system

Abstract

We provide the system. [Solution] Data acquisition methods for collecting data related to transportation, energy, environment, and disasters, A data processing means that preprocesses the acquired data and converts it into an analyzable format, An artificial intelligence processing means analyzes the aforementioned preprocessed data to predict traffic congestion, predict energy demand, and evaluate environmental conditions. Based on the analysis results obtained by the artificial intelligence processing means, a proposal generation means generates improvement suggestions for system users, A system including a notification means for notifying the user's terminal of the aforementioned proposal.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 cities, with the concentration of population, multiple problems such as traffic congestion, increased energy consumption, environmental pollution, and natural disasters have occurred. With conventional methods, it is difficult to manage and analyze these problems integratively and make appropriate judgments. Therefore, it is difficult to operate cities sustainably, and in particular, improvement in the quality of life of residents and prompt disaster response are required.

Means for Solving the Problems

[0005] This invention provides data acquisition means for collecting data related to traffic, energy, environment, and disasters. This makes it possible to collect a wide variety of data in cities in real time. Furthermore, it provides data processing means for preprocessing the acquired data and converting it into an analyzable format. This data is analyzed by artificial intelligence processing means to predict traffic congestion, predict energy demand, and evaluate environmental conditions. It also has proposal generation means that generates improvement suggestions based on the analysis results and notifies system users of this information. Through this series of processes, it enables efficient urban management, improvement of residents' lives, and rapid disaster response.

[0006] "Data acquisition means" refers to methods or devices for collecting a wide variety of information related to traffic, energy, environment, and disasters in urban areas.

[0007] "Data processing means" refers to a method or apparatus for pre-processing acquired data to convert it into an analyzable format.

[0008] "Artificial intelligence processing means" refers to technologies that use pre-processed data to predict traffic congestion, forecast energy demand, and evaluate environmental conditions.

[0009] "Proposal generation means" refers to a method or apparatus for generating improvement suggestions for system users based on analysis results.

[0010] "Notification means" refers to a method or device for transmitting generated suggestions to the user's terminal. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] 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.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention aims to collect, process, and analyze a wide variety of information related to urban traffic, energy, environment, and disasters in real time, and to propose and notify appropriate improvement measures. This system consists of the interaction of a server, terminals, and users.

[0033] Data acquisition and preprocessing

[0034] The server continuously acquires data from sensors, cameras, GPS devices, energy meters, and environmental sensors installed throughout the city. This allows for a comprehensive understanding of traffic flow, energy consumption, air quality, weather conditions, and earthquake information. After data acquisition, the server performs preprocessing such as noise reduction and missing value imputation to format the data into an analyzable format.

[0035] Data Analysis

[0036] The server inputs pre-processed data into an artificial intelligence (AI) processing system to analyze traffic congestion predictions and fluctuations in energy demand. The AI ​​learns from past data to predict future trends and detect anomalies. This makes it possible to take effective measures before problems become apparent.

[0037] Proposal generation and notification

[0038] Based on the analysis results, the server uses a proposal generation mechanism to generate suggestions such as traffic signal optimization, public transport operation adjustments, energy consumption reduction measures, and environmental improvement measures. The generated suggestions are then notified to system users via their terminals.

[0039] As a concrete example, the server analyzes traffic sensor data during rush hour and, if congestion is predicted at a specific intersection, proposes and implements adjustments to traffic signal timing. Furthermore, when peak energy consumption is predicted, it implements controls to promote the use of renewable energy. In the event of a disaster, the server quickly processes emergency information and notifies users of evacuation information on their devices. Users receive the notification and can take appropriate action.

[0040] Thus, this invention is designed to solve the complex challenges of cities and contribute to the realization of sustainable cities.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects data from sensors, cameras, GPS devices, energy meters, and environmental sensors within the city. This includes traffic flow speed and location, energy consumption, air quality hazardous substance concentrations, noise levels, weather data, and seismic information.

[0044] Step 2:

[0045] The server performs noise reduction and missing value imputation on the collected data, and formats the data into a unified format. This preprocessing reduces data uncertainty and prepares the data for analysis.

[0046] Step 3:

[0047] The server passes pre-processed data to an artificial intelligence (AI) processing system for real-time analysis. The AI ​​predicts traffic congestion, analyzes fluctuations in energy demand, and assesses environmental conditions. For traffic flow, it refines congestion predictions through comparison with historical data, and for energy demand, it suggests the possibility of rapid demand shifts.

[0048] Step 4:

[0049] The server uses a proposal generation mechanism to generate optimization proposals based on the analysis results. These generated proposals include signal timing adjustments, changes to the operation schedule, and plans for optimizing energy use.

[0050] Step 5:

[0051] The server notifies the terminal of the generated suggestions and provides the system user with suggestions for necessary actions. The terminal receives this and provides the user with alerts and detailed instructions. Based on this, the user can decide on specific actions and take them.

[0052] (Example 1)

[0053] 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."

[0054] In today's society, where urban traffic congestion, energy efficiency, environmental protection, and rapid response to disasters are all demanded, there is a need to grasp these problems in real time and propose optimal solutions. However, existing technologies have lacked a system that can comprehensively and integrally analyze these areas and quickly generate proposals.

[0055] 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.

[0056] In this invention, the server includes information acquisition means for continuously collecting data from urban sensors and location measuring devices; data formatting means for denoising and imputing missing values ​​in the acquired data and converting it into an analyzable data format; and artificial intelligence analysis means for using the formatted data as input to predict traffic, predict fluctuations in energy demand, and detect anomalies based on learning from past data. This enables real-time analysis of complex urban problems and the rapid proposal and notification of specific and actionable improvement measures.

[0057] "Information acquisition means" refers to a device or system that continuously collects data from sensors and location measuring devices placed within a city.

[0058] A "data formatting means" is a device or system that processes collected data to remove noise and impute missing values, thereby preparing it into an analyzable data format.

[0059] "Artificial intelligence analysis means" refers to technology that uses artificial intelligence to learn from past data and perform traffic forecasting, energy demand fluctuation forecasting, and anomaly detection.

[0060] A "proposal formulation tool" is a device or system that generates improvement proposals, such as adjusting the timing of traffic signals or reducing energy consumption, based on the results of artificial intelligence analysis.

[0061] "Notification provision means" refers to a technology or system that transmits generated proposals to the user's communication device and promptly notifies the user of necessary information.

[0062] This invention is a system for comprehensive management of traffic, energy, environment, and disaster in urban areas. The server acquires data through various sensors and location measurement devices placed throughout the city. Hardware used includes traffic cameras, GPS receivers, and environmental monitoring sensors. The software includes interfaces for collecting, preprocessing, and providing this data.

[0063] The server performs data formatting on the collected data, removing noise and imputing missing values. The pre-processed data is then input into an artificial intelligence (AI) analysis system. This AI uses a generative AI model, learning from historical data to predict traffic patterns, energy demand fluctuations, and anomalies.

[0064] For example, a server might analyze data collected from traffic sensors during the morning rush hour to predict the likelihood of congestion at a particular intersection. In this case, the server could set up suggestions to change the timing of traffic lights and notify the user's terminal, providing immediate action. Furthermore, if increased energy consumption is predicted, measures such as promoting the use of renewable energy could be implemented.

[0065] As an example of a prompt, the AI ​​model can be instructed to "use data from urban traffic sensors to predict traffic congestion and propose effective improvement measures." This allows the server to perform appropriate data analysis and suggest improvements.

[0066] This system is designed to support efficient and sustainable urban life through close cooperation between servers, terminals, and users.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The server continuously collects data from various sensors and location measurement devices within the city. Inputs include data from traffic cameras, GPS devices, and environmental monitoring sensors. The server acquires this data and stores it in storage. During this process, accurate timestamps and location information are added to the data.

[0070] Step 2:

[0071] The server performs data formatting on the collected raw data, including noise reduction and missing value imputation. The input is the raw data obtained in step 1. The server applies a noise filtering algorithm to eliminate outliers. Next, a missing value imputation algorithm estimates and fills in the missing data, outputting data in an analyzable format.

[0072] Step 3:

[0073] The server inputs the formatted data into a generating AI model for data analysis. The input is pre-processed data. Using the artificial intelligence model, the server performs traffic forecasting and energy demand forecasting, as well as anomaly detection, based on historical data. The output is a forecast of the likelihood of future traffic congestion and fluctuations in energy consumption, providing insights for improvement.

[0074] Step 4:

[0075] Based on the AI ​​analysis results, the server generates specific improvement measures using a proposal formulation method. This system formulates measures such as adjusting traffic signals and reducing energy consumption. Based on the prediction results as input, the server outputs measures to adjust signal timing to alleviate congestion and optimize the energy supply and demand balance.

[0076] Step 5:

[0077] The terminal receives suggestions generated from the server and notifies the user. The input is suggestion data from the server. The terminal visualizes these suggestions using an appropriate user interface and outputs them in a format that the user can understand. This allows the user to choose an appropriate action based on the information.

[0078] (Application Example 1)

[0079] 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."

[0080] Modern cities face challenges such as traffic congestion, inefficient energy consumption, environmental degradation, and the need for rapid and effective responses to natural disasters. These issues should not be managed individually but rather considered holistically. However, traditional systems are often fragmented, limiting overall optimization. Furthermore, there is a lack of information to support residents' daily lives.

[0081] 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.

[0082] In this invention, the server includes information acquisition means, information processing means, artificial intelligence processing means, proposal generation means, notification means, and optimization means. This enables the integrated collection and analysis of information related to traffic, energy, environment, and disasters in cities, and allows for optimal urban management that supports the daily activities of residents.

[0083] "Information acquisition means" refers to functions for effectively gathering diverse information related to transportation, energy, the environment, and disasters.

[0084] "Information processing means" refers to methods for formatting acquired information into an analyzable format, removing noise, and imputing missing values.

[0085] "Artificial intelligence processing means" refers to analytical functions that utilize machine learning and deep learning to predict traffic congestion, assess energy demand trends, and evaluate environmental conditions based on pre-processed information.

[0086] The "proposal generation method" is a process that generates improvement measures, such as proposals for optimizing traffic signals or reducing energy consumption, based on the analysis results obtained by the artificial intelligence processing method.

[0087] A "notification means" is a communication system for quickly transmitting generated proposals and analysis results to the user's information device.

[0088] "Optimization methods" refer to techniques that most effectively utilize various proposals related to urban management, such as transportation and energy, in order to support the behavior of residents.

[0089] In order to implement this invention, it is necessary to construct a system equipped with information acquisition means, information processing means, artificial intelligence processing means, proposal generation means, notification means, and optimization means.

[0090] The server collects real-time information related to traffic, energy, the environment, and disasters from various sensors, cameras, GPS devices, energy meters, and environmental sensors installed in the city. This allows for comprehensive monitoring of traffic flow, energy consumption, air quality, weather, earthquakes, and more. The collected information is preprocessed to remove noise and impute missing values, preparing it for analysis. This process uses languages ​​such as Python to clean the data.

[0091] The pre-processed information is input into an artificial intelligence processing system on the server. Here, machine learning libraries such as TENSORFLOW® and PyTorch are used to build predictive models based on historical data, and to predict traffic congestion, energy demand fluctuations, and environmental conditions. Based on the analysis results, the proposal generation system generates improvement proposals such as optimizing traffic signals, reducing energy consumption, and adjusting public infrastructure.

[0092] The generated suggestions are quickly notified to the user's information terminal via a notification system. These notifications are sent using services such as Firebase Cloud Messaging. Furthermore, optimization measures are implemented to optimize the suggestions regarding transportation and energy, thereby supporting residents' actions and enabling more efficient use of these suggestions.

[0093] For example, if a traffic sensor records high traffic volume at 8:00 AM on a particular day, the server analyzes this information and generates improvement measures to adjust the timing of traffic lights at a specific intersection. Similarly, if energy consumption is expected to peak, a notification is sent to encourage the use of renewable energy. In this way, residents receive optimized information on a daily basis, enabling them to efficiently adjust their lives.

[0094] Examples of input prompts for the generating AI model include, "Please tell me the current traffic situation and the forecast for the next hour," and "I would like to know the results of the analysis of today's energy consumption trends." By using these prompts, the system can provide residents with appropriate and timely information based on the complex data of the city.

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] The server acquires various data in real time from sensors, cameras, GPS devices, energy meters, and environmental sensors installed in the city. The input consists of various sensor data, which is then taken into the server. The server's operation involves acquiring data using communication protocols. The output is raw sensor data.

[0098] Step 2:

[0099] The server preprocesses the acquired raw sensor data. The input is the acquired raw data, and this processing includes noise reduction and missing value imputation. The server performs data cleaning using a Python script. The output is in an analyzable, clean data format.

[0100] Step 3:

[0101] The server passes pre-processed clean data to an artificial intelligence processing system. The input is clean data, and the server uses tools such as TensorFlow or PyTorch to analyze this data. Machine learning models are used to predict traffic, predict energy demand, and assess environmental conditions. The output is the analysis results from the predictive model.

[0102] Step 4:

[0103] The server generates improvement measures using a proposal generation means based on the analysis results obtained by the artificial intelligence processing means. The input is the analysis results, and the proposal generation means uses an algorithm to form traffic signal optimization and energy reduction proposals. The output is the generated improvement proposal.

[0104] Step 5:

[0105] The server sends improvement suggestions to the user's device via a notification system. The input is the improvement suggestion, and the server uses Firebase Cloud Messaging to deliver the notification. The output is the display of the notification on the user's device.

[0106] Step 6:

[0107] The user receives notifications displayed on their device and optimizes their daily activities. The input is the notifications sent to the device, which the user uses to make appropriate lifestyle adjustments, such as adjusting travel time or changing energy usage. The output is the optimized daily activities.

[0108] 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.

[0109] This invention is a system that collects information related to traffic, energy, the environment, and disasters, and further combines it with an emotion engine that recognizes user emotions to provide more effective improvement suggestions. Through the coordinated operation of the server, terminal, and user, the system analyzes this information, generates appropriate suggestions, and notifies users.

[0110] Data acquisition and preprocessing

[0111] The server collects data from various devices within the city. This data includes traffic flow, energy consumption, environmental conditions, weather data, and earthquake information. The server preprocesses this data to make it analyzable. Preprocessing includes denoising and formatting the data to improve its accuracy.

[0112] Emotion recognition by an emotion engine

[0113] The device also collects data about the user. In particular, the emotion engine analyzes user feedback, operation history, and specific input data to infer the user's emotional state. This emotion data is also aggregated on a server and used as material for analysis.

[0114] Data Analysis

[0115] The server performs analysis using pre-processed data and sentiment data. Artificial intelligence processing makes predictions about traffic flow and energy demand, and conducts environmental assessments. Information from the sentiment engine is used to customize suggestions according to the user's emotional state.

[0116] Proposal generation and notification

[0117] Based on the analysis results, the server uses a proposal generation mechanism to create improvement suggestions, including signal control, public transport operation adjustments, and energy utilization optimization. An emotion engine selects a communication method appropriate to the user's emotional state, and notifications are sent to the user via the terminal. For example, if the user is feeling stressed, a gentler notification method is selected.

[0118] As a concrete example, if traffic congestion is expected during the morning commute, the server will suggest optimizing traffic signals and provide appropriate notifications to motivated users. If the system determines that a user is experiencing stress, it will only provide more important information in a calm tone. In this way, the system can guide users to take appropriate actions while reducing their psychological burden.

[0119] Thus, this invention is designed to support the efficient management of cities and the comfortable lives of their residents.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server collects information from various points in the city, including traffic sensors, energy meters, environmental sensors, and weather station data. This ensures comprehensive coverage of traffic flow, energy consumption, air quality, noise levels, and weather data.

[0123] Step 2:

[0124] The server preprocesses the collected data to prepare it for analysis. By removing noise and imputing missing values, it generates a reliable dataset.

[0125] Step 3:

[0126] The device sends user operation history and feedback data to the emotion engine. The emotion engine uses this information to identify the user's current emotional state. This process uses natural language processing and pattern recognition techniques to evaluate emotions with high accuracy.

[0127] Step 4:

[0128] The server compares pre-processed data with emotional data obtained from the emotion engine and performs analysis using artificial intelligence processing. This analysis is used to predict traffic congestion, analyze energy demand, and assess environmental risks, and to formulate optimal suggestions tailored to the user's emotions.

[0129] Step 5:

[0130] Based on the analysis results, the server uses a proposal generation mechanism to assemble specific improvement suggestions. These include supporting the optimization of traffic signals, adjusting energy use plans, and recommending environmental policies.

[0131] Step 6:

[0132] The server forwards the generated suggestions to the terminal and sends notifications in an appropriate manner. The user's emotional state is taken into consideration; for example, if the user is stressed, the notification will be sent in a gentler tone.

[0133] Step 7:

[0134] Users receive notifications through their devices and take the suggested actions. User feedback is then sent back from the device to the server and used to improve the system's accuracy.

[0135] (Example 2)

[0136] 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".

[0137] In today's urban environment, traffic congestion, wasteful energy consumption, and environmental deterioration are progressing, resulting in a decline in the quality of life for residents. Furthermore, conventional information systems often fail to consider user emotions when providing notifications and suggestions, potentially causing psychological burden on users. The challenge lies in solving these problems and building systems that support efficient urban management and a comfortable life for residents.

[0138] 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.

[0139] In this invention, the server includes information acquisition means for collecting data, information processing means for preprocessing the acquired data and converting it into an analyzable format, and artificial intelligence analysis means for analyzing the preprocessed data to predict traffic flow, calculate energy demand, and evaluate environmental conditions. This makes it possible to improve the efficiency of traffic management, optimize energy consumption, and monitor the environment. Furthermore, by incorporating emotion recognition means for identifying the user's emotional state, suggestions to the user can be made more personalized, reducing psychological burden.

[0140] "Information acquisition means" refers to technical means for collecting information related to traffic, energy, environment, and disasters using various sensors and devices.

[0141] "Information processing means" refers to the technical process of preparing collected raw data into a format suitable for analysis by performing noise reduction and format conversion.

[0142] "Artificial intelligence analysis methods" refer to technologies that apply algorithms and machine learning models to predict traffic flow and energy consumption based on pre-processed data, and to evaluate environmental conditions.

[0143] "Emotion recognition means" refers to technologies that analyze user feedback, operation history, and facial expression data to identify the user's emotional state.

[0144] The "proposal generation method" is a technology that generates improvement suggestions for system users based on the results obtained from artificial intelligence analysis methods and emotion recognition methods.

[0145] "Notification means" refers to technology for transmitting generated improvement suggestions to the user's device and providing information at an appropriate time and in an appropriate manner.

[0146] This system effectively collects and analyzes information related to traffic, energy, environment, and disasters in urban environments, and provides users with appropriate improvement suggestions. The server utilizes sensors and related devices installed in the city to collect various types of data. Specific hardware used includes traffic flow sensors and energy monitors. These devices collect information in a database in real time.

[0147] The device analyzes user feedback and operation history using an emotion engine to infer the user's emotional state. The emotion engine processes the user's text input and facial expression data to understand changes in emotion. This information is transmitted to a server via the internet and used as material for analysis along with other data.

[0148] The server uses preprocessing modules to clean up raw data and format it into an analyzable format. Then, it uses "AIAnalyticsEngineV5.0" to predict traffic flow, energy consumption, and environmental conditions. This enables highly accurate, data-driven predictions through artificial intelligence analysis.

[0149] The proposal generation method uses analysis results and sentiment data to generate improvement suggestions optimized for the user. For example, if traffic congestion is expected, the server proposes adjustments to signal control and transmits the instructions generated by "ProposalGenerator3000" to the terminal.

[0150] As a concrete example, the server uses traffic data from the morning commute to suggest traffic signal optimizations. Furthermore, a generative AI model is used to generate a prompt message that reads, "Please generate a notification message suggesting the optimal commute route based on this morning's traffic data and the user's sentiment."

[0151] Users review suggestions received through their devices and use them to guide their actions. The devices utilize notification methods to display information tailored to the user's situation, and notifications are adjusted according to the user's emotional state. This system is effective in simultaneously supporting efficient urban management and a comfortable life for residents.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The server collects information related to the urban environment from various sensors and devices. Specifically, it acquires traffic flow data from traffic sensors and energy consumption data from energy monitors. It receives this raw data as input and stores the raw data in a database as output. The data processing performed here is limited to data acquisition only.

[0155] Step 2:

[0156] The server preprocesses the raw data. It receives raw data as input and performs data cleansing, such as noise reduction and formatting standardization. Specifically, it performs data interpolation and outlier replacement. The output is clean data converted into an analyzable format.

[0157] Step 3:

[0158] The device collects user feedback and operation history, and uses an emotion engine to infer the user's emotional state. Inputs include user text input, operation logs, and facial expression data. The emotion engine uses this information to perform natural language processing and sentiment analysis, and generates data representing the user's emotional state as output.

[0159] Step 4:

[0160] The server analyzes both pre-processed city data and sentiment data transmitted from terminals. Inputs include clean data and user sentiment data. "AIAnalyticsEngineV5.0" is used to predict traffic patterns and calculate energy needs. Outputs include analysis results, such as prediction results and evaluation information.

[0161] Step 5:

[0162] The server generates proposals based on the analyzed data. The inputs are the analysis results and emotional state data. Using "ProposalGenerator3000," improvement proposals are created, including adjustments to signal control and optimization of the operation schedule. The output is the generated proposal.

[0163] Step 6:

[0164] The server sends the generated suggestions to the terminal, which then notifies the user. The input is the generated suggestions. Using a notification method, the suggestions are displayed on the user's screen. During this process, the tone and method of notification are customized according to the emotional state. The output is the displayed suggestions and any new feedback received from the user.

[0165] (Application Example 2)

[0166] 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 device 14 will be referred to as the "terminal."

[0167] In modern urban environments, multiple challenges exist simultaneously, including traffic congestion, energy consumption optimization, environmental protection, and disaster preparedness. Furthermore, providing uniform information and suggestions without considering user emotions can cause unnecessary stress. In this context, there is a need for efficient and user-friendly information provision and suggestions.

[0168] 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.

[0169] In this invention, the server includes information acquisition means for collecting information related to traffic, energy, environment, and disasters; emotion analysis means for recognizing the user's emotions; and suggestion creation means for generating improvement suggestions for the user. This enables efficient operation within the city and reduces the psychological burden on users, while also providing user-friendly suggestions and notifications.

[0170] "Information acquisition means" refers to any device or method for collecting information related to transportation, energy, environment, and disasters.

[0171] "Information processing means" refers to an apparatus or method for preprocessing acquired information and converting it into an analyzable format.

[0172] "Machine learning processing means" refers to a device or method that uses artificial intelligence to analyze pre-processed information and predict or evaluate traffic flow, energy demand, and environmental conditions.

[0173] "Emotional analysis means" refers to a device or method for recognizing and analyzing a user's emotions and using that information as material for the analysis results.

[0174] "Proposal generation means" refers to a device or method for generating improvement suggestions for users based on analysis results and sentiment information.

[0175] "Notification means" refers to a device or method for conveying the generated proposal to the user's information terminal.

[0176] In this invention, the server acquires information related to traffic, energy, environment, and disasters from various sensors and devices within a city. Information acquisition means include remotely accessible sensors and data supply systems via the internet. This information is received by the server, and data preprocessing is performed by information processing means. Preprocessing includes noise reduction and data format conversion.

[0177] The server analyzes pre-processed information using machine learning processing tools. Specifically, it utilizes data analysis libraries using Python (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow, PyTorch). As a result of the analysis, it predicts traffic flow and energy demand, and evaluates environmental conditions.

[0178] The device collects user operation history and feedback, and uses sentiment analysis to infer the user's emotions. For this purpose, it utilizes the smartphone's camera and microphone, and leverages an emotion analysis AI model (e.g., OpenCV, librosa + TensorFlow).

[0179] The server generates improvement suggestions using a suggestion generation system based on information obtained from both machine learning processing and sentiment analysis systems. The generated suggestions are delivered to the user's information terminal via a notification system. The notification method is adjusted according to the results of the sentiment analysis, using gentle language and notifications tailored to the importance of the suggestions.

[0180] For example, if traffic congestion is expected during the morning commute, the server will suggest optimizing the timing of traffic signals and notify the user's smartphone. If the user is experiencing stress, the server will provide more gentle language suggesting ways to use public transportation that day.

[0181] An example of a scenario where a generative AI model is used is the following prompt: "Based on data from today's urban environment, generate suggestions to help user X commute with less stress."

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The server collects information related to traffic, energy, environment, and disasters from sensors and devices within the city. It receives raw data from various sensors as input and collects it using information acquisition methods. After standardizing the data format and organizing the acquired information, it constructs a basic dataset for subsequent processing.

[0185] Step 2:

[0186] The server preprocesses the collected information. It receives the raw data acquired in step 1 as input and uses information processing tools to perform noise reduction, imputation of missing values, and removal of outliers. As output, it generates a dataset converted into an analyzable format, preparing it for subsequent analysis.

[0187] Step 3:

[0188] The server analyzes pre-processed data using machine learning methods. It receives a processed dataset as input and uses Python data analysis libraries and machine learning frameworks to predict traffic flow and energy demand, and evaluate environmental conditions. The output includes prediction results and evaluation values. Based on these results, the process moves to the next step.

[0189] Step 4:

[0190] The device performs user emotion analysis. It receives user camera images and audio data as input and uses an emotion analysis AI model within the device to infer emotions. Specifically, it uses OpenCV and librosa to analyze facial expressions and nuances of voice, and generates output that quantifies or categorizes the user's emotional state.

[0191] Step 5:

[0192] The server generates improvement suggestions using a suggestion generation method based on the analysis information obtained by the machine learning processing method and the sentiment analysis method. It receives the analysis results from step 3 and sentiment information from step 4 as input, and generates multiple suggestions using a suggestion generation algorithm. It generates flexible suggestions tailored to the user's emotional state as output, and creates data for notification.

[0193] Step 6:

[0194] The user receives the generated suggestions on their device. The notification system receives the suggestions from the server and guides the user with appropriate language based on their emotional state. For example, if stress is detected, it will be displayed in calm language to ensure the user can receive the information with peace of mind.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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".

[0211] This invention aims to collect, process, and analyze a wide variety of information related to urban traffic, energy, environment, and disasters in real time, and to propose and notify appropriate improvement measures. This system consists of the interaction of a server, terminals, and users.

[0212] Data acquisition and preprocessing

[0213] The server continuously acquires data from sensors, cameras, GPS devices, energy meters, and environmental sensors installed throughout the city. This allows for a comprehensive understanding of traffic flow, energy consumption, air quality, weather conditions, and earthquake information. After data acquisition, the server performs preprocessing such as noise reduction and missing value imputation to format the data into an analyzable format.

[0214] Data Analysis

[0215] The server inputs pre-processed data into an artificial intelligence (AI) processing system to analyze traffic congestion predictions and fluctuations in energy demand. The AI ​​learns from past data to predict future trends and detect anomalies. This makes it possible to take effective measures before problems become apparent.

[0216] Proposal generation and notification

[0217] Based on the analysis results, the server uses a proposal generation mechanism to generate suggestions such as traffic signal optimization, public transport operation adjustments, energy consumption reduction measures, and environmental improvement measures. The generated suggestions are then notified to system users via their terminals.

[0218] As a concrete example, the server analyzes traffic sensor data during rush hour and, if congestion is predicted at a specific intersection, proposes and implements adjustments to traffic signal timing. Furthermore, when peak energy consumption is predicted, it implements controls to promote the use of renewable energy. In the event of a disaster, the server quickly processes emergency information and notifies users of evacuation information on their devices. Users receive the notification and can take appropriate action.

[0219] Thus, this invention is designed to solve the complex challenges of cities and contribute to the realization of sustainable cities.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The server collects data from sensors, cameras, GPS devices, energy meters, and environmental sensors within the city. This includes traffic flow speed and location, energy consumption, air quality hazardous substance concentrations, noise levels, weather data, and seismic information.

[0223] Step 2:

[0224] The server performs noise reduction and missing value imputation on the collected data, and formats the data into a unified format. This preprocessing reduces data uncertainty and prepares the data for analysis.

[0225] Step 3:

[0226] The server passes pre-processed data to an artificial intelligence (AI) processing system for real-time analysis. The AI ​​predicts traffic congestion, analyzes fluctuations in energy demand, and assesses environmental conditions. For traffic flow, it refines congestion predictions through comparison with historical data, and for energy demand, it suggests the possibility of rapid demand shifts.

[0227] Step 4:

[0228] The server uses a proposal generation mechanism to generate optimization proposals based on the analysis results. These generated proposals include signal timing adjustments, changes to the operation schedule, and plans for optimizing energy use.

[0229] Step 5:

[0230] The server notifies the terminal of the generated suggestions and provides the system user with suggestions for necessary actions. The terminal receives this and provides the user with alerts and detailed instructions. Based on this, the user can decide on specific actions and take them.

[0231] (Example 1)

[0232] 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."

[0233] In today's society, where urban traffic congestion, energy efficiency, environmental protection, and rapid response to disasters are all demanded, there is a need to grasp these problems in real time and propose optimal solutions. However, existing technologies have lacked a system that can comprehensively and integrally analyze these areas and quickly generate proposals.

[0234] 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.

[0235] In this invention, the server includes information acquisition means for continuously collecting data from urban sensors and location measuring devices; data formatting means for denoising and imputing missing values ​​in the acquired data and converting it into an analyzable data format; and artificial intelligence analysis means for using the formatted data as input to predict traffic, predict fluctuations in energy demand, and detect anomalies based on learning from past data. This enables real-time analysis of complex urban problems and the rapid proposal and notification of specific and actionable improvement measures.

[0236] "Information acquisition means" refers to a device or system that continuously collects data from sensors and location measuring devices placed within a city.

[0237] A "data formatting means" is a device or system that processes collected data to remove noise and impute missing values, thereby preparing it into an analyzable data format.

[0238] "Artificial intelligence analysis means" refers to technology that uses artificial intelligence to learn from past data and perform traffic forecasting, energy demand fluctuation forecasting, and anomaly detection.

[0239] A "proposal formulation tool" is a device or system that generates improvement proposals, such as adjusting the timing of traffic signals or reducing energy consumption, based on the results of artificial intelligence analysis.

[0240] "Notification provision means" refers to a technology or system that transmits generated proposals to the user's communication device and promptly notifies the user of necessary information.

[0241] This invention is a system for comprehensive management of traffic, energy, environment, and disaster in urban areas. The server acquires data through various sensors and location measurement devices placed throughout the city. Hardware used includes traffic cameras, GPS receivers, and environmental monitoring sensors. The software includes interfaces for collecting, preprocessing, and providing this data.

[0242] The server performs data formatting on the collected data, removing noise and imputing missing values. The pre-processed data is then input into an artificial intelligence (AI) analysis system. This AI uses a generative AI model, learning from historical data to predict traffic patterns, energy demand fluctuations, and anomalies.

[0243] For example, a server might analyze data collected from traffic sensors during the morning rush hour to predict the likelihood of congestion at a particular intersection. In this case, the server could set up suggestions to change the timing of traffic lights and notify the user's terminal, providing immediate action. Furthermore, if increased energy consumption is predicted, measures such as promoting the use of renewable energy could be implemented.

[0244] As an example of a prompt, the AI ​​model can be instructed to "use data from urban traffic sensors to predict traffic congestion and propose effective improvement measures." This allows the server to perform appropriate data analysis and suggest improvements.

[0245] This system is designed to support efficient and sustainable urban life through close cooperation between servers, terminals, and users.

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] The server continuously collects data from various sensors and location measurement devices within the city. Inputs include data from traffic cameras, GPS devices, and environmental monitoring sensors. The server acquires this data and stores it in storage. During this process, accurate timestamps and location information are added to the data.

[0249] Step 2:

[0250] The server performs data formatting on the collected raw data, including noise reduction and missing value imputation. The input is the raw data obtained in step 1. The server applies a noise filtering algorithm to eliminate outliers. Next, a missing value imputation algorithm estimates and fills in the missing data, outputting data in an analyzable format.

[0251] Step 3:

[0252] The server inputs the formatted data into a generating AI model for data analysis. The input is pre-processed data. Using the artificial intelligence model, the server performs traffic forecasting and energy demand forecasting, as well as anomaly detection, based on historical data. The output is a forecast of the likelihood of future traffic congestion and fluctuations in energy consumption, providing insights for improvement.

[0253] Step 4:

[0254] Based on the AI ​​analysis results, the server generates specific improvement measures using a proposal formulation method. This system formulates measures such as adjusting traffic signals and reducing energy consumption. Based on the prediction results as input, the server outputs measures to adjust signal timing to alleviate congestion and optimize the energy supply and demand balance.

[0255] Step 5:

[0256] The terminal receives suggestions generated from the server and notifies the user. The input is suggestion data from the server. The terminal visualizes these suggestions using an appropriate user interface and outputs them in a format that the user can understand. This allows the user to choose an appropriate action based on the information.

[0257] (Application Example 1)

[0258] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0259] Modern cities face challenges such as traffic congestion, inefficient energy consumption, environmental degradation, and the need for rapid and effective responses to natural disasters. These issues should not be managed individually but rather considered holistically. However, traditional systems are often fragmented, limiting overall optimization. Furthermore, there is a lack of information to support residents' daily lives.

[0260] 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.

[0261] In this invention, the server includes information acquisition means, information processing means, artificial intelligence processing means, proposal generation means, notification means, and optimization means. This enables the integrated collection and analysis of information related to traffic, energy, environment, and disasters in cities, and allows for optimal urban management that supports the daily activities of residents.

[0262] "Information acquisition means" refers to functions for effectively gathering diverse information related to transportation, energy, the environment, and disasters.

[0263] "Information processing means" refers to methods for formatting acquired information into an analyzable format, removing noise, and imputing missing values.

[0264] "Artificial intelligence processing means" refers to analytical functions that utilize machine learning and deep learning to predict traffic congestion, assess energy demand trends, and evaluate environmental conditions based on pre-processed information.

[0265] The "proposal generation method" is a process that generates improvement measures, such as proposals for optimizing traffic signals or reducing energy consumption, based on the analysis results obtained by the artificial intelligence processing method.

[0266] A "notification means" is a communication system for quickly transmitting generated proposals and analysis results to the user's information device.

[0267] "Optimization methods" refer to techniques that most effectively utilize various proposals related to urban management, such as transportation and energy, in order to support the behavior of residents.

[0268] In order to implement this invention, it is necessary to construct a system equipped with information acquisition means, information processing means, artificial intelligence processing means, proposal generation means, notification means, and optimization means.

[0269] The server collects real-time information related to traffic, energy, the environment, and disasters from various sensors, cameras, GPS devices, energy meters, and environmental sensors installed in the city. This allows for comprehensive monitoring of traffic flow, energy consumption, air quality, weather, earthquakes, and more. The collected information is preprocessed to remove noise and impute missing values, preparing it for analysis. This process uses languages ​​such as Python to clean the data.

[0270] The pre-processed information is input into an artificial intelligence processing system on the server. Here, machine learning libraries such as TensorFlow and PyTorch are used to build predictive models based on historical data, and to predict traffic congestion, energy demand fluctuations, and environmental conditions. Based on the analysis results, the proposal generation system generates improvement proposals such as optimizing traffic signals, reducing energy consumption, and adjusting public infrastructure.

[0271] The generated suggestions are quickly notified to the user's information terminal via a notification system. These notifications are sent using services such as Firebase Cloud Messaging. Furthermore, optimization measures are implemented to optimize the suggestions regarding transportation and energy, thereby supporting residents' actions and enabling more efficient use of these suggestions.

[0272] For example, if a traffic sensor records high traffic volume at 8:00 AM on a particular day, the server analyzes this information and generates improvement measures to adjust the timing of traffic lights at a specific intersection. Similarly, if energy consumption is expected to peak, a notification is sent to encourage the use of renewable energy. In this way, residents receive optimized information on a daily basis, enabling them to efficiently adjust their lives.

[0273] Examples of input prompts for the generating AI model include, "Please tell me the current traffic situation and the forecast for the next hour," and "I would like to know the results of the analysis of today's energy consumption trends." By using these prompts, the system can provide residents with appropriate and timely information based on the complex data of the city.

[0274] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0275] Step 1:

[0276] The server acquires various data in real time from sensors, cameras, GPS devices, energy meters, and environmental sensors installed in the city. The input is various sensor data, and this data is captured by the server. The operation of the server is to acquire data using communication protocols. The output is raw sensor data.

[0277] Step 2:

[0278] The server preprocesses the acquired raw sensor data. The input is the acquired raw data, and this processing includes noise removal and missing value imputation. The server performs data cleaning using Python scripts. The output is in a clean data format that can be analyzed.

[0279] Step 3:

[0280] The server passes the preprocessed clean data to artificial intelligence processing means. The input is clean data, and the server uses TensorFlow, PyTorch, etc. to analyze this data. Predictions of traffic, predictions of energy demand, and evaluations of environmental situations are made using machine learning models. The output is the analysis result by the prediction model.

[0281] Step 4:

[0282] Based on the analysis result obtained by the artificial intelligence processing means, the server uses proposal generation means to generate improvement measures. The input is the analysis result, and the proposal generation means uses algorithms to form optimizations of traffic signals and energy reduction plans. The output is the generated improvement proposals.

[0283] Step 5:

[0284] The server sends the improvement proposals to the user's terminal through notification means. The input is the improvement proposals, and the server performs the operation of delivering notifications using Firebase Cloud Messaging. The output is the display of the notifications on the user terminal.

[0285] Step 6:

[0286] The user receives the notifications shown on the terminal and optimizes their actions in daily life. The input is the notifications sent to the terminal, and based on this, the user makes appropriate life adjustments such as adjusting travel time or changing energy usage. The output is the optimized daily actions.

[0287] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0288] The present invention is a system that collects information related to transportation, energy, environment, and disasters, and further combines an emotion engine that recognizes the user's emotions, to make more effective improvement proposals. Through the coordinated operation of the server, terminal, and user, the system analyzes this information, generates appropriate proposals, and notifies them.

[0289] Data Collection and Pretreatment

[0290] The server collects data from various devices within the city. The collected data includes traffic flow, energy consumption, environmental conditions, weather data, and earthquake information. These data are preprocessed so that the server can analyze them. The preprocessing includes noise removal and shaping of the data to improve the accuracy of the data.

[0291] Emotion Recognition by the Emotion Engine

[0292] The terminal also collects data related to the user. In particular, the emotion engine analyzes the user's feedback, operation history, and specific input data to infer the user's emotional state. This emotional data is also aggregated on the server and used as material for analysis.

[0293] Data Analysis

[0294] The server performs analysis using pre-processed data and sentiment data. Artificial intelligence processing makes predictions about traffic flow and energy demand, and conducts environmental assessments. Information from the sentiment engine is used to customize suggestions according to the user's emotional state.

[0295] Proposal generation and notification

[0296] Based on the analysis results, the server uses a proposal generation mechanism to create improvement suggestions, including signal control, public transport operation adjustments, and energy utilization optimization. An emotion engine selects a communication method appropriate to the user's emotional state, and notifications are sent to the user via the terminal. For example, if the user is feeling stressed, a gentler notification method is selected.

[0297] As a concrete example, if traffic congestion is expected during the morning commute, the server will suggest optimizing traffic signals and provide appropriate notifications to motivated users. If the system determines that a user is experiencing stress, it will only provide more important information in a calm tone. In this way, the system can guide users to take appropriate actions while reducing their psychological burden.

[0298] Thus, this invention is designed to support the efficient management of cities and the comfortable lives of their residents.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The server collects information from various points in the city, including traffic sensors, energy meters, environmental sensors, and weather station data. This ensures comprehensive coverage of traffic flow, energy consumption, air quality, noise levels, and weather data.

[0302] Step 2:

[0303] The server performs preprocessing to organize the collected data into an analyzable form. By removing noise from the data and complementing missing values, a highly reliable dataset is generated.

[0304] Step 3:

[0305] The terminal sends the user's operation history and feedback data to the emotion engine. The emotion engine uses this information to identify the user's current emotional state. This process uses natural language processing and pattern recognition techniques to evaluate emotions with high accuracy.

[0306] Step 4:

[0307] The server collates the preprocessed data with the emotion data obtained from the emotion engine and performs analysis using artificial intelligence processing means. Through this analysis, traffic congestion prediction, energy demand analysis, and environmental risk assessment are carried out, and optimal proposal content corresponding to the user's emotion is formulated.

[0308] Step 5:

[0309] The server uses the proposal generation means based on the analysis results to assemble specific improvement proposals. This includes support for optimizing traffic signals, adjusting energy usage plans, and recommending environmental policies.

[0310] Step 6:

[0311] The server transfers the generated proposals to the terminal and notifies in an appropriate manner. The user's emotional state is considered. For example, when the user is in a stressed state, the notification is sent in a gentle expression.

[0312] Step 7:

[0313] The user receives the notification through the terminal and implements the proposed actions. The user's feedback is sent from the terminal to the server again and used to improve the accuracy of the system.

[0314] (Example 2)

[0315] 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".

[0316] In today's urban environment, traffic congestion, wasteful energy consumption, and environmental deterioration are progressing, resulting in a decline in the quality of life for residents. Furthermore, conventional information systems often fail to consider user emotions when providing notifications and suggestions, potentially causing psychological burden on users. The challenge lies in solving these problems and building systems that support efficient urban management and a comfortable life for residents.

[0317] 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.

[0318] In this invention, the server includes information acquisition means for collecting data, information processing means for preprocessing the acquired data and converting it into an analyzable format, and artificial intelligence analysis means for analyzing the preprocessed data to predict traffic flow, calculate energy demand, and evaluate environmental conditions. This makes it possible to improve the efficiency of traffic management, optimize energy consumption, and monitor the environment. Furthermore, by incorporating emotion recognition means for identifying the user's emotional state, suggestions to the user can be made more personalized, reducing psychological burden.

[0319] "Information acquisition means" refers to technical means for collecting information related to traffic, energy, environment, and disasters using various sensors and devices.

[0320] "Information processing means" refers to the technical process of preparing collected raw data into a format suitable for analysis by performing noise reduction and format conversion.

[0321] "Artificial intelligence analysis methods" refer to technologies that apply algorithms and machine learning models to predict traffic flow and energy consumption based on pre-processed data, and to evaluate environmental conditions.

[0322] "Emotion recognition means" refers to technologies that analyze user feedback, operation history, and facial expression data to identify the user's emotional state.

[0323] The "proposal generation method" is a technology that generates improvement suggestions for system users based on the results obtained from artificial intelligence analysis methods and emotion recognition methods.

[0324] "Notification means" refers to technology for transmitting generated improvement suggestions to the user's device and providing information at an appropriate time and in an appropriate manner.

[0325] This system effectively collects and analyzes information related to traffic, energy, environment, and disasters in urban environments, and provides users with appropriate improvement suggestions. The server utilizes sensors and related devices installed in the city to collect various types of data. Specific hardware used includes traffic flow sensors and energy monitors. These devices collect information in a database in real time.

[0326] The device analyzes user feedback and operation history using an emotion engine to infer the user's emotional state. The emotion engine processes the user's text input and facial expression data to understand changes in emotion. This information is transmitted to a server via the internet and used as material for analysis along with other data.

[0327] The server uses preprocessing modules to clean up raw data and format it into an analyzable format. Then, it uses "AIAnalyticsEngineV5.0" to predict traffic flow, energy consumption, and environmental conditions. This enables highly accurate, data-driven predictions through artificial intelligence analysis.

[0328] The proposal generation method uses analysis results and sentiment data to generate improvement suggestions optimized for the user. For example, if traffic congestion is expected, the server proposes adjustments to signal control and transmits the instructions generated by "ProposalGenerator3000" to the terminal.

[0329] As a concrete example, the server uses traffic data from the morning commute to suggest traffic signal optimizations. Furthermore, a generative AI model is used to generate a prompt message that reads, "Please generate a notification message suggesting the optimal commute route based on this morning's traffic data and the user's sentiment."

[0330] Users review suggestions received through their devices and use them to guide their actions. The devices utilize notification methods to display information tailored to the user's situation, and notifications are adjusted according to the user's emotional state. This system is effective in simultaneously supporting efficient urban management and a comfortable life for residents.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] The server collects information related to the urban environment from various sensors and devices. Specifically, it acquires traffic flow data from traffic sensors and energy consumption data from energy monitors. It receives this raw data as input and stores the raw data in a database as output. The data processing performed here is limited to data acquisition only.

[0334] Step 2:

[0335] The server preprocesses the raw data. It receives raw data as input and performs data cleansing, such as noise reduction and formatting standardization. Specifically, it performs data interpolation and outlier replacement. The output is clean data converted into an analyzable format.

[0336] Step 3:

[0337] The device collects user feedback and operation history, and uses an emotion engine to infer the user's emotional state. Inputs include user text input, operation logs, and facial expression data. The emotion engine uses this information to perform natural language processing and sentiment analysis, and generates data representing the user's emotional state as output.

[0338] Step 4:

[0339] The server analyzes both pre-processed city data and sentiment data transmitted from terminals. Inputs include clean data and user sentiment data. "AIAnalyticsEngineV5.0" is used to predict traffic patterns and calculate energy needs. Outputs include analysis results, such as prediction results and evaluation information.

[0340] Step 5:

[0341] The server generates proposals based on the analyzed data. The inputs are the analysis results and emotional state data. Using "ProposalGenerator3000," improvement proposals are created, including adjustments to signal control and optimization of the operation schedule. The output is the generated proposal.

[0342] Step 6:

[0343] The server sends the generated suggestions to the terminal, which then notifies the user. The input is the generated suggestions. Using a notification method, the suggestions are displayed on the user's screen. During this process, the tone and method of notification are customized according to the emotional state. The output is the displayed suggestions and any new feedback received from the user.

[0344] (Application Example 2)

[0345] 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".

[0346] In modern urban environments, multiple challenges exist simultaneously, including traffic congestion, energy consumption optimization, environmental protection, and disaster preparedness. Furthermore, providing uniform information and suggestions without considering user emotions can cause unnecessary stress. In this context, there is a need for efficient and user-friendly information provision and suggestions.

[0347] 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.

[0348] In this invention, the server includes information acquisition means for collecting information related to traffic, energy, environment, and disasters; emotion analysis means for recognizing the user's emotions; and suggestion creation means for generating improvement suggestions for the user. This enables efficient operation within the city and reduces the psychological burden on users, while also providing user-friendly suggestions and notifications.

[0349] "Information acquisition means" refers to any device or method for collecting information related to transportation, energy, environment, and disasters.

[0350] "Information processing means" refers to an apparatus or method for preprocessing acquired information and converting it into an analyzable format.

[0351] "Machine learning processing means" refers to a device or method that uses artificial intelligence to analyze pre-processed information and predict or evaluate traffic flow, energy demand, and environmental conditions.

[0352] "Emotional analysis means" refers to a device or method for recognizing and analyzing a user's emotions and using that information as material for the analysis results.

[0353] "Proposal generation means" refers to a device or method for generating improvement suggestions for users based on analysis results and sentiment information.

[0354] "Notification means" refers to a device or method for conveying the generated proposal to the user's information terminal.

[0355] In this invention, the server acquires information related to traffic, energy, environment, and disasters from various sensors and devices within a city. Information acquisition means include remotely accessible sensors and data supply systems via the internet. This information is received by the server, and data preprocessing is performed by information processing means. Preprocessing includes noise reduction and data format conversion.

[0356] The server analyzes pre-processed information using machine learning processing tools. Specifically, it utilizes data analysis libraries using Python (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow, PyTorch). As a result of the analysis, it predicts traffic flow and energy demand, and evaluates environmental conditions.

[0357] The device collects user operation history and feedback, and uses sentiment analysis to infer the user's emotions. For this purpose, it utilizes the smartphone's camera and microphone, and leverages an emotion analysis AI model (e.g., OpenCV, librosa + TensorFlow).

[0358] The server generates improvement suggestions using a suggestion generation system based on information obtained from both machine learning processing and sentiment analysis systems. The generated suggestions are delivered to the user's information terminal via a notification system. The notification method is adjusted according to the results of the sentiment analysis, using gentle language and notifications tailored to the importance of the suggestions.

[0359] For example, if traffic congestion is expected during the morning commute, the server will suggest optimizing the timing of traffic signals and notify the user's smartphone. If the user is experiencing stress, the server will provide more gentle language suggesting ways to use public transportation that day.

[0360] An example of a scenario where a generative AI model is used is the following prompt: "Based on data from today's urban environment, generate suggestions to help user X commute with less stress."

[0361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0362] Step 1:

[0363] The server collects information related to traffic, energy, environment, and disasters from sensors and devices within the city. It receives raw data from various sensors as input and collects it using information acquisition methods. After standardizing the data format and organizing the acquired information, it constructs a basic dataset for subsequent processing.

[0364] Step 2:

[0365] The server preprocesses the collected information. It receives the raw data acquired in step 1 as input and uses information processing tools to perform noise reduction, imputation of missing values, and removal of outliers. As output, it generates a dataset converted into an analyzable format, preparing it for subsequent analysis.

[0366] Step 3:

[0367] The server analyzes pre-processed data using machine learning methods. It receives a processed dataset as input and uses Python data analysis libraries and machine learning frameworks to predict traffic flow and energy demand, and evaluate environmental conditions. The output includes prediction results and evaluation values. Based on these results, the process moves to the next step.

[0368] Step 4:

[0369] The device performs user emotion analysis. It receives user camera images and audio data as input and uses an emotion analysis AI model within the device to infer emotions. Specifically, it uses OpenCV and librosa to analyze facial expressions and nuances of voice, and generates output that quantifies or categorizes the user's emotional state.

[0370] Step 5:

[0371] The server generates improvement suggestions using a suggestion generation method based on the analysis information obtained by the machine learning processing method and the sentiment analysis method. It receives the analysis results from step 3 and sentiment information from step 4 as input, and generates multiple suggestions using a suggestion generation algorithm. It generates flexible suggestions tailored to the user's emotional state as output, and creates data for notification.

[0372] Step 6:

[0373] The user receives the generated suggestions on their device. The notification system receives the suggestions from the server and guides the user with appropriate language based on their emotional state. For example, if stress is detected, it will be displayed in calm language to ensure the user can receive the information with peace of mind.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] [Third Embodiment]

[0378] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0379] 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.

[0380] 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).

[0381] 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.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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".

[0390] This invention aims to collect, process, and analyze a wide variety of information related to urban traffic, energy, environment, and disasters in real time, and to propose and notify appropriate improvement measures. This system consists of the interaction of a server, terminals, and users.

[0391] Data acquisition and preprocessing

[0392] The server continuously acquires data from sensors, cameras, GPS devices, energy meters, and environmental sensors installed throughout the city. This allows for a comprehensive understanding of traffic flow, energy consumption, air quality, weather conditions, and earthquake information. After data acquisition, the server performs preprocessing such as noise reduction and missing value imputation to format the data into an analyzable format.

[0393] Data Analysis

[0394] The server inputs pre-processed data into an artificial intelligence (AI) processing system to analyze traffic congestion predictions and fluctuations in energy demand. The AI ​​learns from past data to predict future trends and detect anomalies. This makes it possible to take effective measures before problems become apparent.

[0395] Proposal generation and notification

[0396] Based on the analysis results, the server uses a proposal generation mechanism to generate suggestions such as traffic signal optimization, public transport operation adjustments, energy consumption reduction measures, and environmental improvement measures. The generated suggestions are then notified to system users via their terminals.

[0397] As a concrete example, the server analyzes traffic sensor data during rush hour and, if congestion is predicted at a specific intersection, proposes and implements adjustments to traffic signal timing. Furthermore, when peak energy consumption is predicted, it implements controls to promote the use of renewable energy. In the event of a disaster, the server quickly processes emergency information and notifies users of evacuation information on their devices. Users receive the notification and can take appropriate action.

[0398] Thus, this invention is designed to solve the complex challenges of cities and contribute to the realization of sustainable cities.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] The server collects data from sensors, cameras, GPS devices, energy meters, and environmental sensors within the city. This includes traffic flow speed and location, energy consumption, air quality hazardous substance concentrations, noise levels, weather data, and seismic information.

[0402] Step 2:

[0403] The server performs noise reduction and missing value imputation on the collected data, and formats the data into a unified format. This preprocessing reduces data uncertainty and prepares the data for analysis.

[0404] Step 3:

[0405] The server passes pre-processed data to an artificial intelligence (AI) processing system for real-time analysis. The AI ​​predicts traffic congestion, analyzes fluctuations in energy demand, and assesses environmental conditions. For traffic flow, it refines congestion predictions through comparison with historical data, and for energy demand, it suggests the possibility of rapid demand shifts.

[0406] Step 4:

[0407] The server uses a proposal generation mechanism to generate optimization proposals based on the analysis results. These generated proposals include signal timing adjustments, changes to the operation schedule, and plans for optimizing energy use.

[0408] Step 5:

[0409] The server notifies the terminal of the generated suggestions and provides the system user with suggestions for necessary actions. The terminal receives this and provides the user with alerts and detailed instructions. Based on this, the user can decide on specific actions and take them.

[0410] (Example 1)

[0411] 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."

[0412] In today's society, where urban traffic congestion, energy efficiency, environmental protection, and rapid response to disasters are all demanded, there is a need to grasp these problems in real time and propose optimal solutions. However, existing technologies have lacked a system that can comprehensively and integrally analyze these areas and quickly generate proposals.

[0413] 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.

[0414] In this invention, the server includes information acquisition means for continuously collecting data from urban sensors and location measuring devices; data formatting means for denoising and imputing missing values ​​in the acquired data and converting it into an analyzable data format; and artificial intelligence analysis means for using the formatted data as input to predict traffic, predict fluctuations in energy demand, and detect anomalies based on learning from past data. This enables real-time analysis of complex urban problems and the rapid proposal and notification of specific and actionable improvement measures.

[0415] "Information acquisition means" refers to a device or system that continuously collects data from sensors and location measuring devices placed within a city.

[0416] A "data formatting means" is a device or system that processes collected data to remove noise and impute missing values, thereby preparing it into an analyzable data format.

[0417] "Artificial intelligence analysis means" refers to technology that uses artificial intelligence to learn from past data and perform traffic forecasting, energy demand fluctuation forecasting, and anomaly detection.

[0418] A "proposal formulation tool" is a device or system that generates improvement proposals, such as adjusting the timing of traffic signals or reducing energy consumption, based on the results of artificial intelligence analysis.

[0419] "Notification provision means" refers to a technology or system that transmits generated proposals to the user's communication device and promptly notifies the user of necessary information.

[0420] This invention is a system for comprehensive management of traffic, energy, environment, and disaster in urban areas. The server acquires data through various sensors and location measurement devices placed throughout the city. Hardware used includes traffic cameras, GPS receivers, and environmental monitoring sensors. The software includes interfaces for collecting, preprocessing, and providing this data.

[0421] The server performs data formatting on the collected data, removing noise and imputing missing values. The pre-processed data is then input into an artificial intelligence (AI) analysis system. This AI uses a generative AI model, learning from historical data to predict traffic patterns, energy demand fluctuations, and anomalies.

[0422] For example, a server might analyze data collected from traffic sensors during the morning rush hour to predict the likelihood of congestion at a particular intersection. In this case, the server could set up suggestions to change the timing of traffic lights and notify the user's terminal, providing immediate action. Furthermore, if increased energy consumption is predicted, measures such as promoting the use of renewable energy could be implemented.

[0423] As an example of a prompt, the AI ​​model can be instructed to "use data from urban traffic sensors to predict traffic congestion and propose effective improvement measures." This allows the server to perform appropriate data analysis and suggest improvements.

[0424] This system is designed to support efficient and sustainable urban life through close cooperation between servers, terminals, and users.

[0425] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0426] Step 1:

[0427] The server continuously collects data from various sensors and location measurement devices within the city. Inputs include data from traffic cameras, GPS devices, and environmental monitoring sensors. The server acquires this data and stores it in storage. During this process, accurate timestamps and location information are added to the data.

[0428] Step 2:

[0429] The server performs data formatting on the collected raw data, including noise reduction and missing value imputation. The input is the raw data obtained in step 1. The server applies a noise filtering algorithm to eliminate outliers. Next, a missing value imputation algorithm estimates and fills in the missing data, outputting data in an analyzable format.

[0430] Step 3:

[0431] The server inputs the formatted data into a generating AI model for data analysis. The input is pre-processed data. Using the artificial intelligence model, the server performs traffic forecasting and energy demand forecasting, as well as anomaly detection, based on historical data. The output is a forecast of the likelihood of future traffic congestion and fluctuations in energy consumption, providing insights for improvement.

[0432] Step 4:

[0433] Based on the AI ​​analysis results, the server generates specific improvement measures using a proposal formulation method. This system formulates measures such as adjusting traffic signals and reducing energy consumption. Based on the prediction results as input, the server outputs measures to adjust signal timing to alleviate congestion and optimize the energy supply and demand balance.

[0434] Step 5:

[0435] The terminal receives suggestions generated from the server and notifies the user. The input is suggestion data from the server. The terminal visualizes these suggestions using an appropriate user interface and outputs them in a format that the user can understand. This allows the user to choose an appropriate action based on the information.

[0436] (Application Example 1)

[0437] 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."

[0438] Modern cities face challenges such as traffic congestion, inefficient energy consumption, environmental degradation, and the need for rapid and effective responses to natural disasters. These issues should not be managed individually but rather considered holistically. However, traditional systems are often fragmented, limiting overall optimization. Furthermore, there is a lack of information to support residents' daily lives.

[0439] 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.

[0440] In this invention, the server includes information acquisition means, information processing means, artificial intelligence processing means, proposal generation means, notification means, and optimization means. This enables the integrated collection and analysis of information related to traffic, energy, environment, and disasters in cities, and allows for optimal urban management that supports the daily activities of residents.

[0441] "Information acquisition means" refers to functions for effectively gathering diverse information related to transportation, energy, the environment, and disasters.

[0442] "Information processing means" refers to methods for formatting acquired information into an analyzable format, removing noise, and imputing missing values.

[0443] "Artificial intelligence processing means" refers to analytical functions that utilize machine learning and deep learning to predict traffic congestion, assess energy demand trends, and evaluate environmental conditions based on pre-processed information.

[0444] The "proposal generation method" is a process that generates improvement measures, such as proposals for optimizing traffic signals or reducing energy consumption, based on the analysis results obtained by the artificial intelligence processing method.

[0445] A "notification means" is a communication system for quickly transmitting generated proposals and analysis results to the user's information device.

[0446] "Optimization methods" refer to techniques that most effectively utilize various proposals related to urban management, such as transportation and energy, in order to support the behavior of residents.

[0447] In order to implement this invention, it is necessary to construct a system equipped with information acquisition means, information processing means, artificial intelligence processing means, proposal generation means, notification means, and optimization means.

[0448] The server collects real-time information related to traffic, energy, the environment, and disasters from various sensors, cameras, GPS devices, energy meters, and environmental sensors installed in the city. This allows for comprehensive monitoring of traffic flow, energy consumption, air quality, weather, earthquakes, and more. The collected information is preprocessed to remove noise and impute missing values, preparing it for analysis. This process uses languages ​​such as Python to clean the data.

[0449] The pre-processed information is input into an artificial intelligence processing system on the server. Here, machine learning libraries such as TensorFlow and PyTorch are used to build predictive models based on historical data, and to predict traffic congestion, energy demand fluctuations, and environmental conditions. Based on the analysis results, the proposal generation system generates improvement proposals such as optimizing traffic signals, reducing energy consumption, and adjusting public infrastructure.

[0450] The generated suggestions are quickly notified to the user's information terminal via a notification system. These notifications are sent using services such as Firebase Cloud Messaging. Furthermore, optimization measures are implemented to optimize the suggestions regarding transportation and energy, thereby supporting residents' actions and enabling more efficient use of these suggestions.

[0451] For example, if a traffic sensor records high traffic volume at 8:00 AM on a particular day, the server analyzes this information and generates improvement measures to adjust the timing of traffic lights at a specific intersection. Similarly, if energy consumption is expected to peak, a notification is sent to encourage the use of renewable energy. In this way, residents receive optimized information on a daily basis, enabling them to efficiently adjust their lives.

[0452] Examples of input prompts for the generating AI model include, "Please tell me the current traffic situation and the forecast for the next hour," and "I would like to know the results of the analysis of today's energy consumption trends." By using these prompts, the system can provide residents with appropriate and timely information based on the complex data of the city.

[0453] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0454] Step 1:

[0455] The server acquires various data in real time from sensors, cameras, GPS devices, energy meters, and environmental sensors installed in the city. The input consists of various sensor data, which is then taken into the server. The server's operation involves acquiring data using communication protocols. The output is raw sensor data.

[0456] Step 2:

[0457] The server preprocesses the acquired raw sensor data. The input is the acquired raw data, and this processing includes noise reduction and missing value imputation. The server performs data cleaning using a Python script. The output is in an analyzable, clean data format.

[0458] Step 3:

[0459] The server passes pre-processed clean data to an artificial intelligence processing system. The input is clean data, and the server uses tools such as TensorFlow or PyTorch to analyze this data. Machine learning models are used to predict traffic, predict energy demand, and assess environmental conditions. The output is the analysis results from the predictive model.

[0460] Step 4:

[0461] The server generates improvement measures using a proposal generation means based on the analysis results obtained by the artificial intelligence processing means. The input is the analysis results, and the proposal generation means uses an algorithm to form traffic signal optimization and energy reduction proposals. The output is the generated improvement proposal.

[0462] Step 5:

[0463] The server sends improvement suggestions to the user's device via a notification system. The input is the improvement suggestion, and the server uses Firebase Cloud Messaging to deliver the notification. The output is the display of the notification on the user's device.

[0464] Step 6:

[0465] The user receives notifications displayed on their device and optimizes their daily activities. The input is the notifications sent to the device, which the user uses to make appropriate lifestyle adjustments, such as adjusting travel time or changing energy usage. The output is the optimized daily activities.

[0466] 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.

[0467] This invention is a system that collects information related to traffic, energy, the environment, and disasters, and further combines it with an emotion engine that recognizes user emotions to provide more effective improvement suggestions. Through the coordinated operation of the server, terminal, and user, the system analyzes this information, generates appropriate suggestions, and notifies users.

[0468] Data acquisition and preprocessing

[0469] The server collects data from various devices within the city. This data includes traffic flow, energy consumption, environmental conditions, weather data, and earthquake information. The server preprocesses this data to make it analyzable. Preprocessing includes denoising and formatting the data to improve its accuracy.

[0470] Emotion recognition by an emotion engine

[0471] The device also collects data about the user. In particular, the emotion engine analyzes user feedback, operation history, and specific input data to infer the user's emotional state. This emotion data is also aggregated on a server and used as material for analysis.

[0472] Data Analysis

[0473] The server performs analysis using pre-processed data and sentiment data. Artificial intelligence processing makes predictions about traffic flow and energy demand, and conducts environmental assessments. Information from the sentiment engine is used to customize suggestions according to the user's emotional state.

[0474] Proposal generation and notification

[0475] Based on the analysis results, the server uses a proposal generation mechanism to create improvement suggestions, including signal control, public transport operation adjustments, and energy utilization optimization. An emotion engine selects a communication method appropriate to the user's emotional state, and notifications are sent to the user via the terminal. For example, if the user is feeling stressed, a gentler notification method is selected.

[0476] As a concrete example, if traffic congestion is expected during the morning commute, the server will suggest optimizing traffic signals and provide appropriate notifications to motivated users. If the system determines that a user is experiencing stress, it will only provide more important information in a calm tone. In this way, the system can guide users to take appropriate actions while reducing their psychological burden.

[0477] Thus, this invention is designed to support the efficient management of cities and the comfortable lives of their residents.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The server collects information from various points in the city, including traffic sensors, energy meters, environmental sensors, and weather station data. This ensures comprehensive coverage of traffic flow, energy consumption, air quality, noise levels, and weather data.

[0481] Step 2:

[0482] The server preprocesses the collected data to prepare it for analysis. By removing noise and imputing missing values, it generates a reliable dataset.

[0483] Step 3:

[0484] The device sends user operation history and feedback data to the emotion engine. The emotion engine uses this information to identify the user's current emotional state. This process uses natural language processing and pattern recognition techniques to evaluate emotions with high accuracy.

[0485] Step 4:

[0486] The server compares pre-processed data with emotional data obtained from the emotion engine and performs analysis using artificial intelligence processing. This analysis is used to predict traffic congestion, analyze energy demand, and assess environmental risks, and to formulate optimal suggestions tailored to the user's emotions.

[0487] Step 5:

[0488] Based on the analysis results, the server uses a proposal generation mechanism to assemble specific improvement suggestions. These include supporting the optimization of traffic signals, adjusting energy use plans, and recommending environmental policies.

[0489] Step 6:

[0490] The server forwards the generated suggestions to the terminal and sends notifications in an appropriate manner. The user's emotional state is taken into consideration; for example, if the user is stressed, the notification will be sent in a gentler tone.

[0491] Step 7:

[0492] Users receive notifications through their devices and take the suggested actions. User feedback is then sent back from the device to the server and used to improve the system's accuracy.

[0493] (Example 2)

[0494] 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."

[0495] In today's urban environment, traffic congestion, wasteful energy consumption, and environmental deterioration are progressing, resulting in a decline in the quality of life for residents. Furthermore, conventional information systems often fail to consider user emotions when providing notifications and suggestions, potentially causing psychological burden on users. The challenge lies in solving these problems and building systems that support efficient urban management and a comfortable life for residents.

[0496] 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.

[0497] In this invention, the server includes information acquisition means for collecting data, information processing means for preprocessing the acquired data and converting it into an analyzable format, and artificial intelligence analysis means for analyzing the preprocessed data to predict traffic flow, calculate energy demand, and evaluate environmental conditions. This makes it possible to improve the efficiency of traffic management, optimize energy consumption, and monitor the environment. Furthermore, by incorporating emotion recognition means for identifying the user's emotional state, suggestions to the user can be made more personalized, reducing psychological burden.

[0498] "Information acquisition means" refers to technical means for collecting information related to traffic, energy, environment, and disasters using various sensors and devices.

[0499] "Information processing means" refers to the technical process of preparing collected raw data into a format suitable for analysis by performing noise reduction and format conversion.

[0500] "Artificial intelligence analysis methods" refer to technologies that apply algorithms and machine learning models to predict traffic flow and energy consumption based on pre-processed data, and to evaluate environmental conditions.

[0501] "Emotion recognition means" refers to technologies that analyze user feedback, operation history, and facial expression data to identify the user's emotional state.

[0502] The "proposal generation method" is a technology that generates improvement suggestions for system users based on the results obtained from artificial intelligence analysis methods and emotion recognition methods.

[0503] "Notification means" refers to technology for transmitting generated improvement suggestions to the user's device and providing information at an appropriate time and in an appropriate manner.

[0504] This system effectively collects and analyzes information related to traffic, energy, environment, and disasters in urban environments, and provides users with appropriate improvement suggestions. The server utilizes sensors and related devices installed in the city to collect various types of data. Specific hardware used includes traffic flow sensors and energy monitors. These devices collect information in a database in real time.

[0505] The device analyzes user feedback and operation history using an emotion engine to infer the user's emotional state. The emotion engine processes the user's text input and facial expression data to understand changes in emotion. This information is transmitted to a server via the internet and used as material for analysis along with other data.

[0506] The server uses preprocessing modules to clean up raw data and format it into an analyzable format. Then, it uses "AIAnalyticsEngineV5.0" to predict traffic flow, energy consumption, and environmental conditions. This enables highly accurate, data-driven predictions through artificial intelligence analysis.

[0507] The proposal generation method uses analysis results and sentiment data to generate improvement suggestions optimized for the user. For example, if traffic congestion is expected, the server proposes adjustments to signal control and transmits the instructions generated by "ProposalGenerator3000" to the terminal.

[0508] As a concrete example, the server uses traffic data from the morning commute to suggest traffic signal optimizations. Furthermore, a generative AI model is used to generate a prompt message that reads, "Please generate a notification message suggesting the optimal commute route based on this morning's traffic data and the user's sentiment."

[0509] Users review suggestions received through their devices and use them to guide their actions. The devices utilize notification methods to display information tailored to the user's situation, and notifications are adjusted according to the user's emotional state. This system is effective in simultaneously supporting efficient urban management and a comfortable life for residents.

[0510] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0511] Step 1:

[0512] The server collects information related to the urban environment from various sensors and devices. Specifically, it acquires traffic flow data from traffic sensors and energy consumption data from energy monitors. It receives this raw data as input and stores the raw data in a database as output. The data processing performed here is limited to data acquisition only.

[0513] Step 2:

[0514] The server preprocesses the raw data. It receives raw data as input and performs data cleansing, such as noise reduction and formatting standardization. Specifically, it performs data interpolation and outlier replacement. The output is clean data converted into an analyzable format.

[0515] Step 3:

[0516] The device collects user feedback and operation history, and uses an emotion engine to infer the user's emotional state. Inputs include user text input, operation logs, and facial expression data. The emotion engine uses this information to perform natural language processing and sentiment analysis, and generates data representing the user's emotional state as output.

[0517] Step 4:

[0518] The server analyzes both pre-processed city data and sentiment data transmitted from terminals. Inputs include clean data and user sentiment data. "AIAnalyticsEngineV5.0" is used to predict traffic patterns and calculate energy needs. Outputs include analysis results, such as prediction results and evaluation information.

[0519] Step 5:

[0520] The server generates proposals based on the analyzed data. The inputs are the analysis results and emotional state data. Using "ProposalGenerator3000," improvement proposals are created, including adjustments to signal control and optimization of the operation schedule. The output is the generated proposal.

[0521] Step 6:

[0522] The server sends the generated suggestions to the terminal, which then notifies the user. The input is the generated suggestions. Using a notification method, the suggestions are displayed on the user's screen. During this process, the tone and method of notification are customized according to the emotional state. The output is the displayed suggestions and any new feedback received from the user.

[0523] (Application Example 2)

[0524] 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."

[0525] In modern urban environments, multiple challenges exist simultaneously, including traffic congestion, energy consumption optimization, environmental protection, and disaster preparedness. Furthermore, providing uniform information and suggestions without considering user emotions can cause unnecessary stress. In this context, there is a need for efficient and user-friendly information provision and suggestions.

[0526] 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.

[0527] In this invention, the server includes information acquisition means for collecting information related to traffic, energy, environment, and disasters; emotion analysis means for recognizing the user's emotions; and suggestion creation means for generating improvement suggestions for the user. This enables efficient operation within the city and reduces the psychological burden on users, while also providing user-friendly suggestions and notifications.

[0528] "Information acquisition means" refers to any device or method for collecting information related to transportation, energy, environment, and disasters.

[0529] "Information processing means" refers to an apparatus or method for preprocessing acquired information and converting it into an analyzable format.

[0530] "Machine learning processing means" refers to a device or method that uses artificial intelligence to analyze pre-processed information and predict or evaluate traffic flow, energy demand, and environmental conditions.

[0531] "Emotional analysis means" refers to a device or method for recognizing and analyzing a user's emotions and using that information as material for the analysis results.

[0532] "Proposal generation means" refers to a device or method for generating improvement suggestions for users based on analysis results and sentiment information.

[0533] "Notification means" refers to a device or method for conveying the generated proposal to the user's information terminal.

[0534] In this invention, the server acquires information related to traffic, energy, environment, and disasters from various sensors and devices within a city. Information acquisition means include remotely accessible sensors and data supply systems via the internet. This information is received by the server, and data preprocessing is performed by information processing means. Preprocessing includes noise reduction and data format conversion.

[0535] The server analyzes pre-processed information using machine learning processing tools. Specifically, it utilizes data analysis libraries using Python (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow, PyTorch). As a result of the analysis, it predicts traffic flow and energy demand, and evaluates environmental conditions.

[0536] The device collects user operation history and feedback, and uses sentiment analysis to infer the user's emotions. For this purpose, it utilizes the smartphone's camera and microphone, and leverages an emotion analysis AI model (e.g., OpenCV, librosa + TensorFlow).

[0537] The server generates improvement suggestions using a suggestion generation system based on information obtained from both machine learning processing and sentiment analysis systems. The generated suggestions are delivered to the user's information terminal via a notification system. The notification method is adjusted according to the results of the sentiment analysis, using gentle language and notifications tailored to the importance of the suggestions.

[0538] For example, if traffic congestion is expected during the morning commute, the server will suggest optimizing the timing of traffic signals and notify the user's smartphone. If the user is experiencing stress, the server will provide more gentle language suggesting ways to use public transportation that day.

[0539] An example of a scenario where a generative AI model is used is the following prompt: "Based on data from today's urban environment, generate suggestions to help user X commute with less stress."

[0540] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0541] Step 1:

[0542] The server collects information related to traffic, energy, environment, and disasters from sensors and devices within the city. It receives raw data from various sensors as input and collects it using information acquisition methods. After standardizing the data format and organizing the acquired information, it constructs a basic dataset for subsequent processing.

[0543] Step 2:

[0544] The server preprocesses the collected information. It receives the raw data acquired in step 1 as input and uses information processing tools to perform noise reduction, imputation of missing values, and removal of outliers. As output, it generates a dataset converted into an analyzable format, preparing it for subsequent analysis.

[0545] Step 3:

[0546] The server analyzes pre-processed data using machine learning methods. It receives a processed dataset as input and uses Python data analysis libraries and machine learning frameworks to predict traffic flow and energy demand, and evaluate environmental conditions. The output includes prediction results and evaluation values. Based on these results, the process moves to the next step.

[0547] Step 4:

[0548] The device performs user emotion analysis. It receives user camera images and audio data as input and uses an emotion analysis AI model within the device to infer emotions. Specifically, it uses OpenCV and librosa to analyze facial expressions and nuances of voice, and generates output that quantifies or categorizes the user's emotional state.

[0549] Step 5:

[0550] The server generates improvement suggestions using a suggestion generation method based on the analysis information obtained by the machine learning processing method and the sentiment analysis method. It receives the analysis results from step 3 and sentiment information from step 4 as input, and generates multiple suggestions using a suggestion generation algorithm. It generates flexible suggestions tailored to the user's emotional state as output, and creates data for notification.

[0551] Step 6:

[0552] The user receives the generated suggestions on their device. The notification system receives the suggestions from the server and guides the user with appropriate language based on their emotional state. For example, if stress is detected, it will be displayed in calm language to ensure the user can receive the information with peace of mind.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] [Fourth Embodiment]

[0557] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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).

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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".

[0570] This invention aims to collect, process, and analyze a wide variety of information related to urban traffic, energy, environment, and disasters in real time, and to propose and notify appropriate improvement measures. This system consists of the interaction of a server, terminals, and users.

[0571] Data acquisition and preprocessing

[0572] The server continuously acquires data from sensors, cameras, GPS devices, energy meters, and environmental sensors installed throughout the city. This allows for a comprehensive understanding of traffic flow, energy consumption, air quality, weather conditions, and earthquake information. After data acquisition, the server performs preprocessing such as noise reduction and missing value imputation to format the data into an analyzable format.

[0573] Data Analysis

[0574] The server inputs pre-processed data into an artificial intelligence (AI) processing system to analyze traffic congestion predictions and fluctuations in energy demand. The AI ​​learns from past data to predict future trends and detect anomalies. This makes it possible to take effective measures before problems become apparent.

[0575] Proposal generation and notification

[0576] Based on the analysis results, the server uses a proposal generation mechanism to generate suggestions such as traffic signal optimization, public transport operation adjustments, energy consumption reduction measures, and environmental improvement measures. The generated suggestions are then notified to system users via their terminals.

[0577] As a concrete example, the server analyzes traffic sensor data during rush hour and, if congestion is predicted at a specific intersection, proposes and implements adjustments to traffic signal timing. Furthermore, when peak energy consumption is predicted, it implements controls to promote the use of renewable energy. In the event of a disaster, the server quickly processes emergency information and notifies users of evacuation information on their devices. Users receive the notification and can take appropriate action.

[0578] Thus, this invention is designed to solve the complex challenges of cities and contribute to the realization of sustainable cities.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] The server collects data from sensors, cameras, GPS devices, energy meters, and environmental sensors within the city. This includes traffic flow speed and location, energy consumption, air quality hazardous substance concentrations, noise levels, weather data, and seismic information.

[0582] Step 2:

[0583] The server performs noise reduction and missing value imputation on the collected data, and formats the data into a unified format. This preprocessing reduces data uncertainty and prepares the data for analysis.

[0584] Step 3:

[0585] The server passes pre-processed data to an artificial intelligence (AI) processing system for real-time analysis. The AI ​​predicts traffic congestion, analyzes fluctuations in energy demand, and assesses environmental conditions. For traffic flow, it refines congestion predictions through comparison with historical data, and for energy demand, it suggests the possibility of rapid demand shifts.

[0586] Step 4:

[0587] The server uses a proposal generation mechanism to generate optimization proposals based on the analysis results. These generated proposals include signal timing adjustments, changes to the operation schedule, and plans for optimizing energy use.

[0588] Step 5:

[0589] The server notifies the terminal of the generated suggestions and provides the system user with suggestions for necessary actions. The terminal receives this and provides the user with alerts and detailed instructions. Based on this, the user can decide on specific actions and take them.

[0590] (Example 1)

[0591] 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".

[0592] In today's society, where urban traffic congestion, energy efficiency, environmental protection, and rapid response to disasters are all demanded, there is a need to grasp these problems in real time and propose optimal solutions. However, existing technologies have lacked a system that can comprehensively and integrally analyze these areas and quickly generate proposals.

[0593] 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.

[0594] In this invention, the server includes information acquisition means for continuously collecting data from urban sensors and location measuring devices; data formatting means for denoising and imputing missing values ​​in the acquired data and converting it into an analyzable data format; and artificial intelligence analysis means for using the formatted data as input to predict traffic, predict fluctuations in energy demand, and detect anomalies based on learning from past data. This enables real-time analysis of complex urban problems and the rapid proposal and notification of specific and actionable improvement measures.

[0595] "Information acquisition means" refers to a device or system that continuously collects data from sensors and location measuring devices placed within a city.

[0596] A "data formatting means" is a device or system that processes collected data to remove noise and impute missing values, thereby preparing it into an analyzable data format.

[0597] "Artificial intelligence analysis means" refers to technology that uses artificial intelligence to learn from past data and perform traffic forecasting, energy demand fluctuation forecasting, and anomaly detection.

[0598] A "proposal formulation tool" is a device or system that generates improvement proposals, such as adjusting the timing of traffic signals or reducing energy consumption, based on the results of artificial intelligence analysis.

[0599] "Notification provision means" refers to a technology or system that transmits generated proposals to the user's communication device and promptly notifies the user of necessary information.

[0600] This invention is a system for comprehensive management of traffic, energy, environment, and disaster in urban areas. The server acquires data through various sensors and location measurement devices placed throughout the city. Hardware used includes traffic cameras, GPS receivers, and environmental monitoring sensors. The software includes interfaces for collecting, preprocessing, and providing this data.

[0601] The server performs data formatting on the collected data, removing noise and imputing missing values. The pre-processed data is then input into an artificial intelligence (AI) analysis system. This AI uses a generative AI model, learning from historical data to predict traffic patterns, energy demand fluctuations, and anomalies.

[0602] For example, a server might analyze data collected from traffic sensors during the morning rush hour to predict the likelihood of congestion at a particular intersection. In this case, the server could set up suggestions to change the timing of traffic lights and notify the user's terminal, providing immediate action. Furthermore, if increased energy consumption is predicted, measures such as promoting the use of renewable energy could be implemented.

[0603] As an example of a prompt, the AI ​​model can be instructed to "use data from urban traffic sensors to predict traffic congestion and propose effective improvement measures." This allows the server to perform appropriate data analysis and suggest improvements.

[0604] This system is designed to support efficient and sustainable urban life through close cooperation between servers, terminals, and users.

[0605] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0606] Step 1:

[0607] The server continuously collects data from various sensors and location measurement devices within the city. Inputs include data from traffic cameras, GPS devices, and environmental monitoring sensors. The server acquires this data and stores it in storage. During this process, accurate timestamps and location information are added to the data.

[0608] Step 2:

[0609] The server performs data formatting on the collected raw data, including noise reduction and missing value imputation. The input is the raw data obtained in step 1. The server applies a noise filtering algorithm to eliminate outliers. Next, a missing value imputation algorithm estimates and fills in the missing data, outputting data in an analyzable format.

[0610] Step 3:

[0611] The server inputs the formatted data into a generating AI model for data analysis. The input is pre-processed data. Using the artificial intelligence model, the server performs traffic forecasting and energy demand forecasting, as well as anomaly detection, based on historical data. The output is a forecast of the likelihood of future traffic congestion and fluctuations in energy consumption, providing insights for improvement.

[0612] Step 4:

[0613] Based on the AI ​​analysis results, the server generates specific improvement measures using a proposal formulation method. This system formulates measures such as adjusting traffic signals and reducing energy consumption. Based on the prediction results as input, the server outputs measures to adjust signal timing to alleviate congestion and optimize the energy supply and demand balance.

[0614] Step 5:

[0615] The terminal receives suggestions generated from the server and notifies the user. The input is suggestion data from the server. The terminal visualizes these suggestions using an appropriate user interface and outputs them in a format that the user can understand. This allows the user to choose an appropriate action based on the information.

[0616] (Application Example 1)

[0617] 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".

[0618] Modern cities face challenges such as traffic congestion, inefficient energy consumption, environmental degradation, and the need for rapid and effective responses to natural disasters. These issues should not be managed individually but rather considered holistically. However, traditional systems are often fragmented, limiting overall optimization. Furthermore, there is a lack of information to support residents' daily lives.

[0619] 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.

[0620] In this invention, the server includes information acquisition means, information processing means, artificial intelligence processing means, proposal generation means, notification means, and optimization means. This enables the integrated collection and analysis of information related to traffic, energy, environment, and disasters in cities, and allows for optimal urban management that supports the daily activities of residents.

[0621] "Information acquisition means" refers to functions for effectively gathering diverse information related to transportation, energy, the environment, and disasters.

[0622] "Information processing means" refers to methods for formatting acquired information into an analyzable format, removing noise, and imputing missing values.

[0623] "Artificial intelligence processing means" refers to analytical functions that utilize machine learning and deep learning to predict traffic congestion, assess energy demand trends, and evaluate environmental conditions based on pre-processed information.

[0624] The "proposal generation method" is a process that generates improvement measures, such as proposals for optimizing traffic signals or reducing energy consumption, based on the analysis results obtained by the artificial intelligence processing method.

[0625] A "notification means" is a communication system for quickly transmitting generated proposals and analysis results to the user's information device.

[0626] "Optimization methods" refer to techniques that most effectively utilize various proposals related to urban management, such as transportation and energy, in order to support the behavior of residents.

[0627] In order to implement this invention, it is necessary to construct a system equipped with information acquisition means, information processing means, artificial intelligence processing means, proposal generation means, notification means, and optimization means.

[0628] The server collects real-time information related to traffic, energy, the environment, and disasters from various sensors, cameras, GPS devices, energy meters, and environmental sensors installed in the city. This allows for comprehensive monitoring of traffic flow, energy consumption, air quality, weather, earthquakes, and more. The collected information is preprocessed to remove noise and impute missing values, preparing it for analysis. This process uses languages ​​such as Python to clean the data.

[0629] The pre-processed information is input into an artificial intelligence processing system on the server. Here, machine learning libraries such as TensorFlow and PyTorch are used to build predictive models based on historical data, and to predict traffic congestion, energy demand fluctuations, and environmental conditions. Based on the analysis results, the proposal generation system generates improvement proposals such as optimizing traffic signals, reducing energy consumption, and adjusting public infrastructure.

[0630] The generated suggestions are quickly notified to the user's information terminal via a notification system. These notifications are sent using services such as Firebase Cloud Messaging. Furthermore, optimization measures are implemented to optimize the suggestions regarding transportation and energy, thereby supporting residents' actions and enabling more efficient use of these suggestions.

[0631] For example, if a traffic sensor records high traffic volume at 8:00 AM on a particular day, the server analyzes this information and generates improvement measures to adjust the timing of traffic lights at a specific intersection. Similarly, if energy consumption is expected to peak, a notification is sent to encourage the use of renewable energy. In this way, residents receive optimized information on a daily basis, enabling them to efficiently adjust their lives.

[0632] Examples of input prompts for the generating AI model include, "Please tell me the current traffic situation and the forecast for the next hour," and "I would like to know the results of the analysis of today's energy consumption trends." By using these prompts, the system can provide residents with appropriate and timely information based on the complex data of the city.

[0633] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0634] Step 1:

[0635] The server acquires various data in real time from sensors, cameras, GPS devices, energy meters, and environmental sensors installed in the city. The input consists of various sensor data, which is then taken into the server. The server's operation involves acquiring data using communication protocols. The output is raw sensor data.

[0636] Step 2:

[0637] The server preprocesses the acquired raw sensor data. The input is the acquired raw data, and this processing includes noise reduction and missing value imputation. The server performs data cleaning using a Python script. The output is in an analyzable, clean data format.

[0638] Step 3:

[0639] The server passes pre-processed clean data to an artificial intelligence processing system. The input is clean data, and the server uses tools such as TensorFlow or PyTorch to analyze this data. Machine learning models are used to predict traffic, predict energy demand, and assess environmental conditions. The output is the analysis results from the predictive model.

[0640] Step 4:

[0641] The server generates improvement measures using a proposal generation means based on the analysis results obtained by the artificial intelligence processing means. The input is the analysis results, and the proposal generation means uses an algorithm to form traffic signal optimization and energy reduction proposals. The output is the generated improvement proposal.

[0642] Step 5:

[0643] The server sends improvement suggestions to the user's device via a notification system. The input is the improvement suggestion, and the server uses Firebase Cloud Messaging to deliver the notification. The output is the display of the notification on the user's device.

[0644] Step 6:

[0645] The user receives notifications displayed on their device and optimizes their daily activities. The input is the notifications sent to the device, which the user uses to make appropriate lifestyle adjustments, such as adjusting travel time or changing energy usage. The output is the optimized daily activities.

[0646] 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.

[0647] This invention is a system that collects information related to traffic, energy, the environment, and disasters, and further combines it with an emotion engine that recognizes user emotions to provide more effective improvement suggestions. Through the coordinated operation of the server, terminal, and user, the system analyzes this information, generates appropriate suggestions, and notifies users.

[0648] Data acquisition and preprocessing

[0649] The server collects data from various devices within the city. This data includes traffic flow, energy consumption, environmental conditions, weather data, and earthquake information. The server preprocesses this data to make it analyzable. Preprocessing includes denoising and formatting the data to improve its accuracy.

[0650] Emotion recognition by an emotion engine

[0651] The device also collects data about the user. In particular, the emotion engine analyzes user feedback, operation history, and specific input data to infer the user's emotional state. This emotion data is also aggregated on a server and used as material for analysis.

[0652] Data Analysis

[0653] The server performs analysis using pre-processed data and sentiment data. Artificial intelligence processing makes predictions about traffic flow and energy demand, and conducts environmental assessments. Information from the sentiment engine is used to customize suggestions according to the user's emotional state.

[0654] Proposal generation and notification

[0655] Based on the analysis results, the server uses a proposal generation mechanism to create improvement suggestions, including signal control, public transport operation adjustments, and energy utilization optimization. An emotion engine selects a communication method appropriate to the user's emotional state, and notifications are sent to the user via the terminal. For example, if the user is feeling stressed, a gentler notification method is selected.

[0656] As a concrete example, if traffic congestion is expected during the morning commute, the server will suggest optimizing traffic signals and provide appropriate notifications to motivated users. If the system determines that a user is experiencing stress, it will only provide more important information in a calm tone. In this way, the system can guide users to take appropriate actions while reducing their psychological burden.

[0657] Thus, this invention is designed to support the efficient management of cities and the comfortable lives of their residents.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The server collects information from various points in the city, including traffic sensors, energy meters, environmental sensors, and weather station data. This ensures comprehensive coverage of traffic flow, energy consumption, air quality, noise levels, and weather data.

[0661] Step 2:

[0662] The server preprocesses the collected data to prepare it for analysis. By removing noise and imputing missing values, it generates a reliable dataset.

[0663] Step 3:

[0664] The device sends user operation history and feedback data to the emotion engine. The emotion engine uses this information to identify the user's current emotional state. This process uses natural language processing and pattern recognition techniques to evaluate emotions with high accuracy.

[0665] Step 4:

[0666] The server compares pre-processed data with emotional data obtained from the emotion engine and performs analysis using artificial intelligence processing. This analysis is used to predict traffic congestion, analyze energy demand, and assess environmental risks, and to formulate optimal suggestions tailored to the user's emotions.

[0667] Step 5:

[0668] Based on the analysis results, the server uses a proposal generation mechanism to assemble specific improvement suggestions. These include supporting the optimization of traffic signals, adjusting energy use plans, and recommending environmental policies.

[0669] Step 6:

[0670] The server forwards the generated suggestions to the terminal and sends notifications in an appropriate manner. The user's emotional state is taken into consideration; for example, if the user is stressed, the notification will be sent in a gentler tone.

[0671] Step 7:

[0672] Users receive notifications through their devices and take the suggested actions. User feedback is then sent back from the device to the server and used to improve the system's accuracy.

[0673] (Example 2)

[0674] 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".

[0675] In today's urban environment, traffic congestion, wasteful energy consumption, and environmental deterioration are progressing, resulting in a decline in the quality of life for residents. Furthermore, conventional information systems often fail to consider user emotions when providing notifications and suggestions, potentially causing psychological burden on users. The challenge lies in solving these problems and building systems that support efficient urban management and a comfortable life for residents.

[0676] 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.

[0677] In this invention, the server includes information acquisition means for collecting data, information processing means for preprocessing the acquired data and converting it into an analyzable format, and artificial intelligence analysis means for analyzing the preprocessed data to predict traffic flow, calculate energy demand, and evaluate environmental conditions. This makes it possible to improve the efficiency of traffic management, optimize energy consumption, and monitor the environment. Furthermore, by incorporating emotion recognition means for identifying the user's emotional state, suggestions to the user can be made more personalized, reducing psychological burden.

[0678] "Information acquisition means" refers to technical means for collecting information related to traffic, energy, environment, and disasters using various sensors and devices.

[0679] "Information processing means" refers to the technical process of preparing collected raw data into a format suitable for analysis by performing noise reduction and format conversion.

[0680] "Artificial intelligence analysis methods" refer to technologies that apply algorithms and machine learning models to predict traffic flow and energy consumption based on pre-processed data, and to evaluate environmental conditions.

[0681] "Emotion recognition means" refers to technologies that analyze user feedback, operation history, and facial expression data to identify the user's emotional state.

[0682] The "proposal generation method" is a technology that generates improvement suggestions for system users based on the results obtained from artificial intelligence analysis methods and emotion recognition methods.

[0683] "Notification means" refers to technology for transmitting generated improvement suggestions to the user's device and providing information at an appropriate time and in an appropriate manner.

[0684] This system effectively collects and analyzes information related to traffic, energy, environment, and disasters in urban environments, and provides users with appropriate improvement suggestions. The server utilizes sensors and related devices installed in the city to collect various types of data. Specific hardware used includes traffic flow sensors and energy monitors. These devices collect information in a database in real time.

[0685] The device analyzes user feedback and operation history using an emotion engine to infer the user's emotional state. The emotion engine processes the user's text input and facial expression data to understand changes in emotion. This information is transmitted to a server via the internet and used as material for analysis along with other data.

[0686] The server uses preprocessing modules to clean up raw data and format it into an analyzable format. Then, it uses "AIAnalyticsEngineV5.0" to predict traffic flow, energy consumption, and environmental conditions. This enables highly accurate, data-driven predictions through artificial intelligence analysis.

[0687] The proposal generation method uses analysis results and sentiment data to generate improvement suggestions optimized for the user. For example, if traffic congestion is expected, the server proposes adjustments to signal control and transmits the instructions generated by "ProposalGenerator3000" to the terminal.

[0688] As a concrete example, the server uses traffic data from the morning commute to suggest traffic signal optimizations. Furthermore, a generative AI model is used to generate a prompt message that reads, "Please generate a notification message suggesting the optimal commute route based on this morning's traffic data and the user's sentiment."

[0689] Users review suggestions received through their devices and use them to guide their actions. The devices utilize notification methods to display information tailored to the user's situation, and notifications are adjusted according to the user's emotional state. This system is effective in simultaneously supporting efficient urban management and a comfortable life for residents.

[0690] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0691] Step 1:

[0692] The server collects information related to the urban environment from various sensors and devices. Specifically, it acquires traffic flow data from traffic sensors and energy consumption data from energy monitors. It receives this raw data as input and stores the raw data in a database as output. The data processing performed here is limited to data acquisition only.

[0693] Step 2:

[0694] The server preprocesses the raw data. It receives raw data as input and performs data cleansing, such as noise reduction and formatting standardization. Specifically, it performs data interpolation and outlier replacement. The output is clean data converted into an analyzable format.

[0695] Step 3:

[0696] The device collects user feedback and operation history, and uses an emotion engine to infer the user's emotional state. Inputs include user text input, operation logs, and facial expression data. The emotion engine uses this information to perform natural language processing and sentiment analysis, and generates data representing the user's emotional state as output.

[0697] Step 4:

[0698] The server analyzes both pre-processed city data and sentiment data transmitted from terminals. Inputs include clean data and user sentiment data. "AIAnalyticsEngineV5.0" is used to predict traffic patterns and calculate energy needs. Outputs include analysis results, such as prediction results and evaluation information.

[0699] Step 5:

[0700] The server generates proposals based on the analyzed data. The inputs are the analysis results and emotional state data. Using "ProposalGenerator3000," improvement proposals are created, including adjustments to signal control and optimization of the operation schedule. The output is the generated proposal.

[0701] Step 6:

[0702] The server sends the generated suggestions to the terminal, which then notifies the user. The input is the generated suggestions. Using a notification method, the suggestions are displayed on the user's screen. During this process, the tone and method of notification are customized according to the emotional state. The output is the displayed suggestions and any new feedback received from the user.

[0703] (Application Example 2)

[0704] 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".

[0705] In modern urban environments, multiple challenges exist simultaneously, including traffic congestion, energy consumption optimization, environmental protection, and disaster preparedness. Furthermore, providing uniform information and suggestions without considering user emotions can cause unnecessary stress. In this context, there is a need for efficient and user-friendly information provision and suggestions.

[0706] 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.

[0707] In this invention, the server includes information acquisition means for collecting information related to traffic, energy, environment, and disasters; emotion analysis means for recognizing the user's emotions; and suggestion creation means for generating improvement suggestions for the user. This enables efficient operation within the city and reduces the psychological burden on users, while also providing user-friendly suggestions and notifications.

[0708] "Information acquisition means" refers to any device or method for collecting information related to transportation, energy, environment, and disasters.

[0709] "Information processing means" refers to an apparatus or method for preprocessing acquired information and converting it into an analyzable format.

[0710] "Machine learning processing means" refers to a device or method that uses artificial intelligence to analyze pre-processed information and predict or evaluate traffic flow, energy demand, and environmental conditions.

[0711] "Emotional analysis means" refers to a device or method for recognizing and analyzing a user's emotions and using that information as material for the analysis results.

[0712] "Proposal generation means" refers to a device or method for generating improvement suggestions for users based on analysis results and sentiment information.

[0713] "Notification means" refers to a device or method for conveying the generated proposal to the user's information terminal.

[0714] In this invention, the server acquires information related to traffic, energy, environment, and disasters from various sensors and devices within a city. Information acquisition means include remotely accessible sensors and data supply systems via the internet. This information is received by the server, and data preprocessing is performed by information processing means. Preprocessing includes noise reduction and data format conversion.

[0715] The server analyzes pre-processed information using machine learning processing tools. Specifically, it utilizes data analysis libraries using Python (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow, PyTorch). As a result of the analysis, it predicts traffic flow and energy demand, and evaluates environmental conditions.

[0716] The device collects user operation history and feedback, and uses sentiment analysis to infer the user's emotions. For this purpose, it utilizes the smartphone's camera and microphone, and leverages an emotion analysis AI model (e.g., OpenCV, librosa + TensorFlow).

[0717] The server generates improvement suggestions using a suggestion generation system based on information obtained from both machine learning processing and sentiment analysis systems. The generated suggestions are delivered to the user's information terminal via a notification system. The notification method is adjusted according to the results of the sentiment analysis, using gentle language and notifications tailored to the importance of the suggestions.

[0718] For example, if traffic congestion is expected during the morning commute, the server will suggest optimizing the timing of traffic signals and notify the user's smartphone. If the user is experiencing stress, the server will provide more gentle language suggesting ways to use public transportation that day.

[0719] An example of a scenario where a generative AI model is used is the following prompt: "Based on data from today's urban environment, generate suggestions to help user X commute with less stress."

[0720] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0721] Step 1:

[0722] The server collects information related to traffic, energy, environment, and disasters from sensors and devices within the city. It receives raw data from various sensors as input and collects it using information acquisition methods. After standardizing the data format and organizing the acquired information, it constructs a basic dataset for subsequent processing.

[0723] Step 2:

[0724] The server preprocesses the collected information. It receives the raw data acquired in step 1 as input and uses information processing tools to perform noise reduction, imputation of missing values, and removal of outliers. As output, it generates a dataset converted into an analyzable format, preparing it for subsequent analysis.

[0725] Step 3:

[0726] The server analyzes pre-processed data using machine learning methods. It receives a processed dataset as input and uses Python data analysis libraries and machine learning frameworks to predict traffic flow and energy demand, and evaluate environmental conditions. The output includes prediction results and evaluation values. Based on these results, the process moves to the next step.

[0727] Step 4:

[0728] The device performs user emotion analysis. It receives user camera images and audio data as input and uses an emotion analysis AI model within the device to infer emotions. Specifically, it uses OpenCV and librosa to analyze facial expressions and nuances of voice, and generates output that quantifies or categorizes the user's emotional state.

[0729] Step 5:

[0730] The server generates improvement suggestions using a suggestion generation method based on the analysis information obtained by the machine learning processing method and the sentiment analysis method. It receives the analysis results from step 3 and sentiment information from step 4 as input, and generates multiple suggestions using a suggestion generation algorithm. It generates flexible suggestions tailored to the user's emotional state as output, and creates data for notification.

[0731] Step 6:

[0732] The user receives the generated suggestions on their device. The notification system receives the suggestions from the server and guides the user with appropriate language based on their emotional state. For example, if stress is detected, it will be displayed in calm language to ensure the user can receive the information with peace of mind.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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."

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] The following is further disclosed regarding the embodiments described above.

[0755] (Claim 1)

[0756] Data acquisition methods for collecting data related to transportation, energy, environment, and disasters,

[0757] A data processing means that preprocesses the acquired data and converts it into an analyzable format,

[0758] An artificial intelligence processing means analyzes the aforementioned preprocessed data to predict traffic congestion, predict energy demand, and evaluate environmental conditions.

[0759] Based on the analysis results obtained by the artificial intelligence processing means, a proposal generation means generates improvement suggestions for system users,

[0760] A system including a notification means for notifying the user's terminal of the aforementioned proposal.

[0761] (Claim 2)

[0762] The system according to claim 1, wherein the suggestion generation means generates instructions for controlling traffic signals and adjusting the operating schedule of public transport.

[0763] (Claim 3)

[0764] The system according to claim 1, which analyzes weather data and earthquake data and notifies system users of emergency information during a disaster at their terminals.

[0765] "Example 1"

[0766] (Claim 1)

[0767] Information acquisition means for continuously collecting data from urban sensors and location measurement devices,

[0768] A data formatting means for denoising and imputing missing values ​​in the acquired data and converting it into an analyzable data format,

[0769] An artificial intelligence analysis means for performing traffic predictions, energy demand fluctuation predictions, and anomaly detection based on learning from past data, using formatted data as input.

[0770] Based on the results of the artificial intelligence analysis means, a proposal formulation means generates improvement proposals such as traffic signal timing adjustments and energy consumption reduction measures,

[0771] A system including a notification provision means for notifying the user's communication device of the aforementioned proposal.

[0772] (Claim 2)

[0773] The system according to claim 1, wherein the proposal formulation means generates traffic control instructions to alleviate congestion at predicted intersections and instructions for the use of renewable energy to meet peak energy demand.

[0774] (Claim 3)

[0775] The system according to claim 1, which analyzes collected environmental information and disaster information such as earthquakes, and promptly notifies the communication devices of system users of emergency information.

[0776] "Application Example 1"

[0777] (Claim 1)

[0778] Information acquisition methods for collecting information related to transportation, energy, environment, and disasters,

[0779] Information processing means for preprocessing the acquired information and converting it into an analyzable format,

[0780] An artificial intelligence processing means analyzes the pre-processed information and evaluates traffic congestion forecasts, energy demand forecasts, and environmental conditions.

[0781] Based on the analysis results obtained by the artificial intelligence processing means, a proposal generation means generates improvement measures for the user,

[0782] A notification means for notifying the user's information device of the aforementioned proposal,

[0783] An optimization means for optimizing the above proposal in order to carry out urban management to support the actions of residents,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, wherein the suggestion generation means generates instructions for adjusting traffic signals and public transport operation plans.

[0787] (Claim 3)

[0788] The system according to claim 1, which analyzes weather information and earthquake information and notifies the user's information device of emergency instructions during a disaster.

[0789] "Example 2 of combining an emotion engine"

[0790] (Claim 1)

[0791] Information acquisition means for collecting data,

[0792] Information processing means for preprocessing the acquired data and converting it into an analyzable format,

[0793] An artificial intelligence analysis means analyzes the aforementioned preprocessed data to predict traffic flow, calculate energy demand, and evaluate environmental conditions.

[0794] An emotion recognition means for identifying the user's emotional state,

[0795] Based on the information obtained from the artificial intelligence analysis means and emotion recognition means, a proposal creation means generates improvement suggestions for the user,

[0796] A system including a notification means for transmitting the aforementioned proposal to the user's device.

[0797] (Claim 2)

[0798] The system according to claim 1, wherein the proposal generation means generates instructions for controlling traffic signals and adjusting the operating schedule of public transport.

[0799] (Claim 3)

[0800] The system according to claim 1, which analyzes weather information and earthquake information and transmits emergency information to the user's device.

[0801] "Application example 2 when combining with an emotional engine"

[0802] (Claim 1)

[0803] Information acquisition methods for collecting information related to transportation, energy, environment, and disasters,

[0804] Information processing means for preprocessing the acquired information and converting it into an analyzable format,

[0805] A machine learning processing means analyzes the pre-processed information and performs traffic flow prediction, energy demand prediction, and environmental condition evaluation.

[0806] A means of analyzing user emotions,

[0807] Based on the analysis information obtained by the machine learning processing means and the sentiment analysis means, a proposal creation means generates improvement suggestions for the user,

[0808] A system including a notification means for notifying the user's information terminal of the aforementioned proposal.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the proposal generation means generates instructions for adjusting traffic signals and modifying the operating schedules of public transport.

[0811] (Claim 3)

[0812] The system according to claim 1, which analyzes weather information and earthquake information and notifies the user's information terminal of emergency information during a disaster. [Explanation of symbols]

[0813] 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. Data acquisition methods for collecting data related to transportation, energy, environment, and disasters, A data processing means that preprocesses the acquired data and converts it into an analyzable format, An artificial intelligence processing means analyzes the aforementioned preprocessed data to predict traffic congestion, predict energy demand, and evaluate environmental conditions. Based on the analysis results obtained by the artificial intelligence processing means, a proposal generation means generates improvement suggestions for system users, A system including a notification means for notifying the user's terminal of the aforementioned proposal.

2. The system according to claim 1, wherein the suggestion generation means generates instructions for controlling traffic signals and adjusting the operating schedule of public transport.

3. The system according to claim 1, which analyzes weather data and earthquake data and notifies system users of emergency information during a disaster at their terminals.

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