the system of the present invention makes multifaceted use of data in the urban environment, enabling efficient and sustainable urban management. This specific example demonstrates how this system functions and contributes to cities and their residents.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server collects data in real time from various data acquisition devices located throughout the city. This includes traffic volume data from traffic sensors, energy consumption data from smart meters, water usage data from water meters, and environmental data from air quality monitors.

[0233] Step 2:

[0234] The server stores the collected data in a cloud database and performs preprocessing to standardize the data format and make it analyzable. This centralizes information from different data sources.

[0235] Step 3:

[0236] The server analyzes pre-processed data in real time using generative artificial intelligence. This analysis detects abnormal traffic patterns, sudden increases in energy consumption, and abnormal increases in pollutants.

[0237] Step 4:

[0238] The server predicts future demand based on the analysis results. This includes pattern prediction to avoid traffic congestion and forecasting peak energy demand. Based on this, it develops a resource optimization plan.

[0239] Step 5:

[0240] The server notifies management devices and relevant organizations of the optimization plan. Specifically, this includes adjusting traffic signals, adjusting public transport schedules, and balancing energy supply.

[0241] Step 6:

[0242] When the server detects the risk of natural disasters or other abnormal situations, it quickly generates a response plan and notifies emergency services and government agencies. This helps to minimize damage.

[0243] Step 7:

[0244] The device provides users with information based on analysis results. Specifically, it notifies them of peak energy consumption times, safe travel routes, and local air quality information. Users can then adjust their daily activities based on this information.

[0245] Step 8:

[0246] The server aggregates data from across the city to conduct sustainability assessments. Based on this, it generates detailed reports on the city's environmental impact and resource utilization efficiency, which are then provided to government agencies and businesses.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] In today's world, where sustainable resource management and efficient operation are essential in cities, it is crucial to collect and process complex and intertwined data in real time and generate effective countermeasures. However, conventional systems struggle to appropriately utilize this data and quickly provide useful information to users. In addition, there is a need for rapid and effective responses to natural disasters and other emergencies.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for collecting data from various sensors placed in urban areas, means for storing the collected data in a cloud environment, and means for detecting anomaly patterns using generative artificial intelligence based on the collected data and predicting future demand. This enables efficient and sustainable operation of cities.

[0252] An "urban area" refers to a region where people live in close proximity and where commercial activities and transportation are active.

[0253] A "sensor" is a device or apparatus that detects specific phenomena or environmental conditions and transmits that data to a measuring device or control system.

[0254] "Data" refers to information and events that are observed, measured, or acquired, expressed numerically or symbolically, and serves as the basis for analysis and decision-making.

[0255] A "cloud environment" is a platform that provides information technology resources via the internet, and is an online infrastructure for storing and processing data.

[0256] "Generative artificial intelligence" refers to a group of algorithms or technologies that automatically generate information based on input data and provide new insights and suggestions based on the analysis results.

[0257] An "anomalous pattern" refers to a data trend or characteristic that indicates a phenomenon or state that differs from what was predicted or the normal state.

[0258] "Demand forecasting" is the act of estimating future consumption and usage patterns based on the analysis of past and present data.

[0259] This invention provides a system that enables data collection, analysis, and information provision in urban areas. In this system, a server plays a primary role.

[0260] The server collects data from various sensors, such as traffic flow sensors, energy consumption monitoring devices, water usage meters, and air quality monitors. This data is temporarily stored in a cloud environment for later processing.

[0261] Based on the collected data, the server uses generative artificial intelligence to analyze it. For example, it combines traffic data with historical data to detect abnormal congestion patterns and predict peak times for each road. It can also use energy consumption data to forecast future consumption peaks and formulate optimal resource allocation plans.

[0262] This information is provided to the user through a device, such as a smartphone or personal computer. Based on the information provided, the user can take more effective actions. For example, they can receive notifications about times when energy prices are high and choose to conserve energy.

[0263] As a concrete example, the server predicts congestion levels during specific time periods based on traffic data and sends a plan to promote efficient traffic flow by adjusting the timing of traffic signals. A benefit for users is that they can adjust the timing of outdoor activities based on air quality information displayed on their devices.

[0264] An example of a prompt message would be: "Based on real-time traffic data for the city, predict the peak congestion times for the next 24 hours. Also, identify areas where congestion is particularly expected."

[0265] In this way, the present invention supports sustainable management in urban environments and enables the efficient use of resources.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server collects data from various sensors placed within urban areas. Specifically, it receives data from traffic sensors, energy consumption monitors, water meters, and air quality monitors. This data includes, for example, traffic volume, energy consumption, water resource usage, and air pollution levels. The server standardizes the format of the received data and prepares it for storage in the cloud environment.

[0269] Step 2:

[0270] The server stores the collected data in a cloud environment. During this process, the data is organized chronologically and by location, and tagged in a way that facilitates searching. Raw data acquired from sensors is provided as input, and structured data stored in a cloud database is obtained as output.

[0271] Step 3:

[0272] The server uses generative artificial intelligence to analyze data stored in the cloud. The input is structured data retrieved from a cloud database, and the generative AI model detects anomaly patterns and forecasts demand. Through data analysis, it extracts congestion patterns on specific roads and predicts peak energy consumption times. A report of the analysis results is generated as output.

[0273] Step 4:

[0274] The server develops a resource optimization plan based on the analysis results. For example, it might plan to adjust traffic signal timing to minimize congestion or create a schedule to optimize energy supply. The input is a report of the analysis results, and the output is a specific optimization plan.

[0275] Step 5:

[0276] The server sends the optimization plan to the relevant control equipment and executes the instructions. A specific example is issuing instructions to a traffic signal control system to change the signal. The input is the optimization plan, and the output is the execution of the control instructions.

[0277] Step 6:

[0278] The terminal provides users with information based on analysis results and optimization plans. Through the terminal, users can receive information on peak energy consumption times and air quality, allowing them to adjust their daily activities. Input is information from the server, and output is notifications to the user.

[0279] This process supports the efficient and sustainable operation of the city.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0282] In an urban environment, it is important to simultaneously achieve efficient and sustainable resource utilization and environmental protection. However, there is a current situation where rapid responses and the presentation of optimization plans based on real-time data are not sufficiently carried out, which has led to inefficiencies in urban functions and an increase in environmental burdens. It is necessary to solve this problem and support sustainable urban operations by providing information that is directly useful for the actions of residents.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for collecting information from data acquisition mechanisms arranged in the urban environment, means for performing anomaly detection and future prediction using generative artificial intelligence based on the collected information, and means for creating an optimization plan for resource allocation based on the results of the anomaly detection and future prediction. As a result, it becomes possible for citizens to receive real-time data and provide advice for supporting resource utilization and the efficiency of urban functions.

[0285] "Urban environment" refers to the space for human life in which various factors such as transportation, energy, water resources, and air quality are intricately intertwined.

[0286] "Data acquisition mechanisms" are a group of devices that collect information in real time, such as traffic sensors, energy monitors, water meters, and air quality monitors in the urban environment.

[0287] "Generative artificial intelligence" is an advanced computer program used to identify patterns and relationships from large amounts of data, and to perform anomaly detection and future predictions.

[0288] "Anomaly detection" is the process of identifying unusual situations or changes in the urban environment in real time.

[0289] "Future forecasting" is the process of predicting future demand and changes based on collected data.

[0290] A "resource allocation optimization plan" is a plan to efficiently allocate resources such as transportation, energy, and water within a city to achieve maximum convenience and minimum environmental impact.

[0291] A "management mechanism" is a central system for implementing optimization plans and managing various issues in the urban environment.

[0292] "Citizen devices" refer to terminals such as smartphones and computers that residents use to receive real-time information about the city and to improve their daily lives.

[0293] "Real-time data" refers to information that is collected and analyzed instantaneously, reflecting the situation at that particular moment.

[0294] "Providing advice" means recommending appropriate actions and choices to citizens based on the data collected.

[0295] In this invention, a server, terminal, and user collaborate to build a system. The server is located in the cloud and collects information from various data acquisition mechanisms in the urban environment. This includes traffic sensors, energy monitors, water meters, air quality monitors, etc. The collected data is analyzed on the server using generative artificial intelligence. This analysis uses backend programming languages ​​such as Python and Django, and a cloud database from Google Cloud Platform. For the generative AI, for example, a model from OpenAI is applied.

[0296] The server analyzes the results to identify abnormal situations and forecast demand in the urban environment. Based on this information, an optimization plan for resource allocation is formulated and notified to the management organization. Information is also provided to citizens in real time through terminals. These terminals include smartphones and personal computers, and React Native is used for the user interface. The terminals present users with information on the city's resource usage and advice for efficiency, promoting environmental protection and resource conservation.

[0297] For example, when a terminal receives information about an area with very high traffic volume, a message is displayed to the user encouraging them to use public transport or choose an alternative route. This allows the user to take actions that support the efficiency of urban functions. Additionally, the generating AI model is input with a prompt message such as, "Based on the current traffic peak information for the specified area, suggest the optimal route and use of public transport for the user," and receives appropriate output.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server collects information from various data acquisition mechanisms such as traffic sensors, energy monitors, water meters, air quality monitors, etc. The input data includes traffic volume, energy consumption, water resource usage, and various measurements of air quality. The collected data is stored in a cloud database on the Google Cloud Platform to prepare for subsequent analysis processes.

[0301] Step 2:

[0302] The server retrieves the data stored in the cloud database and performs analysis using generative artificial intelligence. The input data is the real-time information collected in Step 1. In this analysis process, programs using Python and Django process the data to perform anomaly detection and future prediction. Specifically, it extracts abnormal values and conducts future demand prediction based on past trends. The output results are utilized for resource optimization.

[0303] Step 3:

[0304] Based on the analysis results output by the generative AI, the server formulates an optimization plan for resource allocation. The input is the analysis result of Step 2, and based on this, an optimal traffic distribution and energy supply adjustment plan are formulated. The server sends this to the management mechanism and generates a resource control message for automating necessary adjustments.

[0305] Step 4:

[0306] The terminal receives the optimization plan and real-time information sent from the server and notifies the citizens. The input is the notification from the server, but on the terminal side, the information is displayed in a user-friendly interface using React Native. Users can receive advice on avoiding traffic congestion and proposals for reducing energy consumption, enabling efficient resource utilization.

[0307] Step 5:

[0308] Users adjust their daily activities using their devices. Specific inputs are information and advice displayed on the device, and based on this, users choose actions that contribute to the efficiency of urban functions, such as using public transportation or changing car routes. The output includes reducing the burden on the urban environment and improving individual convenience.

[0309] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0310] This invention is a system that combines the collection and analysis of real-time data in urban environments with an emotion engine that recognizes user emotions, thereby supporting sustainable urban management and improving the user experience.

[0311] First, the server collects information on traffic, energy, water use, and air quality from various data acquisition devices placed throughout the city and stores it in a cloud database. Next, this data is analyzed in real time using generative artificial intelligence to detect abnormal situations in the urban environment, forecast future demand, and formulate optimal resource allocation plans. This analysis makes it possible to alleviate traffic congestion and optimize energy consumption.

[0312] The device plays a crucial role in providing information to the user. Specifically, it notifies the user of peak energy consumption times and air quality levels, and provides advice to promote environmental protection and resource conservation in daily life. It also uses an emotion engine to recognize the user's emotions and dynamically adjusts the content and format of the information provided based on that information. This operation makes it possible to provide a comfortable user experience, such as avoiding information overload when the user is feeling stressed.

[0313] For example, when a user receives a notification about energy consumption via their smartphone, if the emotion engine detects an "anxious" state, the application can provide concise information along with positive feedback and encouraging messages.

[0314] In addition, the server accumulates data based on feedback from the emotion engine to improve urban management policies and notification content, thereby contributing to increased operational efficiency throughout the city. In this way, this system, which combines emotion recognition, helps to realize sustainable urban development with the cooperation of residents.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The server collects data in real time from various devices installed in the urban environment, such as traffic sensors, energy meters, water meters, and air quality monitors. This data is immediately integrated into a cloud database after collection.

[0318] Step 2:

[0319] The server centrally manages the collected data and performs analysis using generative artificial intelligence. This analysis includes anomaly detection, traffic pattern prediction, and energy demand forecasting. Based on the analysis results, an optimization plan for resource allocation is formulated.

[0320] Step 3:

[0321] The terminal receives analysis results sent from the server and provides information to the user. This information includes advice on improving energy efficiency and reports on the current state of air quality.

[0322] Step 4:

[0323] The device uses its built-in emotion engine to analyze the user's emotions in real time. This allows it to understand the user's feedback and emotional state, and fine-tune how information is delivered.

[0324] Step 5:

[0325] The server analyzes user emotion data obtained from the emotion engine and uses it to improve notification content and user approaches. For example, if a user is feeling stressed, the information provided will be simplified and encouraging messages will be added.

[0326] Step 6:

[0327] Users can adjust their daily behaviors appropriately based on analytical information and personalized advice provided by their devices. This allows them to actively participate in environmental protection and the efficient use of resources.

[0328] Step 7:

[0329] The server will aggregate all feedback data and complete the process of using it to improve city management and policies. The data will be regularly evaluated to help improve the city's sustainability and the quality of life for its residents.

[0330] (Example 2)

[0331] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0332] In modern urban environments, various urban problems such as traffic congestion, inefficient energy use, and environmental pollution are increasing. Under these circumstances, achieving sustainable urban management and improving the lives of residents requires real-time decision-making utilizing city-wide data, along with information provision that takes residents' feelings into consideration. However, current systems are not adequately addressing these challenges.

[0333] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0334] In this invention, the server includes means for collecting data from observation devices placed in the city, means for detecting anomalies and predicting future demand using a generative model based on the collected data, and means for evaluating the emotional state of users using an emotion recognition engine and adjusting the information provided based on those emotions. This enables sustainable operation by combining real-time data analysis and emotion recognition of the city.

[0335] "Observation equipment" refers to devices used to collect diverse data in urban environments, such as traffic flow, energy consumption, weather, and air quality.

[0336] A "generative model" is an artificial intelligence technology used in data analysis to perform anomaly detection and future predictions based on collected data.

[0337] Anomaly detection is the process of identifying phenomena or events that deviate from normal patterns.

[0338] "Predicting future demand" is a method of predicting future trends and needs by analyzing past and present data.

[0339] A "resource optimization plan" is a strategic plan for the efficient allocation of resources such as transportation, energy, and water in a city.

[0340] A "control device" refers to the various devices and systems that receive and execute the generated optimization plan.

[0341] "User devices" refer to terminals that allow individual residents to receive information and provide feedback on urban management.

[0342] An "emotion recognition engine" is a technology that detects the emotional state of a user and adjusts the way information is provided based on that state.

[0343] "Feedback" refers to information collected from users, such as responses and opinions, that is used to improve the system.

[0344] This system aims for sustainable urban management by combining real-time data analysis and emotion recognition in urban environments. The implementation details are described below.

[0345] The server collects data from observation devices placed throughout the city. These include sensors that monitor traffic flow, meters that measure energy use, and sensors that monitor air quality and water usage. The data obtained from these observation devices is stored in a cloud database in real time. Based on this data, the server uses generative AI models to perform anomaly detection and future predictions. Through the analysis of data patterns, the generative AI models enable, for example, predictions of traffic congestion and energy demand.

[0346] The device plays a crucial role in providing information to the user. This includes, for example, sending notifications when energy demand is at its peak or when air quality issues occur. The device is equipped with an emotion recognition engine that uses the camera and microphone to determine the user's emotional state. Based on this, the device can adjust the content and format of the information to match the user's emotions, providing the most appropriate information for the user.

[0347] As a concrete example, when a user receives an app notification on their smartphone, if the emotion recognition engine detects an "anxious state," the device will simplify the notification content and add a positive message. In this way, it becomes possible to provide information that is less likely to cause stress to the user.

[0348] Furthermore, the server contributes to improving urban management policies by accumulating user feedback and sentiment data. For example, if it is found that residents experience high levels of anxiety during a specific time period, the notification methods and content for that time period can be reviewed. This makes it possible to improve the overall operational efficiency of the city.

[0349] An example of a prompt to input into a generative AI model could be: "Please tell me about specific methods for urban management that utilize emotion recognition, and how to improve feedback to residents based on those methods."

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The server collects real-time data from observation devices installed in the city. Specifically, it obtains vehicle count and speed data from traffic sensors, consumption data from energy meters, and environmental data such as PM2.5 and CO2 concentration from air quality sensors. This data is stored in a cloud database and forms the basis for analysis. The input is various data from observation devices, and the output is the centralization of this data and its storage in the database.

[0353] Step 2:

[0354] The server passes data stored in a cloud database to a generative AI model for anomaly detection and future prediction. The generative AI model learns anomaly patterns from historical data and analyzes real-time data. This allows it to predict traffic congestion and peak energy demand. The input is raw data stored in a cloud database, and the output is anomaly detection information and demand forecasts as analysis results.

[0355] Step 3:

[0356] The server formulates an optimal resource allocation plan based on the analysis results from the generated AI model. This process includes route suggestions to optimize traffic flow and the development of energy-saving measures. Inputs include anomaly detection information and demand forecasts, while the output is a specific proposal for the optimal allocation plan.

[0357] Step 4:

[0358] The terminal notifies the user of information based on an optimal allocation plan sent from the server. Here, an emotion recognition engine is used to analyze the user's emotional state and adjust the notification content accordingly. For example, when the user is feeling stressed, a concise message with reassuring content is provided. The inputs are the allocation plan from the server and emotion data from the emotion recognition engine, while the output is the adjusted information notification.

[0359] Step 5:

[0360] Users receive information notifications from their devices. If necessary, users input their status and feedback into their devices and send it to the server. The server then uses the collected feedback to improve future strategies. Input consists of information notifications from the device and user feedback, while output is feedback data sent to the server.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0363] Current urban environments face problems such as traffic congestion, inefficient energy consumption, and deteriorating air quality. Furthermore, residents often experience stress due to information overload. Conventional systems fail to not only collect and analyze this environmental data in real time, but also to provide information tailored to users' emotions, thereby failing to improve residents' living environments. Therefore, there is a need to develop a system that not only optimizes the urban environment but also provides information tailored to users' emotional states, offering a comfortable user experience while supporting sustainable urban management.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes means for collecting information from various data acquisition devices placed in urban spaces, means for performing anomaly detection and future prediction using generative artificial intelligence based on the collected information, and means for analyzing the emotional state of users and dynamically adjusting the content and format of the information provided based on that emotional state. This makes it possible not only to analyze data from the urban environment to detect anomalies and predict future demand, but also to realize a comfortable living environment through the provision of information that responds to the emotions of users, while simultaneously supporting sustainable urban management.

[0366] "Various data acquisition devices placed in urban spaces" refers to a general term for multiple sensors and devices installed within a city to collect information such as traffic, energy consumption, water use, and air quality.

[0367] "Generative artificial intelligence" is an artificial intelligence technology that learns patterns based on accumulated data to detect anomalies and predict the future.

[0368] "Means for anomaly detection and future prediction" refers to a process that analyzes urban environment data to detect current anomalies and predict future trends based on that data.

[0369] "Means for creating an optimized resource allocation plan" refers to a method for formulating a plan to efficiently and effectively allocate the available resources of a city based on the results of anomaly detection and future prediction.

[0370] "Means characterized by notifying control equipment" refers to a method of transmitting the created resource allocation optimization plan as commands or information to a management system or control device.

[0371] "User devices" refer to terminals used to provide information to people living in cities, such as smartphones and computers.

[0372] "Means for analyzing a user's emotional state and dynamically adjusting the content and format of information provided based on that emotional state" refers to using an emotion recognition engine to determine the emotions of individual users in real time and changing the type and method of information provided accordingly.

[0373] To implement this invention, a server first collects information from various data acquisition devices placed in urban spaces. This includes data such as traffic conditions, energy consumption, water usage, and air quality. This data is collected through sensors and IoT devices and integrated and managed by the server. The server inputs the collected data into generative artificial intelligence in real time to perform anomaly detection and future prediction. The generative artificial intelligence uses pattern recognition technology to analyze the data and predict signs of abnormal situations and future resource demands.

[0374] Based on this analysis, the server automatically generates an optimized resource allocation plan. This plan details how to effectively distribute resources within the city and is linked to the planning decision system. This plan is also communicated to control devices, enabling specific operations and adjustments.

[0375] Residents receive information from the server through their user devices (e.g., smartphones or tablets). The devices display notifications to encourage environmental protection and resource conservation. Furthermore, the user's emotional state is analyzed by an emotion recognition engine via the user device's camera and microphone. For example, if a user is feeling stressed, the device simplifies the presentation of information and delivers a positive, encouraging message.

[0376] As a concrete example of the present invention, consider a scenario where a user receives an air quality notification on their smartphone on a sunny day. If the air quality is poor, the emotion engine recognizes that the user has an anxious expression. At this time, the device suggests moving to a nearby park and sends a reassuring message to support the user in making a comfortable choice.

[0377] An example of a prompt message would be, "Generate the most appropriate notification message based on the user's current emotional state and urban environment data." By inputting this into the AI ​​model, the most relevant information will be provided.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The server collects information on traffic, energy consumption, water use, and air quality from data acquisition devices placed throughout the urban space. This information is acquired in real time from various sensors and aggregated on the server as a dataset representing the state of the urban environment. This data serves as foundational data for subsequent analysis.

[0381] Step 2:

[0382] The server inputs the collected data into generative artificial intelligence to detect anomalies and make future predictions. Based on the input data, the AI ​​applies pattern recognition and machine learning algorithms to identify the occurrence of anomalies and make precise predictions about future resource demands. The calculated anomaly detection results and prediction data form the basis of the resource allocation plan.

[0383] Step 3:

[0384] The server creates an optimized resource allocation plan based on anomaly detection and future prediction results. Using the analysis results as input, it calculates efficient resource utilization methods and develops specific plans to optimize the allocation of different urban resources. The generated plans are output as commands to control equipment or as adjustable suggestions.

[0385] Step 4:

[0386] The terminal receives information from the server through the user's device and provides it to the user. This information is provided as notifications, such as peak energy consumption times and air quality levels, encouraging improvements to the living environment and a review of behavioral changes. Based on the received data, the terminal determines what information should be displayed and provides notifications in the most beneficial format for the user.

[0387] Step 5:

[0388] The device uses its camera and microphone to analyze the user's emotional state in real time. An emotion recognition engine analyzes the user's emotions from the input video and audio data to determine if they are experiencing stress. The analysis results are used to adjust the information presented.

[0389] Step 6:

[0390] The device dynamically adjusts the content and format of information provided based on the results of emotional state analysis. Specifically, if the user indicates stress, the information is simplified and a positive message is created and delivered. This allows the user to receive information in the most optimal state.

[0391] Step 7:

[0392] As an example of a prompt, the AI ​​model is input with the message, "Generate the optimal notification message based on the user's current emotional state and urban environment data," and generates appropriate notification content for each user. The generated notification message is presented in the most suitable format for the user.

[0393] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0394] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0395] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0399] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0400] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0401] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0402] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0403] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0404] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0405] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0406] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0407] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0408] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0409] This invention is a system that supports sustainable urban management through the collection and analysis of real-time data in urban environments. This system consists of multiple components.

[0410] First, the server collects information from various data acquisition devices within the city, such as traffic sensors, energy consumption monitors, water meters, and air quality monitors. This information is diverse and includes data such as traffic volume, energy usage, water resource usage, and air pollution levels. The server stores the collected data in a cloud database and prepares it for analysis.

[0411] Next, the server uses generative artificial intelligence to analyze the collected data in real time. This analysis enables the detection of anomalies and the forecasting of future demand. For example, it can identify congestion patterns on specific roads from traffic data and predict peak demand from energy data.

[0412] Based on the analysis results, the server develops a resource optimization plan. For example, it might adjust traffic signals appropriately to improve traffic flow or optimize energy supply to reduce waste, and notify the management system of this plan.

[0413] Furthermore, if a risk of natural disaster or an abnormal situation is detected, the server can immediately generate a response plan and notify the relevant management devices and emergency services, enabling rapid countermeasures.

[0414] For residents, devices (smartphones and personal computers) play a role in providing information. Users can, for example, receive information about peak energy consumption times and air quality, and adjust their daily activities accordingly. This allows users to contribute to environmental protection and resource conservation.

[0415] As described above, the system of the present invention makes multifaceted use of data in the urban environment, enabling efficient and sustainable urban management. This specific example demonstrates how this system functions and contributes to cities and their residents.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The server collects data in real time from various data acquisition devices located throughout the city. This includes traffic volume data from traffic sensors, energy consumption data from smart meters, water usage data from water meters, and environmental data from air quality monitors.

[0419] Step 2:

[0420] The server stores the collected data in a cloud database and performs preprocessing to standardize the data format and make it analyzable. This centralizes information from different data sources.

[0421] Step 3:

[0422] The server analyzes pre-processed data in real time using generative artificial intelligence. This analysis detects abnormal traffic patterns, sudden increases in energy consumption, and abnormal increases in pollutants.

[0423] Step 4:

[0424] The server predicts future demand based on the analysis results. This includes pattern prediction to avoid traffic congestion and forecasting peak energy demand. Based on this, it develops a resource optimization plan.

[0425] Step 5:

[0426] The server notifies management devices and relevant organizations of the optimization plan. Specifically, this includes adjusting traffic signals, adjusting public transport schedules, and balancing energy supply.

[0427] Step 6:

[0428] When the server detects the risk of natural disasters or other abnormal situations, it quickly generates a response plan and notifies emergency services and government agencies. This helps to minimize damage.

[0429] Step 7:

[0430] The device provides users with information based on analysis results. Specifically, it notifies them of peak energy consumption times, safe travel routes, and local air quality information. Users can then adjust their daily activities based on this information.

[0431] Step 8:

[0432] The server aggregates data from across the city to conduct sustainability assessments. Based on this, it generates detailed reports on the city's environmental impact and resource utilization efficiency, which are then provided to government agencies and businesses.

[0433] (Example 1)

[0434] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] In today's world, where sustainable resource management and efficient operation are essential in cities, it is crucial to collect and process complex and intertwined data in real time and generate effective countermeasures. However, conventional systems struggle to appropriately utilize this data and quickly provide useful information to users. In addition, there is a need for rapid and effective responses to natural disasters and other emergencies.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for collecting data from various sensors placed in urban areas, means for storing the collected data in a cloud environment, and means for detecting anomaly patterns using generative artificial intelligence based on the collected data and predicting future demand. This enables efficient and sustainable operation of cities.

[0438] An "urban area" refers to a region where people live in close proximity and where commercial activities and transportation are active.

[0439] A "sensor" is a device or apparatus that detects specific phenomena or environmental conditions and transmits that data to a measuring device or control system.

[0440] "Data" refers to information and events that are observed, measured, or acquired, expressed numerically or symbolically, and serves as the basis for analysis and decision-making.

[0441] A "cloud environment" is a platform that provides information technology resources via the internet, and is an online infrastructure for storing and processing data.

[0442] "Generative artificial intelligence" refers to a group of algorithms or technologies that automatically generate information based on input data and provide new insights and suggestions based on the analysis results.

[0443] An "anomalous pattern" refers to a data trend or characteristic that indicates a phenomenon or state that differs from what was predicted or the normal state.

[0444] "Demand forecasting" is the act of estimating future consumption and usage patterns based on the analysis of past and present data.

[0445] This invention provides a system that enables data collection, analysis, and information provision in urban areas. In this system, a server plays a primary role.

[0446] The server collects data from various sensors, such as traffic flow sensors, energy consumption monitoring devices, water usage meters, and air quality monitors. This data is temporarily stored in a cloud environment for later processing.

[0447] Based on the collected data, the server uses generative artificial intelligence to analyze it. For example, it combines traffic data with historical data to detect abnormal congestion patterns and predict peak times for each road. It can also use energy consumption data to forecast future consumption peaks and formulate optimal resource allocation plans.

[0448] This information is provided to the user through a device, such as a smartphone or personal computer. Based on the information provided, the user can take more effective actions. For example, they can receive notifications about times when energy prices are high and choose to conserve energy.

[0449] As a concrete example, the server predicts congestion levels during specific time periods based on traffic data and sends a plan to promote efficient traffic flow by adjusting the timing of traffic signals. A benefit for users is that they can adjust the timing of outdoor activities based on air quality information displayed on their devices.

[0450] An example of a prompt message would be: "Based on real-time traffic data for the city, predict the peak congestion times for the next 24 hours. Also, identify areas where congestion is particularly expected."

[0451] In this way, the present invention supports sustainable management in urban environments and enables the efficient use of resources.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The server collects data from various sensors placed within urban areas. Specifically, it receives data from traffic sensors, energy consumption monitors, water meters, and air quality monitors. This data includes, for example, traffic volume, energy consumption, water resource usage, and air pollution levels. The server standardizes the format of the received data and prepares it for storage in the cloud environment.

[0455] Step 2:

[0456] The server stores the collected data in a cloud environment. During this process, the data is organized chronologically and by location, and tagged in a way that facilitates searching. Raw data acquired from sensors is provided as input, and structured data stored in a cloud database is obtained as output.

[0457] Step 3:

[0458] The server uses generative artificial intelligence to analyze data stored in the cloud. The input is structured data retrieved from a cloud database, and the generative AI model detects anomaly patterns and forecasts demand. Through data analysis, it extracts congestion patterns on specific roads and predicts peak energy consumption times. A report of the analysis results is generated as output.

[0459] Step 4:

[0460] The server develops a resource optimization plan based on the analysis results. For example, it might plan to adjust traffic signal timing to minimize congestion or create a schedule to optimize energy supply. The input is a report of the analysis results, and the output is a specific optimization plan.

[0461] Step 5:

[0462] The server sends the optimization plan to the relevant control equipment and executes the instructions. A specific example is issuing instructions to a traffic signal control system to change the signal. The input is the optimization plan, and the output is the execution of the control instructions.

[0463] Step 6:

[0464] The terminal provides users with information based on analysis results and optimization plans. Through the terminal, users can receive information on peak energy consumption times and air quality, allowing them to adjust their daily activities. Input is information from the server, and output is notifications to the user.

[0465] This process supports the efficient and sustainable management of cities.

[0466] (Application Example 1)

[0467] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] In urban environments, it is crucial to simultaneously achieve efficient and sustainable resource use and environmental protection. However, the current situation is that rapid responses and optimization plans based on real-time data are not adequately presented, leading to inefficiencies in urban functions and increased environmental burden. It is necessary to address this challenge and support sustainable urban management by providing information that directly benefits residents' actions.

[0469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0470] In this invention, the server includes means for collecting information from data acquisition mechanisms placed in the urban environment, means for performing anomaly detection and future prediction using generative artificial intelligence based on the collected information, and means for creating an optimized resource allocation plan based on the results of the anomaly detection and future prediction. This makes it possible for citizens to receive real-time data and receive advice to support the efficient use of resources and urban functions.

[0471] The term "urban environment" refers to the space in which humans live, where various factors such as transportation, energy, water resources, and air quality are intricately intertwined.

[0472] A "data acquisition mechanism" is a group of devices that collect information in real time in urban environments, such as traffic sensors, energy monitors, water meters, and air quality monitors.

[0473] "Generative artificial intelligence" is an advanced computer program used to identify patterns and relationships from large amounts of data, and to perform anomaly detection and future predictions.

[0474] "Anomaly detection" is the process of identifying unusual situations or changes in the urban environment in real time.

[0475] "Future forecasting" is the process of predicting future demand and changes based on collected data.

[0476] A "resource allocation optimization plan" is a plan to efficiently allocate resources such as transportation, energy, and water within a city to achieve maximum convenience and minimum environmental impact.

[0477] A "management mechanism" is a central system for implementing optimization plans and managing various issues in the urban environment.

[0478] "Citizen devices" refer to terminals such as smartphones and computers that residents use to receive real-time information about the city and to improve their daily lives.

[0479] "Real-time data" refers to information that is collected and analyzed instantaneously, reflecting the situation at that particular moment.

[0480] "Providing advice" means recommending appropriate actions and choices to citizens based on the data collected.

[0481] In this invention, a server, terminal, and user collaborate to build a system. The server is located in the cloud and collects information from various data acquisition mechanisms in the urban environment. This includes traffic sensors, energy monitors, water meters, air quality monitors, etc. The collected data is analyzed on the server using generative artificial intelligence. This analysis uses backend programming languages ​​such as Python and Django, and a cloud database from Google Cloud Platform. For the generative AI, for example, a model from OpenAI is applied.

[0482] The server analyzes the results to identify abnormal situations and forecast demand in the urban environment. Based on this information, an optimization plan for resource allocation is formulated and notified to the management organization. Information is also provided to citizens in real time through terminals. These terminals include smartphones and personal computers, and React Native is used for the user interface. The terminals present users with information on the city's resource usage and advice for efficiency, promoting environmental protection and resource conservation.

[0483] For example, when a terminal receives information about an area with very high traffic volume, a message is displayed to the user encouraging them to use public transport or choose an alternative route. This allows the user to take actions that support the efficiency of urban functions. Additionally, the generating AI model is input with a prompt message such as, "Based on the current traffic peak information for the specified area, suggest the optimal route and use of public transport for the user," and receives appropriate output.

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] The server collects information from various data acquisition mechanisms, such as traffic sensors, energy monitors, water meters, and air quality monitors. Input data includes various measurements of traffic volume, energy consumption, water usage, and air quality. The collected data is stored in a cloud database on the Google Cloud Platform, preparing it for subsequent analysis processes.

[0487] Step 2:

[0488] The server retrieves data stored in a cloud database and performs analysis using generative artificial intelligence. The input data is real-time information collected in Step 1. In this analysis process, programs using Python or Django process the data and perform anomaly detection and future prediction. Specifically, they extract outliers and perform future demand forecasts based on past trends. The output results are used to optimize resources.

[0489] Step 3:

[0490] The server formulates an optimization plan for resource allocation based on the analysis results output by the generative AI. The input is the analysis results from step 2, and based on this, an optimal traffic allocation and energy supply adjustment plan is formulated. The server sends this to the management mechanism and generates resource control messages to automate the necessary adjustments.

[0491] Step 4:

[0492] The terminal receives optimization plans and real-time information sent from the server and disseminates it to citizens. While input is notifications from the server, the terminal displays the information in a user-friendly interface using React Native. Users can utilize resources more efficiently by receiving advice on avoiding traffic congestion and suggestions for reducing energy consumption.

[0493] Step 5:

[0494] Users adjust their daily activities using their devices. Specific inputs are information and advice displayed on the device, and based on this, users choose actions that contribute to the efficiency of urban functions, such as using public transportation or changing car routes. The output includes reducing the burden on the urban environment and improving individual convenience.

[0495] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0496] This invention is a system that combines the collection and analysis of real-time data in urban environments with an emotion engine that recognizes user emotions, thereby supporting sustainable urban management and improving the user experience.

[0497] First, the server collects information on traffic, energy, water use, and air quality from various data acquisition devices placed throughout the city and stores it in a cloud database. Next, this data is analyzed in real time using generative artificial intelligence to detect abnormal situations in the urban environment, forecast future demand, and formulate optimal resource allocation plans. This analysis makes it possible to alleviate traffic congestion and optimize energy consumption.

[0498] The device plays a crucial role in providing information to the user. Specifically, it notifies the user of peak energy consumption times and air quality levels, and provides advice to promote environmental protection and resource conservation in daily life. It also uses an emotion engine to recognize the user's emotions and dynamically adjusts the content and format of the information provided based on that information. This operation makes it possible to provide a comfortable user experience, such as avoiding information overload when the user is feeling stressed.

[0499] For example, when a user receives a notification about energy consumption via their smartphone, if the emotion engine detects an "anxious" state, the application can provide concise information along with positive feedback and encouraging messages.

[0500] In addition, the server accumulates data based on feedback from the emotion engine to improve urban management policies and notification content, thereby contributing to increased operational efficiency throughout the city. In this way, this system, which combines emotion recognition, helps to realize sustainable urban development with the cooperation of residents.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The server collects data in real time from various devices installed in the urban environment, such as traffic sensors, energy meters, water meters, and air quality monitors. This data is immediately integrated into a cloud database after collection.

[0504] Step 2:

[0505] The server centrally manages the collected data and performs analysis using generative artificial intelligence. This analysis includes anomaly detection, traffic pattern prediction, and energy demand forecasting. Based on the analysis results, an optimization plan for resource allocation is formulated.

[0506] Step 3:

[0507] The terminal receives analysis results sent from the server and provides information to the user. This information includes advice on improving energy efficiency and reports on the current state of air quality.

[0508] Step 4:

[0509] The device uses its built-in emotion engine to analyze the user's emotions in real time. This allows it to understand the user's feedback and emotional state, and fine-tune how information is delivered.

[0510] Step 5:

[0511] The server analyzes user emotion data obtained from the emotion engine and uses it to improve notification content and user approaches. For example, if a user is feeling stressed, the information provided will be simplified and encouraging messages will be added.

[0512] Step 6:

[0513] Users can adjust their daily behaviors appropriately based on analytical information and personalized advice provided by their devices. This allows them to actively participate in environmental protection and the efficient use of resources.

[0514] Step 7:

[0515] The server will aggregate all feedback data and complete the process of using it to improve city management and policies. The data will be regularly evaluated to help improve the city's sustainability and the quality of life for its residents.

[0516] (Example 2)

[0517] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] In modern urban environments, various urban problems such as traffic congestion, inefficient energy use, and environmental pollution are increasing. Under these circumstances, achieving sustainable urban management and improving the lives of residents requires real-time decision-making utilizing city-wide data, along with information provision that takes residents' feelings into consideration. However, current systems are not adequately addressing these challenges.

[0519] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0520] In this invention, the server includes means for collecting data from observation devices placed in the city, means for detecting anomalies and predicting future demand using a generative model based on the collected data, and means for evaluating the emotional state of users using an emotion recognition engine and adjusting the information provided based on those emotions. This enables sustainable operation by combining real-time data analysis and emotion recognition of the city.

[0521] "Observation equipment" refers to devices used to collect diverse data in urban environments, such as traffic flow, energy consumption, weather, and air quality.

[0522] A "generative model" is an artificial intelligence technology used in data analysis to perform anomaly detection and future predictions based on collected data.

[0523] Anomaly detection is the process of identifying phenomena or events that deviate from normal patterns.

[0524] "Predicting future demand" is a method of predicting future trends and needs by analyzing past and present data.

[0525] A "resource optimization plan" is a strategic plan for the efficient allocation of resources such as transportation, energy, and water in a city.

[0526] A "control device" refers to the various devices and systems that receive and execute the generated optimization plan.

[0527] "User devices" refer to terminals that allow individual residents to receive information and provide feedback on urban management.

[0528] An "emotion recognition engine" is a technology that detects the emotional state of a user and adjusts the way information is provided based on that state.

[0529] "Feedback" refers to information collected from users, such as responses and opinions, that is used to improve the system.

[0530] This system aims for sustainable urban management by combining real-time data analysis and emotion recognition in urban environments. The implementation details are described below.

[0531] The server collects data from observation devices placed throughout the city. These include sensors that monitor traffic flow, meters that measure energy use, and sensors that monitor air quality and water usage. The data obtained from these observation devices is stored in a cloud database in real time. Based on this data, the server uses generative AI models to perform anomaly detection and future predictions. Through the analysis of data patterns, the generative AI models enable, for example, predictions of traffic congestion and energy demand.

[0532] The device plays a crucial role in providing information to the user. This includes, for example, sending notifications when energy demand is at its peak or when air quality issues occur. The device is equipped with an emotion recognition engine that uses the camera and microphone to determine the user's emotional state. Based on this, the device can adjust the content and format of the information to match the user's emotions, providing the most appropriate information for the user.

[0533] As a concrete example, when a user receives an app notification on their smartphone, if the emotion recognition engine detects an "anxious state," the device will simplify the notification content and add a positive message. In this way, it becomes possible to provide information that is less likely to cause stress to the user.

[0534] Furthermore, the server contributes to improving urban management policies by accumulating user feedback and sentiment data. For example, if it is found that residents experience high levels of anxiety during a specific time period, the notification methods and content for that time period can be reviewed. This makes it possible to improve the overall operational efficiency of the city.

[0535] An example of a prompt to input into a generative AI model could be: "Please tell me about specific methods for urban management that utilize emotion recognition, and how to improve feedback to residents based on those methods."

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] The server collects real-time data from observation devices installed in the city. Specifically, it obtains vehicle count and speed data from traffic sensors, consumption data from energy meters, and environmental data such as PM2.5 and CO2 concentration from air quality sensors. This data is stored in a cloud database and forms the basis for analysis. The input is various data from observation devices, and the output is the centralization of this data and its storage in the database.

[0539] Step 2:

[0540] The server passes data stored in a cloud database to a generative AI model for anomaly detection and future prediction. The generative AI model learns anomaly patterns from historical data and analyzes real-time data. This allows it to predict traffic congestion and peak energy demand. The input is raw data stored in a cloud database, and the output is anomaly detection information and demand forecasts as analysis results.

[0541] Step 3:

[0542] The server formulates an optimal resource allocation plan based on the analysis results from the generated AI model. This process includes route suggestions to optimize traffic flow and the development of energy-saving measures. Inputs include anomaly detection information and demand forecasts, while the output is a specific proposal for the optimal allocation plan.

[0543] Step 4:

[0544] The terminal notifies the user of information based on an optimal allocation plan sent from the server. Here, an emotion recognition engine is used to analyze the user's emotional state and adjust the notification content accordingly. For example, when the user is feeling stressed, a concise message with reassuring content is provided. The inputs are the allocation plan from the server and emotion data from the emotion recognition engine, while the output is the adjusted information notification.

[0545] Step 5:

[0546] Users receive information notifications from their devices. If necessary, users input their status and feedback into their devices and send it to the server. The server then uses the collected feedback to improve future strategies. Input consists of information notifications from the device and user feedback, while output is feedback data sent to the server.

[0547] (Application Example 2)

[0548] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] Current urban environments face problems such as traffic congestion, inefficient energy consumption, and deteriorating air quality. Furthermore, residents often experience stress due to information overload. Conventional systems fail to not only collect and analyze this environmental data in real time, but also to provide information tailored to users' emotions, thereby failing to improve residents' living environments. Therefore, there is a need to develop a system that not only optimizes the urban environment but also provides information tailored to users' emotional states, offering a comfortable user experience while supporting sustainable urban management.

[0550] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0551] In this invention, the server includes means for collecting information from various data acquisition devices placed in urban spaces, means for performing anomaly detection and future prediction using generative artificial intelligence based on the collected information, and means for analyzing the emotional state of users and dynamically adjusting the content and format of the information provided based on that emotional state. This makes it possible not only to analyze data from the urban environment to detect anomalies and predict future demand, but also to realize a comfortable living environment through the provision of information that responds to the emotions of users, while simultaneously supporting sustainable urban management.

[0552] "Various data acquisition devices placed in urban spaces" refers to a general term for multiple sensors and devices installed within a city to collect information such as traffic, energy consumption, water use, and air quality.

[0553] "Generative artificial intelligence" is an artificial intelligence technology that learns patterns based on accumulated data to detect anomalies and predict the future.

[0554] "Means for anomaly detection and future prediction" refers to a process that analyzes urban environment data to detect current anomalies and predict future trends based on that data.

[0555] "Means for creating an optimized resource allocation plan" refers to a method for formulating a plan to efficiently and effectively allocate the available resources of a city based on the results of anomaly detection and future prediction.

[0556] "Means characterized by notifying control equipment" refers to a method of transmitting the created resource allocation optimization plan as commands or information to a management system or control device.

[0557] "User devices" refer to terminals used to provide information to people living in cities, such as smartphones and computers.

[0558] "Means for analyzing a user's emotional state and dynamically adjusting the content and format of information provided based on that emotional state" refers to using an emotion recognition engine to determine the emotions of individual users in real time and changing the type and method of information provided accordingly.

[0559] To implement this invention, a server first collects information from various data acquisition devices placed in urban spaces. This includes data such as traffic conditions, energy consumption, water usage, and air quality. This data is collected through sensors and IoT devices and integrated and managed by the server. The server inputs the collected data into generative artificial intelligence in real time to perform anomaly detection and future prediction. The generative artificial intelligence uses pattern recognition technology to analyze the data and predict signs of abnormal situations and future resource demands.

[0560] Based on this analysis, the server automatically generates an optimized resource allocation plan. This plan details how to effectively distribute resources within the city and is linked to the planning decision system. This plan is also communicated to control devices, enabling specific operations and adjustments.

[0561] Residents receive information from the server through their user devices (e.g., smartphones or tablets). The devices display notifications to encourage environmental protection and resource conservation. Furthermore, the user's emotional state is analyzed by an emotion recognition engine via the user device's camera and microphone. For example, if a user is feeling stressed, the device simplifies the presentation of information and delivers a positive, encouraging message.

[0562] As a concrete example of the present invention, consider a scenario where a user receives an air quality notification on their smartphone on a sunny day. If the air quality is poor, the emotion engine recognizes that the user has an anxious expression. At this time, the device suggests moving to a nearby park and sends a reassuring message to support the user in making a comfortable choice.

[0563] An example of a prompt message would be, "Generate the most appropriate notification message based on the user's current emotional state and urban environment data." By inputting this into the AI ​​model, the most relevant information will be provided.

[0564] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0565] Step 1:

[0566] The server collects information on traffic, energy consumption, water use, and air quality from data acquisition devices placed throughout the urban space. This information is acquired in real time from various sensors and aggregated on the server as a dataset representing the state of the urban environment. This data serves as foundational data for subsequent analysis.

[0567] Step 2:

[0568] The server inputs the collected data into generative artificial intelligence to detect anomalies and make future predictions. Based on the input data, the AI ​​applies pattern recognition and machine learning algorithms to identify the occurrence of anomalies and make precise predictions about future resource demands. The calculated anomaly detection results and prediction data form the basis of the resource allocation plan.

[0569] Step 3:

[0570] The server creates an optimized resource allocation plan based on anomaly detection and future prediction results. Using the analysis results as input, it calculates efficient resource utilization methods and develops specific plans to optimize the allocation of different urban resources. The generated plans are output as commands to control equipment or as adjustable suggestions.

[0571] Step 4:

[0572] The terminal receives information from the server through the user's device and provides it to the user. This information is provided as notifications, such as peak energy consumption times and air quality levels, encouraging improvements to the living environment and a review of behavioral changes. Based on the received data, the terminal determines what information should be displayed and provides notifications in the most beneficial format for the user.

[0573] Step 5:

[0574] The device uses its camera and microphone to analyze the user's emotional state in real time. An emotion recognition engine analyzes the user's emotions from the input video and audio data to determine if they are experiencing stress. The analysis results are used to adjust the information presented.

[0575] Step 6:

[0576] The device dynamically adjusts the content and format of information provided based on the results of emotional state analysis. Specifically, if the user indicates stress, the information is simplified and a positive message is created and delivered. This allows the user to receive information in the most optimal state.

[0577] Step 7:

[0578] As an example of a prompt, the AI ​​model is input with the message, "Generate the optimal notification message based on the user's current emotional state and urban environment data," and generates appropriate notification content for each user. The generated notification message is presented in the most suitable format for the user.

[0579] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0580] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0581] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0585] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0586] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0587] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0588] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0589] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0590] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0591] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0592] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0593] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0594] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0595] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0596] This invention is a system that supports sustainable urban management through the collection and analysis of real-time data in urban environments. This system consists of multiple components.

[0597] First, the server collects information from various data acquisition devices within the city, such as traffic sensors, energy consumption monitors, water meters, and air quality monitors. This information is diverse and includes data such as traffic volume, energy usage, water resource usage, and air pollution levels. The server stores the collected data in a cloud database and prepares it for analysis.

[0598] Next, the server uses generative artificial intelligence to analyze the collected data in real time. This analysis enables the detection of anomalies and the forecasting of future demand. For example, it can identify congestion patterns on specific roads from traffic data and predict peak demand from energy data.

[0599] Based on the analysis results, the server develops a resource optimization plan. For example, it might adjust traffic signals appropriately to improve traffic flow or optimize energy supply to reduce waste, and notify the management system of this plan.

[0600] Furthermore, if a risk of natural disaster or an abnormal situation is detected, the server can immediately generate a response plan and notify the relevant management devices and emergency services, enabling rapid countermeasures.

[0601] For residents, devices (smartphones and personal computers) play a role in providing information. Users can, for example, receive information about peak energy consumption times and air quality, and adjust their daily activities accordingly. This allows users to contribute to environmental protection and resource conservation.

[0602] As described above, the system of the present invention makes multifaceted use of data in the urban environment, enabling efficient and sustainable urban management. This specific example demonstrates how this system functions and contributes to cities and their residents.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The server collects data in real time from various data acquisition devices located throughout the city. This includes traffic volume data from traffic sensors, energy consumption data from smart meters, water usage data from water meters, and environmental data from air quality monitors.

[0606] Step 2:

[0607] The server stores the collected data in a cloud database and performs preprocessing to standardize the data format and make it analyzable. This centralizes information from different data sources.

[0608] Step 3:

[0609] The server analyzes pre-processed data in real time using generative artificial intelligence. This analysis detects abnormal traffic patterns, sudden increases in energy consumption, and abnormal increases in pollutants.

[0610] Step 4:

[0611] The server predicts future demand based on the analysis results. This includes pattern prediction to avoid traffic congestion and forecasting peak energy demand. Based on this, it develops a resource optimization plan.

[0612] Step 5:

[0613] The server notifies management devices and relevant organizations of the optimization plan. Specifically, this includes adjusting traffic signals, adjusting public transport schedules, and balancing energy supply.

[0614] Step 6:

[0615] When the server detects the risk of natural disasters or other abnormal situations, it quickly generates a response plan and notifies emergency services and government agencies. This helps to minimize damage.

[0616] Step 7:

[0617] The device provides users with information based on analysis results. Specifically, it notifies them of peak energy consumption times, safe travel routes, and local air quality information. Users can then adjust their daily activities based on this information.

[0618] Step 8:

[0619] The server aggregates data from across the city to conduct sustainability assessments. Based on this, it generates detailed reports on the city's environmental impact and resource utilization efficiency, which are then provided to government agencies and businesses.

[0620] (Example 1)

[0621] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] In today's world, where sustainable resource management and efficient operation are essential in cities, it is crucial to collect and process complex and intertwined data in real time and generate effective countermeasures. However, conventional systems struggle to appropriately utilize this data and quickly provide useful information to users. In addition, there is a need for rapid and effective responses to natural disasters and other emergencies.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0624] In this invention, the server includes means for collecting data from various sensors placed in urban areas, means for storing the collected data in a cloud environment, and means for detecting anomaly patterns using generative artificial intelligence based on the collected data and predicting future demand. This enables efficient and sustainable operation of cities.

[0625] An "urban area" refers to a region where people live in close proximity and where commercial activities and transportation are active.

[0626] A "sensor" is a device or apparatus that detects specific phenomena or environmental conditions and transmits that data to a measuring device or control system.

[0627] "Data" refers to information and events that are observed, measured, or acquired, expressed numerically or symbolically, and serves as the basis for analysis and decision-making.

[0628] A "cloud environment" is a platform that provides information technology resources via the internet, and is an online infrastructure for storing and processing data.

[0629] "Generative artificial intelligence" refers to a group of algorithms or technologies that automatically generate information based on input data and provide new insights and suggestions based on the analysis results.

[0630] An "anomalous pattern" refers to a data trend or characteristic that indicates a phenomenon or state that differs from what was predicted or the normal state.

[0631] "Demand forecasting" is the act of estimating future consumption and usage patterns based on the analysis of past and present data.

[0632] This invention provides a system that enables data collection, analysis, and information provision in urban areas. In this system, a server plays a primary role.

[0633] The server collects data from various sensors, such as traffic flow sensors, energy consumption monitoring devices, water usage meters, and air quality monitors. This data is temporarily stored in a cloud environment for later processing.

[0634] Based on the collected data, the server uses generative artificial intelligence to analyze it. For example, it combines traffic data with historical data to detect abnormal congestion patterns and predict peak times for each road. It can also use energy consumption data to forecast future consumption peaks and formulate optimal resource allocation plans.

[0635] This information is provided to the user through a device, such as a smartphone or personal computer. Based on the information provided, the user can take more effective actions. For example, they can receive notifications about times when energy prices are high and choose to conserve energy.

[0636] As a concrete example, the server predicts congestion levels during specific time periods based on traffic data and sends a plan to promote efficient traffic flow by adjusting the timing of traffic signals. A benefit for users is that they can adjust the timing of outdoor activities based on air quality information displayed on their devices.

[0637] An example of a prompt message would be: "Based on real-time traffic data for the city, predict the peak congestion times for the next 24 hours. Also, identify areas where congestion is particularly expected."

[0638] In this way, the present invention supports sustainable management in urban environments and enables the efficient use of resources.

[0639] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0640] Step 1:

[0641] The server collects data from various sensors placed within urban areas. Specifically, it receives data from traffic sensors, energy consumption monitors, water meters, and air quality monitors. This data includes, for example, traffic volume, energy consumption, water resource usage, and air pollution levels. The server standardizes the format of the received data and prepares it for storage in the cloud environment.

[0642] Step 2:

[0643] The server stores the collected data in a cloud environment. During this process, the data is organized chronologically and by location, and tagged in a way that facilitates searching. Raw data acquired from sensors is provided as input, and structured data stored in a cloud database is obtained as output.

[0644] Step 3:

[0645] The server uses generative artificial intelligence to analyze data stored in the cloud. The input is structured data retrieved from a cloud database, and the generative AI model detects anomaly patterns and forecasts demand. Through data analysis, it extracts congestion patterns on specific roads and predicts peak energy consumption times. A report of the analysis results is generated as output.

[0646] Step 4:

[0647] The server develops a resource optimization plan based on the analysis results. For example, it might plan to adjust traffic signal timing to minimize congestion or create a schedule to optimize energy supply. The input is a report of the analysis results, and the output is a specific optimization plan.

[0648] Step 5:

[0649] The server sends the optimization plan to the relevant control equipment and executes the instructions. A specific example is issuing instructions to a traffic signal control system to change the signal. The input is the optimization plan, and the output is the execution of the control instructions.

[0650] Step 6:

[0651] The terminal provides users with information based on analysis results and optimization plans. Through the terminal, users can receive information on peak energy consumption times and air quality, allowing them to adjust their daily activities. Input is information from the server, and output is notifications to the user.

[0652] This process supports the efficient and sustainable management of cities.

[0653] (Application Example 1)

[0654] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] In urban environments, it is crucial to simultaneously achieve efficient and sustainable resource use and environmental protection. However, the current situation is that rapid responses and optimization plans based on real-time data are not adequately presented, leading to inefficiencies in urban functions and increased environmental burden. It is necessary to address this challenge and support sustainable urban management by providing information that directly benefits residents' actions.

[0656] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0657] In this invention, the server includes means for collecting information from data acquisition mechanisms placed in the urban environment, means for performing anomaly detection and future prediction using generative artificial intelligence based on the collected information, and means for creating an optimized resource allocation plan based on the results of the anomaly detection and future prediction. This makes it possible for citizens to receive real-time data and receive advice to support the efficient use of resources and urban functions.

[0658] The term "urban environment" refers to the space in which humans live, where various factors such as transportation, energy, water resources, and air quality are intricately intertwined.

[0659] A "data acquisition mechanism" is a group of devices that collect information in real time in urban environments, such as traffic sensors, energy monitors, water meters, and air quality monitors.

[0660] "Generative artificial intelligence" is an advanced computer program used to identify patterns and relationships from large amounts of data, and to perform anomaly detection and future predictions.

[0661] "Anomaly detection" is the process of identifying unusual situations or changes in the urban environment in real time.

[0662] "Future forecasting" is the process of predicting future demand and changes based on collected data.

[0663] A "resource allocation optimization plan" is a plan to efficiently allocate resources such as transportation, energy, and water within a city to achieve maximum convenience and minimum environmental impact.

[0664] A "management mechanism" is a central system for implementing optimization plans and managing various issues in the urban environment.

[0665] "Citizen devices" refer to terminals such as smartphones and computers that residents use to receive real-time information about the city and to improve their daily lives.

[0666] "Real-time data" refers to information that is collected and analyzed instantaneously, reflecting the situation at that particular moment.

[0667] "Providing advice" means recommending appropriate actions and choices to citizens based on the data collected.

[0668] In this invention, a server, terminal, and user collaborate to build a system. The server is located in the cloud and collects information from various data acquisition mechanisms in the urban environment. This includes traffic sensors, energy monitors, water meters, air quality monitors, etc. The collected data is analyzed on the server using generative artificial intelligence. This analysis uses backend programming languages ​​such as Python and Django, and a cloud database from Google Cloud Platform. For the generative AI, for example, a model from OpenAI is applied.

[0669] The server analyzes the results to identify abnormal situations and forecast demand in the urban environment. Based on this information, an optimization plan for resource allocation is formulated and notified to the management organization. Information is also provided to citizens in real time through terminals. These terminals include smartphones and personal computers, and React Native is used for the user interface. The terminals present users with information on the city's resource usage and advice for efficiency, promoting environmental protection and resource conservation.

[0670] For example, when a terminal receives information about an area with very high traffic volume, a message is displayed to the user encouraging them to use public transport or choose an alternative route. This allows the user to take actions that support the efficiency of urban functions. Additionally, the generating AI model is input with a prompt message such as, "Based on the current traffic peak information for the specified area, suggest the optimal route and use of public transport for the user," and receives appropriate output.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The server collects information from various data acquisition mechanisms, such as traffic sensors, energy monitors, water meters, and air quality monitors. Input data includes various measurements of traffic volume, energy consumption, water usage, and air quality. The collected data is stored in a cloud database on the Google Cloud Platform, preparing it for subsequent analysis processes.

[0674] Step 2:

[0675] The server retrieves data stored in a cloud database and performs analysis using generative artificial intelligence. The input data is real-time information collected in Step 1. In this analysis process, programs using Python or Django process the data and perform anomaly detection and future prediction. Specifically, they extract outliers and perform future demand forecasts based on past trends. The output results are used to optimize resources.

[0676] Step 3:

[0677] The server formulates an optimization plan for resource allocation based on the analysis results output by the generative AI. The input is the analysis results from step 2, and based on this, an optimal traffic allocation and energy supply adjustment plan is formulated. The server sends this to the management mechanism and generates resource control messages to automate the necessary adjustments.

[0678] Step 4:

[0679] The terminal receives optimization plans and real-time information sent from the server and disseminates it to citizens. While input is notifications from the server, the terminal displays the information in a user-friendly interface using React Native. Users can utilize resources more efficiently by receiving advice on avoiding traffic congestion and suggestions for reducing energy consumption.

[0680] Step 5:

[0681] Users adjust their daily activities using their devices. Specific inputs are information and advice displayed on the device, and based on this, users choose actions that contribute to the efficiency of urban functions, such as using public transportation or changing car routes. The output includes reducing the burden on the urban environment and improving individual convenience.

[0682] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0683] This invention is a system that combines the collection and analysis of real-time data in urban environments with an emotion engine that recognizes user emotions, thereby supporting sustainable urban management and improving the user experience.

[0684] First, the server collects information on traffic, energy, water use, and air quality from various data acquisition devices placed throughout the city and stores it in a cloud database. Next, this data is analyzed in real time using generative artificial intelligence to detect abnormal situations in the urban environment, forecast future demand, and formulate optimal resource allocation plans. This analysis makes it possible to alleviate traffic congestion and optimize energy consumption.

[0685] The device plays a crucial role in providing information to the user. Specifically, it notifies the user of peak energy consumption times and air quality levels, and provides advice to promote environmental protection and resource conservation in daily life. It also uses an emotion engine to recognize the user's emotions and dynamically adjusts the content and format of the information provided based on that information. This operation makes it possible to provide a comfortable user experience, such as avoiding information overload when the user is feeling stressed.

[0686] For example, when a user receives a notification about energy consumption via their smartphone, if the emotion engine detects an "anxious" state, the application can provide concise information along with positive feedback and encouraging messages.

[0687] In addition, the server accumulates data based on feedback from the emotion engine to improve urban management policies and notification content, thereby contributing to increased operational efficiency throughout the city. In this way, this system, which combines emotion recognition, helps to realize sustainable urban development with the cooperation of residents.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The server collects data in real time from various devices installed in the urban environment, such as traffic sensors, energy meters, water meters, and air quality monitors. This data is immediately integrated into a cloud database after collection.

[0691] Step 2:

[0692] The server centrally manages the collected data and performs analysis using generative artificial intelligence. This analysis includes anomaly detection, traffic pattern prediction, and energy demand forecasting. Based on the analysis results, an optimization plan for resource allocation is formulated.

[0693] Step 3:

[0694] The terminal receives analysis results sent from the server and provides information to the user. This information includes advice on improving energy efficiency and reports on the current state of air quality.

[0695] Step 4:

[0696] The device uses its built-in emotion engine to analyze the user's emotions in real time. This allows it to understand the user's feedback and emotional state, and fine-tune how information is delivered.

[0697] Step 5:

[0698] The server analyzes user emotion data obtained from the emotion engine and uses it to improve notification content and user approaches. For example, if a user is feeling stressed, the information provided will be simplified and encouraging messages will be added.

[0699] Step 6:

[0700] Users can adjust their daily behaviors appropriately based on analytical information and personalized advice provided by their devices. This allows them to actively participate in environmental protection and the efficient use of resources.

[0701] Step 7:

[0702] The server will aggregate all feedback data and complete the process of using it to improve city management and policies. The data will be regularly evaluated to help improve the city's sustainability and the quality of life for its residents.

[0703] (Example 2)

[0704] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0705] In modern urban environments, various urban problems such as traffic congestion, inefficient energy use, and environmental pollution are increasing. Under these circumstances, achieving sustainable urban management and improving the lives of residents requires real-time decision-making utilizing city-wide data, along with information provision that takes residents' feelings into consideration. However, current systems are not adequately addressing these challenges.

[0706] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0707] In this invention, the server includes means for collecting data from observation devices placed in the city, means for detecting anomalies and predicting future demand using a generative model based on the collected data, and means for evaluating the emotional state of users using an emotion recognition engine and adjusting the information provided based on those emotions. This enables sustainable operation by combining real-time data analysis and emotion recognition of the city.

[0708] "Observation equipment" refers to devices used to collect diverse data in urban environments, such as traffic flow, energy consumption, weather, and air quality.

[0709] A "generative model" is an artificial intelligence technology used in data analysis to perform anomaly detection and future predictions based on collected data.

[0710] Anomaly detection is the process of identifying phenomena or events that deviate from normal patterns.

[0711] "Predicting future demand" is a method of predicting future trends and needs by analyzing past and present data.

[0712] A "resource optimization plan" is a strategic plan for the efficient allocation of resources such as transportation, energy, and water in a city.

[0713] A "control device" refers to the various devices and systems that receive and execute the generated optimization plan.

[0714] "User devices" refer to terminals that allow individual residents to receive information and provide feedback on urban management.

[0715] An "emotion recognition engine" is a technology that detects the emotional state of a user and adjusts the way information is provided based on that state.

[0716] "Feedback" refers to information collected from users, such as responses and opinions, that is used to improve the system.

[0717] This system aims for sustainable urban management by combining real-time data analysis and emotion recognition in urban environments. The implementation details are described below.

[0718] The server collects data from observation devices placed throughout the city. These include sensors that monitor traffic flow, meters that measure energy use, and sensors that monitor air quality and water usage. The data obtained from these observation devices is stored in a cloud database in real time. Based on this data, the server uses generative AI models to perform anomaly detection and future predictions. Through the analysis of data patterns, the generative AI models enable, for example, predictions of traffic congestion and energy demand.

[0719] The device plays a crucial role in providing information to the user. This includes, for example, sending notifications when energy demand is at its peak or when air quality issues occur. The device is equipped with an emotion recognition engine that uses the camera and microphone to determine the user's emotional state. Based on this, the device can adjust the content and format of the information to match the user's emotions, providing the most appropriate information for the user.

[0720] As a concrete example, when a user receives an app notification on their smartphone, if the emotion recognition engine detects an "anxious state," the device will simplify the notification content and add a positive message. In this way, it becomes possible to provide information that is less likely to cause stress to the user.

[0721] Furthermore, the server contributes to improving urban management policies by accumulating user feedback and sentiment data. For example, if it is found that residents experience high levels of anxiety during a specific time period, the notification methods and content for that time period can be reviewed. This makes it possible to improve the overall operational efficiency of the city.

[0722] An example of a prompt to input into a generative AI model could be: "Please tell me about specific methods for urban management that utilize emotion recognition, and how to improve feedback to residents based on those methods."

[0723] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0724] Step 1:

[0725] The server collects real-time data from observation devices installed in the city. Specifically, it obtains vehicle count and speed data from traffic sensors, consumption data from energy meters, and environmental data such as PM2.5 and CO2 concentration from air quality sensors. This data is stored in a cloud database and forms the basis for analysis. The input is various data from observation devices, and the output is the centralization of this data and its storage in the database.

[0726] Step 2:

[0727] The server passes data stored in a cloud database to a generative AI model for anomaly detection and future prediction. The generative AI model learns anomaly patterns from historical data and analyzes real-time data. This allows it to predict traffic congestion and peak energy demand. The input is raw data stored in a cloud database, and the output is anomaly detection information and demand forecasts as analysis results.

[0728] Step 3:

[0729] The server formulates an optimal resource allocation plan based on the analysis results from the generated AI model. This process includes route suggestions to optimize traffic flow and the development of energy-saving measures. Inputs include anomaly detection information and demand forecasts, while the output is a specific proposal for the optimal allocation plan.

[0730] Step 4:

[0731] The terminal notifies the user of information based on an optimal allocation plan sent from the server. Here, an emotion recognition engine is used to analyze the user's emotional state and adjust the notification content accordingly. For example, when the user is feeling stressed, a concise message with reassuring content is provided. The inputs are the allocation plan from the server and emotion data from the emotion recognition engine, while the output is the adjusted information notification.

[0732] Step 5:

[0733] Users receive information notifications from their devices. If necessary, users input their status and feedback into their devices and send it to the server. The server then uses the collected feedback to improve future strategies. Input consists of information notifications from the device and user feedback, while output is feedback data sent to the server.

[0734] (Application Example 2)

[0735] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] Current urban environments face problems such as traffic congestion, inefficient energy consumption, and deteriorating air quality. Furthermore, residents often experience stress due to information overload. Conventional systems fail to not only collect and analyze this environmental data in real time, but also to provide information tailored to users' emotions, thereby failing to improve residents' living environments. Therefore, there is a need to develop a system that not only optimizes the urban environment but also provides information tailored to users' emotional states, offering a comfortable user experience while supporting sustainable urban management.

[0737] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0738] In this invention, the server includes means for collecting information from various data acquisition devices placed in urban spaces, means for performing anomaly detection and future prediction using generative artificial intelligence based on the collected information, and means for analyzing the emotional state of users and dynamically adjusting the content and format of the information provided based on that emotional state. This makes it possible not only to analyze data from the urban environment to detect anomalies and predict future demand, but also to realize a comfortable living environment through the provision of information that responds to the emotions of users, while simultaneously supporting sustainable urban management.

[0739] "Various data acquisition devices placed in urban spaces" refers to a general term for multiple sensors and devices installed within a city to collect information such as traffic, energy consumption, water use, and air quality.

[0740] "Generative artificial intelligence" is an artificial intelligence technology that learns patterns based on accumulated data to detect anomalies and predict the future.

[0741] "Means for anomaly detection and future prediction" refers to a process that analyzes urban environment data to detect current anomalies and predict future trends based on that data.

[0742] "Means for creating an optimized resource allocation plan" refers to a method for formulating a plan to efficiently and effectively allocate the available resources of a city based on the results of anomaly detection and future prediction.

[0743] "Means characterized by notifying control equipment" refers to a method of transmitting the created resource allocation optimization plan as commands or information to a management system or control device.

[0744] "User devices" refer to terminals used to provide information to people living in cities, such as smartphones and computers.

[0745] "Means for analyzing a user's emotional state and dynamically adjusting the content and format of information provided based on that emotional state" refers to using an emotion recognition engine to determine the emotions of individual users in real time and changing the type and method of information provided accordingly.

[0746] To implement this invention, a server first collects information from various data acquisition devices placed in urban spaces. This includes data such as traffic conditions, energy consumption, water usage, and air quality. This data is collected through sensors and IoT devices and integrated and managed by the server. The server inputs the collected data into generative artificial intelligence in real time to perform anomaly detection and future prediction. The generative artificial intelligence uses pattern recognition technology to analyze the data and predict signs of abnormal situations and future resource demands.

[0747] Based on this analysis, the server automatically generates an optimized resource allocation plan. This plan details how to effectively distribute resources within the city and is linked to the planning decision system. This plan is also communicated to control devices, enabling specific operations and adjustments.

[0748] Residents receive information from the server through their user devices (e.g., smartphones or tablets). The devices display notifications to encourage environmental protection and resource conservation. Furthermore, the user's emotional state is analyzed by an emotion recognition engine via the user device's camera and microphone. For example, if a user is feeling stressed, the device simplifies the presentation of information and delivers a positive, encouraging message.

[0749] As a concrete example of the present invention, consider a scenario where a user receives an air quality notification on their smartphone on a sunny day. If the air quality is poor, the emotion engine recognizes that the user has an anxious expression. At this time, the device suggests moving to a nearby park and sends a reassuring message to support the user in making a comfortable choice.

[0750] An example of a prompt message would be, "Generate the most appropriate notification message based on the user's current emotional state and urban environment data." By inputting this into the AI ​​model, the most relevant information will be provided.

[0751] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0752] Step 1:

[0753] The server collects information on traffic, energy consumption, water use, and air quality from data acquisition devices placed throughout the urban space. This information is acquired in real time from various sensors and aggregated on the server as a dataset representing the state of the urban environment. This data serves as foundational data for subsequent analysis.

[0754] Step 2:

[0755] The server inputs the collected data into generative artificial intelligence to detect anomalies and make future predictions. Based on the input data, the AI ​​applies pattern recognition and machine learning algorithms to identify the occurrence of anomalies and make precise predictions about future resource demands. The calculated anomaly detection results and prediction data form the basis of the resource allocation plan.

[0756] Step 3:

[0757] The server creates an optimized resource allocation plan based on anomaly detection and future prediction results. Using the analysis results as input, it calculates efficient resource utilization methods and develops specific plans to optimize the allocation of different urban resources. The generated plans are output as commands to control equipment or as adjustable suggestions.

[0758] Step 4:

[0759] The terminal receives information from the server through the user's device and provides it to the user. This information is provided as notifications, such as peak energy consumption times and air quality levels, encouraging improvements to the living environment and a review of behavioral changes. Based on the received data, the terminal determines what information should be displayed and provides notifications in the most beneficial format for the user.

[0760] Step 5:

[0761] The device uses its camera and microphone to analyze the user's emotional state in real time. An emotion recognition engine analyzes the user's emotions from the input video and audio data to determine if they are experiencing stress. The analysis results are used to adjust the information presented.

[0762] Step 6:

[0763] The device dynamically adjusts the content and format of information provided based on the results of emotional state analysis. Specifically, if the user indicates stress, the information is simplified and a positive message is created and delivered. This allows the user to receive information in the most optimal state.

[0764] Step 7:

[0765] As an example of a prompt, the AI ​​model is input with the message, "Generate the optimal notification message based on the user's current emotional state and urban environment data," and generates appropriate notification content for each user. The generated notification message is presented in the most suitable format for the user.

[0766] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0767] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0768] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0769] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0770] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0771] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0772] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0773] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0774] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0775] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0776] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0777] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0778] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0779] 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.

[0780] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0781] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0782] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0783] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0784] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0785] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0786] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] A means characterized by collecting information from various data acquisition devices placed in an urban environment,

[0790] A method characterized by performing anomaly detection and future prediction using generative artificial intelligence based on collected information,

[0791] A means characterized by creating an optimized resource allocation plan based on the results of the anomaly detection and future prediction,

[0792] A means characterized by notifying the management device of the optimization plan for resource allocation,

[0793] A means characterized by providing information to resident devices and promoting environmental protection and resource conservation behaviors,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, characterized by sensing the risks of natural disasters and abnormal situations and generating a rapid response plan to those risks.

[0797] (Claim 3)

[0798] The system according to claim 1, characterized by evaluating the environmental impact and resource utilization status of a city and preparing a detailed report.

[0799] "Example 1"

[0800] (Claim 1)

[0801] A means of collecting data from various sensors placed in urban areas,

[0802] A means of storing the collected data in a cloud environment,

[0803] Based on the collected data, a means of detecting anomaly patterns using generative artificial intelligence and predicting future demand,

[0804] A means for formulating a resource efficiency plan based on the detection of the aforementioned abnormal patterns and the results of demand forecasting,

[0805] Means for notifying control equipment of the resource efficiency plan,

[0806] A means of providing information to users via a terminal and promoting environmental conservation and efficient use of resources,

[0807] A means of promptly generating response measures and notifying relevant organizations when a natural disaster or exceptional situation is detected,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, characterized by evaluating the environmental impact and resource allocation status of urban areas and preparing a detailed analytical report.

[0811] (Claim 3)

[0812] The system according to claim 1, which provides information for optimizing user behavior based on the results of the analysis of the aforementioned data.

[0813] "Application Example 1"

[0814] (Claim 1)

[0815] A means of collecting information from data acquisition mechanisms placed in the urban environment,

[0816] A means of performing anomaly detection and future prediction using generative artificial intelligence based on collected information,

[0817] A means for creating an optimized resource allocation plan based on the results of the anomaly detection and future prediction,

[0818] Means for notifying the management organization of the aforementioned optimization plan,

[0819] A means of providing information to public devices and promoting environmental protection and resource conservation actions,

[0820] A means for citizens to receive real-time data and receive advice to support the efficient use of resources and urban functions,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, which detects the risk of natural disasters and abnormal situations, generates a rapid response plan to such risks, and notifies citizens of it.

[0824] (Claim 3)

[0825] The system according to claim 1, which evaluates the environmental impact and resource utilization status of a city, prepares a detailed report, and provides citizens with analysis results for improving resource utilization efficiency using generative artificial intelligence.

[0826] "Example 2 of combining an emotion engine"

[0827] (Claim 1)

[0828] A means of collecting data from observation devices placed in the city,

[0829] A means of detecting anomalies and predicting future demand using a generative model based on collected data,

[0830] A means for formulating an optimal resource allocation plan based on the results of the aforementioned anomaly detection and demand forecasting,

[0831] Means for notifying the control device of the optimization plan,

[0832] A means of providing information to user devices and promoting environmental protection and resource conservation actions,

[0833] A means for evaluating the user's emotional state using an emotion recognition engine and adjusting the information provided based on that emotion,

[0834] A means of improving policies by accumulating user feedback and sentiment data,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, characterized by sensing the risks of natural disasters and abnormal situations and rapidly generating plans to respond to such risks.

[0838] (Claim 3)

[0839] The system according to claim 1, characterized by evaluating the environmental impact and resource use of a city and preparing a detailed report.

[0840] "Application example 2 when combining with an emotional engine"

[0841] (Claim 1)

[0842] A means characterized by collecting information from various data acquisition devices placed in urban spaces,

[0843] A method characterized by performing anomaly detection and future prediction using generative artificial intelligence based on collected information,

[0844] A means characterized by creating an optimized resource allocation plan based on the results of the anomaly detection and future prediction,

[0845] A means characterized by notifying a control device of the optimization plan for resource allocation,

[0846] A means characterized by providing information to user devices and promoting environmental protection and resource conservation behaviors,

[0847] A means characterized by analyzing the user's emotional state and dynamically adjusting the content and format of the information provided based on that emotional state,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, characterized by sensing the risks of natural disasters and abnormal situations and generating a rapid response plan to those risks.

[0851] (Claim 3)

[0852] The system according to claim 1, characterized by evaluating the environmental impact and resource use of a city and preparing a detailed report. [Explanation of Symbols]

[0853] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information from data acquisition mechanisms placed in the urban environment, A means of performing anomaly detection and future prediction using generative artificial intelligence based on collected information, A means for creating an optimized resource allocation plan based on the results of the anomaly detection and future prediction, Means for notifying the management organization of the aforementioned optimization plan, A means of providing information to public devices and promoting environmental protection and resource conservation actions, A means for citizens to receive real-time data and receive advice to support the efficient use of resources and urban functions, A system that includes this.

2. The system according to claim 1, which detects the risk of natural disasters and abnormal situations, generates a rapid response plan to such risks, and notifies citizens of it.

3. The system according to claim 1, which evaluates the environmental impact and resource utilization status of a city, prepares a detailed report, and provides citizens with analysis results for improving resource utilization efficiency using generative artificial intelligence.

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