system

Abstract

Provide a system. 【Solution means】 As data collection means, a device that collects urban-related data from the existing systems of local governments, As data storage means, a device that stores the collected data in a database and performs data cleaning and shaping, As data analysis means, a device that provides analysis results of population dynamics and economic trends by using machine learning algorithms, As resident participation means, a device that accepts opinions and questionnaires from residents, As policy proposal means, a device that integrates analysis results and residents' opinions to generate policy proposals, As simulation means, a device that generates urban planning simulations by using VR or AR technology, Means for residents to evaluate policy proposals via VR or AR technology, Means for providing a user interface for residents to participate through smart devices, A system including the above.

Description

Technical Field

[0005] ,

[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, 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 conventional urban planning and policy formation, there is a problem that local governments cannot efficiently utilize the data they possess and lack means to quickly reflect the opinions of residents, making it difficult to make appropriate policy decisions. Also, in areas where vitality has declined due to population decline and aging, in order to formulate effective urban plans within limited budgets, quick and accurate data analysis is necessary, but there is also a problem that this has not been sufficiently carried out.

Means for Solving the Problems

[0005] This invention solves the aforementioned problems by providing a data collection means for collecting urban-related data from existing systems of local governments, a data storage means for storing and cleaning the collected data in a database, a data analysis means for analyzing demographic trends and economic trends using machine learning algorithms, a means for resident participation for receiving residents' opinions and surveys, a policy proposal means for integrating analysis results and residents' opinions to generate policy proposals, and a simulation means for performing urban planning simulations using VR and AR technologies. This makes it possible to realize data-driven policy making and efficient urban planning through resident participation.

[0006] "Data collection means" refers to devices or methods for efficiently acquiring city-related data using existing systems and sensor devices of local governments.

[0007] A "data storage means" is a device or method for storing collected data in a database, cleaning and formatting inaccurate data, and preparing it for analysis.

[0008] "Data analysis means" refers to a device or method that uses machine learning algorithms to analyze data such as demographic trends, economic trends, and traffic patterns, and to generate basic data for policy decision-making.

[0009] "Means of citizen participation" refers to devices or methods for collecting opinions and survey responses from residents and gathering residents' needs and opinions as data.

[0010] A "policy proposal tool" is a device or method for integrating the results of data analysis with feedback from residents to generate new policy proposals or regional revitalization strategies.

[0011] A "simulation means" is a device or method that uses VR or AR technology to test and visualize the impact of policies and urban planning in a virtual space. [Brief explanation of the drawing]

[0012] [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]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention relates to a system that analyzes the current state of a city using existing data and sensor data from local governments, aggregates residents' opinions, and makes efficient policy proposals in order to realize data-driven policy making. The following describes the processing of the system's program in natural language, along with specific examples.

[0034] First, the server periodically collects urban-related data, such as traffic volume and environmental sensor data, from existing local government systems via APIs. This ensures that the latest urban condition information is constantly updated.

[0035] Next, the server cleans the collected data and stores it in a database. This includes a normalization process for time and location information to maintain data consistency. The server then feeds this data into machine learning algorithms to analyze demographics, economic trends, traffic patterns, and more.

[0036] The terminal application is designed for conducting surveys and soliciting opinions from residents. Users can use their smartphones or PCs to input their thoughts and opinions on local issues into the app. These opinions are then immediately sent to the server and processed in real time.

[0037] Subsequently, the server integrates the residents' opinions and analysis results to generate policy proposals. These policy proposals include specific strategies for addressing issues of interest to residents and critical urban challenges.

[0038] Furthermore, the server uses VR and AR technologies to visually simulate the impact of proposed urban plans and policies. These simulation results can be viewed through smartphones and VR devices, helping users visualize what the future city might look like if the policies were implemented.

[0039] As a concrete example, when proposing a new public transport route in a certain area, the server analyzes traffic data to identify peak hours and major traffic flows. Users use a terminal app to provide feedback on their willingness to use the new route. Based on this feedback and the analysis data, the server generates several policy proposals, including the most effective route placement. Users can then evaluate each proposal by viewing a simulation through VR.

[0040] In this way, by combining data collection and analysis, collection and integration of residents' opinions, policy proposals, and their simulations, it is possible to build a system that enables local governments and residents to collaborate in data-driven decision-making and promote concrete and efficient urban planning.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server connects to the local government's data management system and collects the latest city-related data via API. This includes traffic volume data, environmental sensor data, and usage data for public facilities.

[0044] Step 2:

[0045] The server stores the collected data in a database for centralization and cleans up any inconsistent data. Normalization of time and location information is also performed at this stage.

[0046] Step 3:

[0047] The server inputs the accumulated data into machine learning algorithms to analyze demographic changes and economic trends. This allows for a numerical understanding of urban challenges.

[0048] Step 4:

[0049] The terminal app sends notifications to residents regarding surveys and requests for opinions. It is designed to be accessible from smartphones and PCs.

[0050] Step 5:

[0051] Users submit their opinions and suggestions regarding local issues through the application. Submitted opinions are immediately uploaded to the server.

[0052] Step 6:

[0053] The server integrates analysis results and user feedback to automatically generate policy proposals for high-priority issues.

[0054] Step 7:

[0055] The server uses VR and AR technologies to create urban planning simulations based on the proposed policies. This allows users to visually confirm the future impact of the policies.

[0056] Step 8:

[0057] Users can use VR devices or smartphones to view simulation results and intuitively understand how the city would change if the policies were implemented.

[0058] Step 9:

[0059] The device collects additional feedback after the user views the simulation and sends it to the server to be used in the next data analysis.

[0060] (Example 1)

[0061] 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."

[0062] In modern urban management, efficiently utilizing vast amounts of data and formulating policies that reflect residents' opinions is a challenging task. Furthermore, simulating policy effects in advance and providing residents with concrete visuals is also difficult. Traditional methods tend to fragment the processes from data collection and analysis to policy proposal creation and promoting resident participation; therefore, there is a need for methods that realize more integrated and participatory urban management.

[0063] 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.

[0064] In this invention, the server includes data collection means for collecting urban-related information from existing systems of local governments, data storage means for storing the collected information in a database and performing cleaning and format conversion, and data analysis means for analyzing social trends and economic indicators using machine learning methods. This enables integrated data collection and analysis, and facilitates modern urban management that promotes citizen participation through effective policy formulation that reflects citizens' opinions and its visual simulation.

[0065] "Data collection means" refers to a process or device for acquiring city-related information from existing systems and sensor devices of local governments.

[0066] A "data storage means" is a process or device that efficiently stores collected data and prepares it for analysis by performing cleaning and format conversion.

[0067] "Data analysis tools" refer to processes or devices that use machine learning and statistical methods to analyze social trends and economic indicators from urban-related data and extract useful information.

[0068] "Means of citizen participation" refer to processes or mechanisms for collecting opinions and responses from residents and reflecting them in policy-making.

[0069] A "policy proposal tool" is a process or device that combines data analysis results with residents' opinions to generate concrete policy proposals.

[0070] A "simulation method" is a process or device that uses virtual reality or augmented reality technologies to visually reproduce proposed policies or urban plans.

[0071] The system of this invention is designed to support smart urban management by local governments. The following steps illustrate how the invention is specifically implemented.

[0072] The server collects city-related information from existing local government systems and sensor devices via APIs. Specifically, it uses a data collection application running on a cloud platform to periodically acquire traffic volume and environmental sensor data. This platform utilizes publicly available cloud infrastructure (e.g., cloud computing services).

[0073] Next, the server uses Python and its libraries (e.g., pandas and numpy) to clean and format the collected data and store it in a database. This process improves data consistency and availability. A widely used data management system (e.g., a relational database system) is used for the database.

[0074] The terminal functions as an interface for resident participation. Users can easily input and submit their opinions through applications for smartphones and PCs. This application is built using cross-platform development tools such as React Native and Flutter®.

[0075] The server further analyzes various trends using machine learning algorithms (e.g., clustering, regression analysis) for data analysis. It commonly uses libraries such as Python's scikit-learn and TENSORFLOW®. Subsequently, a generative AI model is used to automatically generate policy proposals that integrate opinions and analysis results. Natural language processing techniques are utilized in this process.

[0076] Finally, the server generates simulations of the proposed policies using virtual reality and augmented reality technologies, allowing users to visually experience what the future city will look like through their smartphones or VR devices. This visual information is created using Unity or Unreal Engine.

[0077] A concrete example is the proposal of new public transport routes. The server analyzes real-time traffic data to identify major traffic flows in detail. Users input their opinions indicating their willingness to use the service through a terminal application. Based on the collected opinions and data, the server generates multiple policy proposals, including the optimal route layout.

[0078] An example of a prompt is, "Please explain the methods of traffic data analysis necessary to collect residents' opinions on proposed new public transport routes and propose the optimal route layout." In this way, the system integrates data and residents' opinions to support effective urban management.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server collects urban-related information from existing local government systems and sensor devices using APIs. The input consists of raw log data such as traffic volume and environmental sensor data. The server acquires this data and, instead of simply storing it, prepares it for later processing by cleaning it.

[0082] Step 2:

[0083] The server uses Python's pandas and numpy libraries to clean the collected data. Specifically, it imputes missing values, removes outliers, and standardizes the format to ensure data consistency from different data sources. The input for this step is the raw data before cleaning, and the output is a clean dataset prepared for analysis.

[0084] Step 3:

[0085] The server stores clean data in a database. Specifically, it uses a relational database management system to store clean data. The input is the clean dataset generated in step 2, and the output is the stored data used in subsequent analysis processes.

[0086] Step 4:

[0087] The server performs data analysis. It uses machine learning algorithms to analyze traffic patterns, demographics, and economic trends. It utilizes libraries such as Python's scikit-learn and tensorflow. The input is data stored in a database, and the analysis output provides indicators showing urban trends.

[0088] Step 5:

[0089] The terminal provides an application for conducting surveys and soliciting opinions from residents, who are the users. Users input their opinions on local issues using their smartphones or PCs. This data is transmitted to the server in real time, and the output is a collection of opinion data.

[0090] Step 6:

[0091] The server integrates residents' opinions and analysis results, and uses a generative AI model to generate policy proposals. The input consists of user opinion data and the analysis results from step 4. Based on these, policy proposals are output using natural language processing technology.

[0092] Step 7:

[0093] The server visually simulates policies generated using virtual reality and augmented reality technologies. This is done using platforms such as Unity and Unreal Engine, and users can receive visual information from the simulation via smartphones or VR devices. The input is the policy proposal generated in step 6, and the output is a visual scenario of the policy after implementation.

[0094] (Application Example 1)

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

[0096] In modern cities, the collection and analysis of diverse data are essential for policymaking, but this process is often inefficient, and residents' opinions are frequently not adequately reflected. Furthermore, residents have limited means of understanding and critiquing the consequences of implemented policies beforehand. Therefore, promoting citizen participation and implementing data-driven, efficient policymaking are crucial.

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

[0098] In this invention, the server includes, as a data collection means, means for collecting urban-related data from existing systems of local governments; as a data analysis means, means for providing analysis results of demographics and economic trends using machine learning algorithms; and as a simulation means, means for generating urban planning simulations using VR or AR technology. This enables residents to visually understand the validity of policy proposals and facilitates a more inclusive policy-making process.

[0099] "Data collection methods" refer to technologies for obtaining necessary city-related data from existing systems of local governments.

[0100] A "data storage method" is a technology that centralizes collected information, cleanses and formats it, and then stores it.

[0101] "Data analysis methods" refer to technologies that use machine learning algorithms to analyze demographic trends and economic trends from collected information and provide the results.

[0102] "Methods of citizen participation" refer to technologies that collect opinions and survey responses from residents and incorporate them into the system.

[0103] A "policy proposal tool" is a technology that integrates collected and analyzed data with residents' opinions to generate effective policy proposals.

[0104] "Simulation methods" refer to technologies that use VR or AR technology to visually simulate the impact of urban planning and policies.

[0105] A "smart device" is a portable electronic device that allows residents to access information and participate in activities.

[0106] A "user interface" is the technology that provides the screens and operating methods that residents use when interacting with a system.

[0107] The system of this invention is primarily composed of a server, a terminal application, and user interaction. The server periodically acquires urban-related data, such as traffic volume and environmental data, from existing local government systems using APIs as a data collection means. Sensor devices are also used as needed.

[0108] The acquired data undergoes data cleansing as a data storage method and is stored in a database. Database management systems such as MySQL (registered trademark) are used for data storage. After normalizing the time and location information of the data, various analyses, including demographics and traffic patterns, are performed using machine learning libraries (e.g., TensorFlow).

[0109] The terminal application is designed as a means of citizen participation, allowing users to input their opinions and participate in surveys using smart devices (smartphones, tablets, PCs, etc.) and send them to the server. The input opinions are then integrated with analysis results by the policy proposal system and generated as concrete policy proposals.

[0110] Furthermore, the system incorporates a simulation mechanism that visually recreates urban scenarios using VR / AR technologies such as Unity, to show how policy proposals would unfold if implemented. Users can experience this simulation through VR headsets or AR-enabled devices.

[0111] For example, if there is a proposal for a new public transport route, the server analyzes traffic data to identify major traffic flows. Users can then input their opinions and questions about the proposed route through the application. This allows residents to visually evaluate the validity of the proposal in real time and provide feedback.

[0112] An example of a prompt might be, "Please provide your opinion on the new public transport system in your area. Which route do you think would be most effective?" Users can provide their opinions in response to such prompts, and the results contribute to further optimization of the generative AI model.

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

[0114] Step 1:

[0115] The server collects urban-related data from existing local government systems using APIs. Inputs include traffic volume and environmental sensor data provided by local governments. The server periodically retrieves this data and maintains it as up-to-date information. Outputs are sets of collected urban-related data.

[0116] Step 2:

[0117] The server applies a data cleansing process to the collected data. The input is the raw data obtained from step 1. To maintain the consistency of this data, the server normalizes the time and location information and removes noise. The output is cleansed, well-formed data.

[0118] Step 3:

[0119] The server stores well-formed data in the database system. The input is the cleansed data created in step 2. The output is the data successfully stored in the database. This makes the data available for subsequent analysis.

[0120] Step 4:

[0121] The server performs data analysis using machine learning algorithms. The input is all the data stored in the database. The server utilizes a generative AI model to perform analysis including demographics and economic trends. The output is detailed analysis results.

[0122] Step 5:

[0123] Through a terminal application, users input their opinions and participate in surveys. The input consists of text data (opinions and responses) provided by the user. The terminal application transmits this data to the server in real time. The output is data representing residents' opinions. An example of a prompt is: "Please provide your opinion on the new public transportation system in your area. Which route do you think would be most effective?"

[0124] Step 6:

[0125] The server integrates the analysis results and residents' opinions to generate policy proposals. The inputs are the analysis results from step 4 and the residents' opinions from step 5. The output is the integrated policy proposal, which includes specific policy improvements and multiple policy scenarios.

[0126] Step 7:

[0127] The server uses VR or AR technology to generate simulations of policy proposals. The input is the policy proposal generated in step 6. The output is a visualized simulation. Users can view and evaluate what the future city would look like if the policy were implemented through a VR device.

[0128] 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.

[0129] This invention is a system that incorporates an emotion engine that recognizes user emotions, in addition to analyzing data acquired from existing systems of local governments, in order to conduct data-driven policy making and urban planning. As a result, the emotional aspects of residents are taken into consideration in policy proposals, enabling more comprehensive and effective decision-making.

[0130] First, the server collects city-related data from local government data management systems and sensor devices. This data includes information related to traffic, the environment, and resident activities. After collection, the data is stored in a database and cleaned and normalized. At this stage, the data is prepared in a format suitable for analysis while maintaining consistency.

[0131] Next, the server analyzes the collected data using machine learning algorithms. This reveals demographic trends and economic trends within the city, enabling a better understanding of the situation.

[0132] The terminal provides an interface for collecting opinions and feelings from residents. Residents access surveys and opinion forms using their smartphones or PCs. As users input their opinions, an emotion engine analyzes their facial expressions and voice in real time to recognize their emotional state. For example, positive reactions to suggestions and concerns are identified and taken into consideration.

[0133] The server integrates analysis results, feedback from residents, and sentiment data analyzed by the sentiment engine to generate policy proposals. These proposals, based on data including sentiment information, can respond more sensitively to residents' needs and opinions.

[0134] Furthermore, the server uses VR and AR technology to visually simulate the effects of the proposed policies. This simulation also incorporates emotional information, allowing users to intuitively understand the potential impact of policy implementation on urban life.

[0135] As a concrete example, a user provides feedback on a new commercial facility design plan using a terminal app, and the emotion engine judges that feedback as positive. This emotion data is collected by a server and analyzed together with data from other users with similar opinions. As a result, a policy proposal is generated indicating that the facility plan is likely to be accepted by many residents. This proposal is then presented to the user through a VR simulation, and further feedback is collected.

[0136] In this way, by combining an emotion engine with a series of processes, it is possible to build a system that supports the realization of smart cities through collaboration between local governments and residents.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server uses APIs to collect urban-related data such as traffic volume, environmental sensor data, and public facility usage data from local government data management systems and sensor devices. Once the data is collected, it is automatically sent to a database within the system.

[0140] Step 2:

[0141] The server cleans the data stored in the database to ensure consistency and accuracy. It normalizes time and location information and formats the data into an analyzable format.

[0142] Step 3:

[0143] The server uses machine learning algorithms to analyze the collected data. The analysis results include demographic changes, economic trends, and traffic patterns, which are used as foundational information for policy decisions.

[0144] Step 4:

[0145] The terminal provides an interface for soliciting opinions and feedback from residents. Residents can use applications on their smartphones or PCs to answer surveys and submit their opinions.

[0146] Step 5:

[0147] When a user submits feedback, an emotion engine activates, analyzing the user's emotional state in real time based on their facial expressions and voice. Positive feedback and concerns are identified and stored along with the feedback data.

[0148] Step 6:

[0149] The server integrates residents' opinions, including analyzed sentiment data, with data analysis results to generate policy proposals for specific issues. These proposals are then prioritized and refined based on the residents' sentiment data.

[0150] Step 7:

[0151] The server visualizes the proposed policies using VR and AR technologies and creates urban planning simulations. This visually represents the changes the city would undergo if the proposals were implemented.

[0152] Step 8:

[0153] Users can view the simulation results through a VR device or smartphone and intuitively understand how the proposal will specifically impact the city.

[0154] Step 9:

[0155] The terminal collects additional user feedback after the simulation is confirmed and sends it to the server to be incorporated into the next data collection and analysis cycle.

[0156] (Example 2)

[0157] 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".

[0158] In urban policymaking and planning, conventional data analysis methods often fail to adequately consider the emotional aspects of residents, leading to a disconnect between policies and their needs. Furthermore, a lack of intuitive means to understand the effectiveness of policy proposals hinders effective resident participation.

[0159] 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.

[0160] In this invention, the server includes data collection means, data storage means, and emotion recognition means. This enables the generation of comprehensive policy proposals that take into account the opinions and emotions of residents. Furthermore, by using virtual reality or augmented reality technology, the effects of the policy can be visually simulated, allowing residents to gain an intuitive and concrete understanding of the policy.

[0161] "Data collection methods" refer to technologies for acquiring information about cities from local government information systems and sensor devices.

[0162] "Data storage means" refers to the technology of storing acquired data in a storage device and then cleaning and formatting the data.

[0163] "Data analysis methods" refer to techniques that use machine learning methods to analyze complex data such as demographic trends and economic trends in order to gain specific insights.

[0164] "Means of citizen participation" refers to a system that allows residents to provide opinions and survey information through mobile information terminal applications or other methods.

[0165] "Emotion recognition means" refers to technology that detects the emotional state of residents in real time by analyzing their opinions and expressions.

[0166] "Policy proposal tools" refer to technologies that integrate data analysis results with residents' opinions and sentiments to propose policies that better meet the needs of the residents.

[0167] A "simulation method" is a technique that uses virtual reality or augmented reality technology to visually reproduce urban planning policies and proposals, and to evaluate the effectiveness of those policies in advance.

[0168] Embodiments for carrying out this invention are described below.

[0169] This system provides a technological foundation to support local governments in formulating effective urban policies. First, the server collects urban-related data from the local government's information systems and various sensor devices. For example, it uses APIs to obtain traffic data, environmental data, and resident activity data. The acquired data is stored in a database and cleansed and formatted using the "Pandas" library.

[0170] Next, the server analyzes the collected data using machine learning techniques with "Scikit-learn." In particular, it analyzes data related to demographics and economic trends to understand urban trends. Based on these analysis results, it forms the foundational data for new urban planning.

[0171] The terminal provides an easily accessible interface for residents. Residents can use their portable information terminals to participate in opinion polls and surveys via web applications. The interface is built using "React" and features a user-friendly design. After submitting opinions, the information is analyzed in real time by a sentiment engine.

[0172] When a user enters an opinion, the emotion recognition engine uses natural language processing technology to analyze the content of the opinion and classify the user's emotion as positive, negative, or neutral. For example, "TextBlob" can be used to enable quick emotion determination.

[0173] Subsequently, the server integrates these analysis results with residents' opinions and sentiment data to generate optimal policy proposals. Using a generative AI model, new policy scenarios are created, aiming for higher resident satisfaction by incorporating residents' emotions and opinions into the policies. These policy proposals are visualized using VR and AR technologies. For example, using "Unity" or "Unreal Engine," simulations are created to allow citizens to intuitively understand the impact of the proposed policies. This makes it possible to evaluate the effectiveness of policies in advance and incorporate further resident feedback.

[0174] One concrete example involves generating policy proposals for a new commercial facility. Users answer questionnaires on their mobile devices, and an emotion engine analyzes their opinions as positive. Once this emotion information is collected, it is integrated with the analysis results on a server, and the proposals are visualized using VR technology. Residents can then view the impact of the new facility in a virtual environment and provide additional feedback.

[0175] An example of a prompt would be: "Gather residents' opinions on the construction of a new commercial facility in the city, use a sentiment analysis engine to understand their emotional responses, and then generate policy proposals based on that."

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The server collects urban-related data from local government information systems and sensor devices. Inputs include traffic, environmental, and resident activity data obtained via APIs. This data is stored in a database, and data cleaning, such as imputing missing values ​​and standardizing data formats, is performed using the Pandas library. The output is analyzable, formatted data.

[0179] Step 2:

[0180] The server analyzes the formatted data using machine learning algorithms. The input is the data cleaned in step 1. Using the Scikit-learn library, it performs clustering and regression analysis to discover patterns and trends in the data. The output is the analysis results, including insights into demographics and economic trends.

[0181] Step 3:

[0182] The terminal provides an interface for residents to submit their opinions. Input consists of opinions and feedback entered by users via smartphone or web applications. A React-based interface receives this data and sends it to the backend in real time. Output is raw data for sentiment analysis.

[0183] Step 4:

[0184] The opinions and feedback entered by the user are analyzed by the emotion engine. The input is the opinion data obtained in step 3. Using TextBlob or similar natural language processing tools, emotion analysis is performed to classify the opinions as positive, negative, or neutral. The output is a numerical representation of the user's emotion.

[0185] Step 5:

[0186] The server integrates the analyzed sentiment data with the results of existing urban data analysis. The input is the analysis and sentiment data obtained in steps 2 and 4. The integration process uses a data frame to merge relevant data and generate a consistent dataset for policy proposals. The output is the integrated data required for policy proposals.

[0187] Step 6:

[0188] The server generates policy proposals based on integrated data. The input is the dataset integrated in step 5. A generative AI model is used to create urban policy scenarios. The model proposes policies that reflect the sentiments and opinions of residents. The output is specific policy proposals and their underlying data.

[0189] Step 7:

[0190] The server visualizes the generated policy proposals using VR and AR technology. The input is the policy proposals generated in step 6. A virtual environment is built using Unity or Unreal Engine to simulate the effects of the policies. The output is visual simulation data that residents can experience.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] While a data-driven approach is essential in modern urban planning and policymaking, existing methods fail to adequately consider the emotional aspects of residents, making it difficult to propose effective policies that meet their needs. Furthermore, there is a lack of visual means to understand the effects of policy proposals, hindering efforts to increase resident participation.

[0194] 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.

[0195] In this invention, the server includes means for collecting city-related information from existing systems at the administrative level as a data collection means, means for analyzing residents' facial expressions and voices in real time to recognize their emotional state as an emotion recognition means, and means for visually simulating the effects of generated policy proposals as a virtual visualization means. This makes it possible to make data-driven policy proposals that reflect the emotions of residents, and to make the effects of policies intuitively understandable to residents, thereby promoting their active participation.

[0196] A "data collection method" is a device used to collect city-related information from existing systems at the administrative level.

[0197] A "data storage means" is a device for storing collected information on a recording medium and for organizing and formatting that information.

[0198] A "data analysis tool" is a device that uses machine learning algorithms to provide analysis results on personnel dynamics and social conditions.

[0199] "Means of citizen participation" refer to devices for receiving opinions and survey responses from residents.

[0200] An "emotion recognition device" is a device that analyzes residents' facial expressions and voices in real time to recognize their emotional state.

[0201] A "policy proposal tool" is a device for generating policy proposals by integrating analysis results with residents' opinions and sentiment data.

[0202] A "simulation means" is a device for generating urban planning simulations using virtual reality or augmented reality technology.

[0203] A "virtual visualization tool" is a device for visually simulating the effects of generated policy proposals.

[0204] A specific system for implementing this invention is designed to comprehensively perform data collection, storage, analysis, public opinion gathering, sentiment recognition, policy proposals, and simulations.

[0205] The server collects city-related information from existing systems and sensor devices at the administrative level. The collected data is stored in a database, where it is organized and formatted. For example, traffic flow, environmental data, and records of resident activities are collected. In this process, the server uses a high-performance processor as hardware and a database management system as software (e.g., MySQL).

[0206] The server then applies machine learning algorithms to analyze the accumulated data, extracting trends in personnel dynamics and social conditions. Data analysis libraries (e.g., TensorFlow) are used for this analysis. This enables the prediction of the city's future and the proposal of appropriate policies.

[0207] The terminal provides an interface for collecting opinions and emotional data from residents. Residents use a smartphone application to input their opinions, and their emotions are analyzed in real time through facial expressions and voice. An emotion recognition library (e.g., OpenCV) is used in this process. Specifically, the terminal accurately recognizes the emotional states of residents, such as anxiety and expectations.

[0208] The server synthesizes residents' opinions and sentiment data to generate specific policy proposals. It can utilize virtual reality or augmented reality technology to visually simulate the effects of these policies. This simulation is generated using a VR / AR development platform such as Unity.

[0209] One concrete example is collecting real-time feedback on how residents feel about a new public transport system and integrating it with sentiment data to verify the effectiveness of policy proposals. An example of a prompt might be, "How do you feel about the new urban transport system? Please tell us if you are excited, interested, or anxious, and why." This approach ensures that policy proposals better align with residents' needs and enables more effective urban management.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server collects city-related information from existing systems and sensor devices at the administrative level. Inputs include traffic flow, environmental data, and resident activity. This information is acquired in real time and stored in a database. The database management system used in this process is, for example, MySQL.

[0213] Step 2:

[0214] The server stores the collected data in a database and performs cleaning and formatting. The input is raw data, and the output is formatted, clean data. Data formatting includes removing duplicate data, standardizing the format, and handling missing values.

[0215] Step 3:

[0216] The server analyzes accumulated data using machine learning algorithms to extract demographic and economic trends. The input is formatted data, and the output is the analysis results. Here, data analysis libraries such as TensorFlow are used to extract trends and patterns from the data.

[0217] Step 4:

[0218] The terminal provides an interface for collecting opinion and emotion data from residents. Input consists of opinions and emotions (facial expressions, voice) obtained directly from residents. Output consists of structured opinion data and emotion recognition data. At this stage, a smartphone application and emotion recognition libraries such as OpenCV are used.

[0219] Step 5:

[0220] The server integrates residents' opinion and sentiment data and generates specific policy proposals based on this data. The input is integrated opinion and sentiment data, and the output is policy proposal data. These proposals meticulously reflect the needs of the residents.

[0221] Step 6:

[0222] The server uses virtual reality or augmented reality technology to visually simulate the generated policy proposals. The input is policy proposal data, and the output is a virtually visualized simulation. Using VR / AR development platforms such as Unity, simulations are generated that allow for concrete predictions of the future of cities.

[0223] 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.

[0224] Data generation model 58 is a type of 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention relates to a system that analyzes the current state of a city using existing data and sensor data from local governments, aggregates residents' opinions, and makes efficient policy proposals in order to realize data-driven policy making. The following describes the processing of the system's program in natural language, along with specific examples.

[0240] First, the server periodically collects urban-related data, such as traffic volume and environmental sensor data, from existing local government systems via APIs. This ensures that the latest urban condition information is constantly updated.

[0241] Next, the server cleans the collected data and stores it in a database. This includes a normalization process for time and location information to maintain data consistency. The server then feeds this data into machine learning algorithms to analyze demographics, economic trends, traffic patterns, and more.

[0242] The terminal application is designed for conducting surveys and soliciting opinions from residents. Users can use their smartphones or PCs to input their thoughts and opinions on local issues into the app. These opinions are then immediately sent to the server and processed in real time.

[0243] Subsequently, the server integrates the residents' opinions and analysis results to generate policy proposals. These policy proposals include specific strategies for addressing issues of interest to residents and critical urban challenges.

[0244] Furthermore, the server uses VR and AR technologies to visually simulate the impact of proposed urban plans and policies. These simulation results can be viewed through smartphones and VR devices, helping users visualize what the future city might look like if the policies were implemented.

[0245] As a concrete example, when proposing a new public transport route in a certain area, the server analyzes traffic data to identify peak hours and major traffic flows. Users use a terminal app to provide feedback on their willingness to use the new route. Based on this feedback and the analysis data, the server generates several policy proposals, including the most effective route placement. Users can then evaluate each proposal by viewing a simulation through VR.

[0246] In this way, by combining data collection and analysis, collection and integration of residents' opinions, policy proposals, and their simulations, it is possible to build a system that enables local governments and residents to collaborate in data-driven decision-making and promote concrete and efficient urban planning.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The server connects to the local government's data management system and collects the latest city-related data via API. This includes traffic volume data, environmental sensor data, and usage data for public facilities.

[0250] Step 2:

[0251] The server stores the collected data in a database for centralization and cleans up any inconsistent data. Normalization of time and location information is also performed at this stage.

[0252] Step 3:

[0253] The server inputs the accumulated data into machine learning algorithms to analyze demographic changes and economic trends. This allows for a numerical understanding of urban challenges.

[0254] Step 4:

[0255] The terminal app sends notifications to residents regarding surveys and requests for opinions. It is designed to be accessible from smartphones and PCs.

[0256] Step 5:

[0257] Users submit their opinions and suggestions regarding local issues through the application. Submitted opinions are immediately uploaded to the server.

[0258] Step 6:

[0259] The server integrates analysis results and user feedback to automatically generate policy proposals for high-priority issues.

[0260] Step 7:

[0261] The server uses VR and AR technologies to create urban planning simulations based on the proposed policies. This allows users to visually confirm the future impact of the policies.

[0262] Step 8:

[0263] Users can use VR devices or smartphones to view simulation results and intuitively understand how the city would change if the policies were implemented.

[0264] Step 9:

[0265] The device collects additional feedback after the user views the simulation and sends it to the server to be used in the next data analysis.

[0266] (Example 1)

[0267] 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."

[0268] In modern urban management, efficiently utilizing vast amounts of data and formulating policies that reflect residents' opinions is a challenging task. Furthermore, simulating policy effects in advance and providing residents with concrete visuals is also difficult. Traditional methods tend to fragment the processes from data collection and analysis to policy proposal creation and promoting resident participation; therefore, there is a need for methods that realize more integrated and participatory urban management.

[0269] 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.

[0270] In this invention, the server includes data collection means for collecting urban-related information from existing systems of local governments, data storage means for storing the collected information in a database and performing cleaning and format conversion, and data analysis means for analyzing social trends and economic indicators using machine learning methods. This enables integrated data collection and analysis, and facilitates modern urban management that promotes citizen participation through effective policy formulation that reflects citizens' opinions and its visual simulation.

[0271] "Data collection means" refers to a process or device for acquiring city-related information from existing systems and sensor devices of local governments.

[0272] A "data storage means" is a process or device that efficiently stores collected data and prepares it for analysis by performing cleaning and format conversion.

[0273] "Data analysis tools" refer to processes or devices that use machine learning and statistical methods to analyze social trends and economic indicators from urban-related data and extract useful information.

[0274] "Means of citizen participation" refer to processes or mechanisms for collecting opinions and responses from residents and reflecting them in policy-making.

[0275] A "policy proposal tool" is a process or device that combines data analysis results with residents' opinions to generate concrete policy proposals.

[0276] A "simulation method" is a process or device that uses virtual reality or augmented reality technologies to visually reproduce proposed policies or urban plans.

[0277] The system of this invention is designed to support smart urban management by local governments. The following steps illustrate how the invention is specifically implemented.

[0278] The server collects city-related information from existing local government systems and sensor devices via APIs. Specifically, it uses a data collection application running on a cloud platform to periodically acquire traffic volume and environmental sensor data. This platform utilizes publicly available cloud infrastructure (e.g., cloud computing services).

[0279] Next, the server uses Python and its libraries (e.g., pandas and numpy) to clean and format the collected data and store it in a database. This process improves data consistency and availability. A widely used data management system (e.g., a relational database system) is used for the database.

[0280] The terminal functions as an interface for resident participation. Users can easily input and submit their opinions through applications for smartphones and PCs. This application is built using cross-platform development tools such as React Native and Flutter.

[0281] The server then uses machine learning algorithms (e.g., clustering, regression analysis) for data analysis to analyze various trends. It is common to use libraries such as Python's scikit-learn and tensorflow. Subsequently, a generative AI model is used to automatically generate policy proposals that integrate opinions and analysis results. Natural language processing techniques are utilized in this process.

[0282] Finally, the server generates simulations of the proposed policies using virtual reality and augmented reality technologies, allowing users to visually experience what the future city will look like through their smartphones or VR devices. This visual information is created using Unity or Unreal Engine.

[0283] As a specific example, the proposal of a new public transportation route can be considered. The server analyzes real-time traffic data and identifies the main traffic flows in detail. Users input opinions indicating their intention to use through the terminal app. Based on the collected opinions and data, the server generates multiple policy proposals including the optimal route layout.

[0284] As an example of the prompt sentence, "Please teach me about the method of traffic data analysis necessary to collect the opinions of residents on the proposal of a new public transportation route and propose the optimal route layout." can be cited. In this way, the system integrally handles data and the opinions of residents and supports effective urban management.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The server collects urban-related information using APIs from the existing systems of local governments and sensor devices. The input is raw log data such as traffic volume and environmental sensor data. The server obtains this data and instead of saving the data as it is, prepares to clean the data to make it easier to utilize in later processes.

[0288] Step 2:

[0289] The server uses libraries such as pandas and numpy in Python to clean the collected data. Specifically, it complements missing values, removes outliers, and unifies the format to ensure the consistency of data from different data sources. The input for this step is the raw data before cleaning, and the output obtained is a clean dataset prepared in a state suitable for analysis.

[0290] Step 3:

[0291] The server stores clean data in a database. Specifically, it uses a relational database management system to store clean data. The input is the clean dataset generated in step 2, and the output is the stored data used in subsequent analysis processes.

[0292] Step 4:

[0293] The server performs data analysis. It uses machine learning algorithms to analyze traffic patterns, demographics, and economic trends. It utilizes libraries such as Python's scikit-learn and tensorflow. The input is data stored in a database, and the analysis output provides indicators showing urban trends.

[0294] Step 5:

[0295] The terminal provides an application for conducting surveys and soliciting opinions from residents, who are the users. Users input their opinions on local issues using their smartphones or PCs. This data is transmitted to the server in real time, and the output is a collection of opinion data.

[0296] Step 6:

[0297] The server integrates residents' opinions and analysis results, and uses a generative AI model to generate policy proposals. The input consists of user opinion data and the analysis results from step 4. Based on these, policy proposals are output using natural language processing technology.

[0298] Step 7:

[0299] The server visually simulates policies generated using virtual reality and augmented reality technologies. This is done using platforms such as Unity and Unreal Engine, and users can receive visual information from the simulation via smartphones or VR devices. The input is the policy proposal generated in step 6, and the output is a visual scenario of the policy after implementation.

[0300] (Application Example 1)

[0301] 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."

[0302] In modern cities, the collection and analysis of diverse data are essential for policymaking, but this process is often inefficient, and residents' opinions are frequently not adequately reflected. Furthermore, residents have limited means of understanding and critiquing the consequences of implemented policies beforehand. Therefore, promoting citizen participation and implementing data-driven, efficient policymaking are crucial.

[0303] 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.

[0304] In this invention, the server includes, as a data collection means, means for collecting urban-related data from existing systems of local governments; as a data analysis means, means for providing analysis results of demographics and economic trends using machine learning algorithms; and as a simulation means, means for generating urban planning simulations using VR or AR technology. This enables residents to visually understand the validity of policy proposals and facilitates a more inclusive policy-making process.

[0305] "Data collection methods" refer to technologies for obtaining necessary city-related data from existing systems of local governments.

[0306] "Data storage means" is a technology that unifies the collected information, performs cleansing and shaping, and then stores it.

[0307] "Data analysis means" is a technology that analyzes population dynamics and economic trends from the collected information using machine learning algorithms and provides the results.

[0308] "Resident participation means" is a technology that collects opinions and questionnaires from residents and reflects them in the system.

[0309] "Policy proposal means" is a technology that integrates the collected and analyzed data and the opinions of residents to generate effective policy proposals.

[0310] "Simulation means" is a technology that visually simulates the impact of urban planning and policies using VR or AR technology.

[0311] "Smart device" is a portable electronic device for residents to access and participate in information.

[0312] "User interface" is a technology that provides the screens and operation methods used when residents interact with the system.

[0313] The system of this invention is mainly composed based on the server, terminal application, and user interaction. The server regularly acquires urban-related data such as traffic volume and environmental data from the existing systems of local governments using APIs as data collection means. Sensor devices are also used as needed.

[0314] The acquired data is subjected to data cleansing as data storage means and stored in a database. A database management system such as MySQL is used for data storage. After normalizing time information and location information, various analyses including population dynamics and traffic patterns are performed by data analysis means using a machine learning library (e.g., TensorFlow).

[0315] The terminal application is designed as a means of citizen participation, allowing users to input their opinions and participate in surveys using smart devices (smartphones, tablets, PCs, etc.) and send them to the server. The input opinions are then integrated with analysis results by the policy proposal system and generated as concrete policy proposals.

[0316] Furthermore, the system incorporates a simulation mechanism that visually recreates urban scenarios using VR / AR technologies such as Unity, to show how policy proposals would unfold if implemented. Users can experience this simulation through VR headsets or AR-enabled devices.

[0317] For example, if there is a proposal for a new public transport route, the server analyzes traffic data to identify major traffic flows. Users can then input their opinions and questions about the proposed route through the application. This allows residents to visually evaluate the validity of the proposal in real time and provide feedback.

[0318] An example of a prompt might be, "Please provide your opinion on the new public transport system in your area. Which route do you think would be most effective?" Users can provide their opinions in response to such prompts, and the results contribute to further optimization of the generative AI model.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The server collects urban-related data from existing local government systems using APIs. Inputs include traffic volume and environmental sensor data provided by local governments. The server periodically retrieves this data and maintains it as up-to-date information. Outputs are sets of collected urban-related data.

[0322] Step 2:

[0323] The server applies a data cleansing process to the collected data. The input is the raw data obtained from step 1. To maintain the consistency of this data, the server normalizes the time and location information and removes noise. The output is cleansed, well-formed data.

[0324] Step 3:

[0325] The server stores well-formed data in the database system. The input is the cleansed data created in step 2. The output is the data successfully stored in the database. This makes the data available for subsequent analysis.

[0326] Step 4:

[0327] The server performs data analysis using machine learning algorithms. The input is all the data stored in the database. The server utilizes a generative AI model to perform analysis including demographics and economic trends. The output is detailed analysis results.

[0328] Step 5:

[0329] Through a terminal application, users input their opinions and participate in surveys. The input consists of text data (opinions and responses) provided by the user. The terminal application transmits this data to the server in real time. The output is data representing residents' opinions. An example of a prompt is: "Please provide your opinion on the new public transportation system in your area. Which route do you think would be most effective?"

[0330] Step 6:

[0331] The server integrates the analysis results and residents' opinions to generate policy proposals. The inputs are the analysis results from step 4 and the residents' opinions from step 5. The output is the integrated policy proposal, which includes specific policy improvements and multiple policy scenarios.

[0332] Step 7:

[0333] The server uses VR or AR technology to generate simulations of policy proposals. The input is the policy proposal generated in step 6. The output is a visualized simulation. Users can view and evaluate what the future city would look like if the policy were implemented through a VR device.

[0334] 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.

[0335] This invention is a system that incorporates an emotion engine that recognizes user emotions, in addition to analyzing data acquired from existing systems of local governments, in order to conduct data-driven policy making and urban planning. As a result, the emotional aspects of residents are taken into consideration in policy proposals, enabling more comprehensive and effective decision-making.

[0336] First, the server collects city-related data from local government data management systems and sensor devices. This data includes information related to traffic, the environment, and resident activities. After collection, the data is stored in a database and cleaned and normalized. At this stage, the data is prepared in a format suitable for analysis while maintaining consistency.

[0337] Next, the server analyzes the collected data using machine learning algorithms. This reveals demographic trends and economic trends within the city, enabling a better understanding of the situation.

[0338] The terminal provides an interface for collecting opinions and feelings from residents. Residents access surveys and opinion forms using their smartphones or PCs. As users input their opinions, an emotion engine analyzes their facial expressions and voice in real time to recognize their emotional state. For example, positive reactions to suggestions and concerns are identified and taken into consideration.

[0339] The server integrates analysis results, feedback from residents, and sentiment data analyzed by the sentiment engine to generate policy proposals. These proposals, based on data including sentiment information, can respond more sensitively to residents' needs and opinions.

[0340] Furthermore, the server uses VR and AR technology to visually simulate the effects of the proposed policies. This simulation also incorporates emotional information, allowing users to intuitively understand the potential impact of policy implementation on urban life.

[0341] As a concrete example, a user provides feedback on a new commercial facility design plan using a terminal app, and the emotion engine judges that feedback as positive. This emotion data is collected by a server and analyzed together with data from other users with similar opinions. As a result, a policy proposal is generated indicating that the facility plan is likely to be accepted by many residents. This proposal is then presented to the user through a VR simulation, and further feedback is collected.

[0342] In this way, by combining an emotion engine with a series of processes, it is possible to build a system that supports the realization of smart cities through collaboration between local governments and residents.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The server uses APIs to collect urban-related data such as traffic volume, environmental sensor data, and public facility usage data from local government data management systems and sensor devices. Once the data is collected, it is automatically sent to a database within the system.

[0346] Step 2:

[0347] The server cleans the data stored in the database to ensure consistency and accuracy. It normalizes time and location information and formats the data into an analyzable format.

[0348] Step 3:

[0349] The server uses machine learning algorithms to analyze the collected data. The analysis results include demographic changes, economic trends, and traffic patterns, which are used as foundational information for policy decisions.

[0350] Step 4:

[0351] The terminal provides an interface for soliciting opinions and feedback from residents. Residents can use applications on their smartphones or PCs to answer surveys and submit their opinions.

[0352] Step 5:

[0353] When a user submits feedback, an emotion engine activates, analyzing the user's emotional state in real time based on their facial expressions and voice. Positive feedback and concerns are identified and stored along with the feedback data.

[0354] Step 6:

[0355] The server integrates residents' opinions, including analyzed sentiment data, with data analysis results to generate policy proposals for specific issues. These proposals are then prioritized and refined based on the residents' sentiment data.

[0356] Step 7:

[0357] The server visualizes the proposed policies using VR and AR technologies and creates urban planning simulations. This visually represents the changes the city would undergo if the proposals were implemented.

[0358] Step 8:

[0359] Users can view the simulation results through a VR device or smartphone and intuitively understand how the proposal will specifically impact the city.

[0360] Step 9:

[0361] The terminal collects additional user feedback after the simulation is confirmed and sends it to the server to be incorporated into the next data collection and analysis cycle.

[0362] (Example 2)

[0363] 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".

[0364] In urban policymaking and planning, conventional data analysis methods often fail to adequately consider the emotional aspects of residents, leading to a disconnect between policies and their needs. Furthermore, a lack of intuitive means to understand the effectiveness of policy proposals hinders effective resident participation.

[0365] 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.

[0366] In this invention, the server includes data collection means, data storage means, and emotion recognition means. This enables the generation of comprehensive policy proposals that take into account the opinions and emotions of residents. Furthermore, by using virtual reality or augmented reality technology, the effects of the policy can be visually simulated, allowing residents to gain an intuitive and concrete understanding of the policy.

[0367] "Data collection methods" refer to technologies for acquiring information about cities from local government information systems and sensor devices.

[0368] "Data storage means" refers to the technology of storing acquired data in a storage device and then cleaning and formatting the data.

[0369] "Data analysis methods" refer to techniques that use machine learning methods to analyze complex data such as demographic trends and economic trends in order to gain specific insights.

[0370] "Means of citizen participation" refers to a system that allows residents to provide opinions and survey information through mobile information terminal applications or other methods.

[0371] "Emotion recognition means" refers to technology that detects the emotional state of residents in real time by analyzing their opinions and expressions.

[0372] "Policy proposal tools" refer to technologies that integrate data analysis results with residents' opinions and sentiments to propose policies that better meet the needs of the residents.

[0373] A "simulation method" is a technique that uses virtual reality or augmented reality technology to visually reproduce urban planning policies and proposals, and to evaluate the effectiveness of those policies in advance.

[0374] Embodiments for carrying out this invention are described below.

[0375] This system provides a technological foundation to support local governments in formulating effective urban policies. First, the server collects urban-related data from the local government's information systems and various sensor devices. For example, it uses APIs to obtain traffic data, environmental data, and resident activity data. The acquired data is stored in a database and cleansed and formatted using the "Pandas" library.

[0376] Next, the server analyzes the collected data using machine learning techniques with "Scikit-learn." In particular, it analyzes data related to demographics and economic trends to understand urban trends. Based on these analysis results, it forms the foundational data for new urban planning.

[0377] The terminal provides an easily accessible interface for residents. Residents can use their portable information terminals to participate in opinion polls and surveys via web applications. The interface is built using "React" and features a user-friendly design. After submitting opinions, the information is analyzed in real time by a sentiment engine.

[0378] When a user enters an opinion, the emotion recognition engine uses natural language processing technology to analyze the content of the opinion and classify the user's emotion as positive, negative, or neutral. For example, "TextBlob" can be used to enable quick emotion determination.

[0379] Subsequently, the server integrates these analysis results with residents' opinions and sentiment data to generate optimal policy proposals. Using a generative AI model, new policy scenarios are created, aiming for higher resident satisfaction by incorporating residents' emotions and opinions into the policies. These policy proposals are visualized using VR and AR technologies. For example, using "Unity" or "Unreal Engine," simulations are created to allow citizens to intuitively understand the impact of the proposed policies. This makes it possible to evaluate the effectiveness of policies in advance and incorporate further resident feedback.

[0380] One concrete example involves generating policy proposals for a new commercial facility. Users answer questionnaires on their mobile devices, and an emotion engine analyzes their opinions as positive. Once this emotion information is collected, it is integrated with the analysis results on a server, and the proposals are visualized using VR technology. Residents can then view the impact of the new facility in a virtual environment and provide additional feedback.

[0381] An example of a prompt would be: "Gather residents' opinions on the construction of a new commercial facility in the city, use a sentiment analysis engine to understand their emotional responses, and then generate policy proposals based on that."

[0382] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0383] Step 1:

[0384] The server collects urban-related data from local government information systems and sensor devices. Inputs include traffic, environmental, and resident activity data obtained via APIs. This data is stored in a database, and data cleaning, such as imputing missing values ​​and standardizing data formats, is performed using the Pandas library. The output is analyzable, formatted data.

[0385] Step 2:

[0386] The server analyzes the formatted data using machine learning algorithms. The input is the data cleaned in step 1. Using the Scikit-learn library, it performs clustering and regression analysis to discover patterns and trends in the data. The output is the analysis results, including insights into demographics and economic trends.

[0387] Step 3:

[0388] The terminal provides an interface for residents to submit their opinions. Input consists of opinions and feedback entered by users via smartphone or web applications. A React-based interface receives this data and sends it to the backend in real time. Output is raw data for sentiment analysis.

[0389] Step 4:

[0390] The opinions and feedback entered by the user are analyzed by the emotion engine. The input is the opinion data obtained in step 3. Using TextBlob or similar natural language processing tools, emotion analysis is performed to classify the opinions as positive, negative, or neutral. The output is a numerical representation of the user's emotion.

[0391] Step 5:

[0392] The server integrates the analyzed sentiment data with the results of existing urban data analysis. The input is the analysis and sentiment data obtained in steps 2 and 4. The integration process uses a data frame to merge relevant data and generate a consistent dataset for policy proposals. The output is the integrated data required for policy proposals.

[0393] Step 6:

[0394] The server generates policy proposals based on integrated data. The input is the dataset integrated in step 5. A generative AI model is used to create urban policy scenarios. The model proposes policies that reflect the sentiments and opinions of residents. The output is specific policy proposals and their underlying data.

[0395] Step 7:

[0396] The server visualizes the generated policy proposals using VR and AR technology. The input is the policy proposals generated in step 6. A virtual environment is built using Unity or Unreal Engine to simulate the effects of the policies. The output is visual simulation data that residents can experience.

[0397] (Application Example 2)

[0398] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0399] While a data-driven approach is essential in modern urban planning and policymaking, existing methods fail to adequately consider the emotional aspects of residents, making it difficult to propose effective policies that meet their needs. Furthermore, there is a lack of visual means to understand the effects of policy proposals, hindering efforts to increase resident participation.

[0400] 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.

[0401] In this invention, the server includes means for collecting city-related information from existing systems at the administrative level as a data collection means, means for analyzing residents' facial expressions and voices in real time to recognize their emotional state as an emotion recognition means, and means for visually simulating the effects of generated policy proposals as a virtual visualization means. This makes it possible to make data-driven policy proposals that reflect the emotions of residents, and to make the effects of policies intuitively understandable to residents, thereby promoting their active participation.

[0402] A "data collection method" is a device used to collect city-related information from existing systems at the administrative level.

[0403] A "data storage means" is a device for storing collected information on a recording medium and for organizing and formatting that information.

[0404] A "data analysis tool" is a device that uses machine learning algorithms to provide analysis results on personnel dynamics and social conditions.

[0405] "Means of citizen participation" refer to devices for receiving opinions and survey responses from residents.

[0406] An "emotion recognition device" is a device that analyzes residents' facial expressions and voices in real time to recognize their emotional state.

[0407] A "policy proposal tool" is a device for generating policy proposals by integrating analysis results with residents' opinions and sentiment data.

[0408] A "simulation means" is a device for generating urban planning simulations using virtual reality or augmented reality technology.

[0409] A "virtual visualization tool" is a device for visually simulating the effects of generated policy proposals.

[0410] A specific system for implementing this invention is designed to comprehensively perform data collection, storage, analysis, public opinion gathering, sentiment recognition, policy proposals, and simulations.

[0411] The server collects city-related information from existing systems and sensor devices at the administrative level. The collected data is stored in a database, where it is organized and formatted. For example, traffic flow, environmental data, and records of resident activities are collected. In this process, the server uses a high-performance processor as hardware and a database management system as software (e.g., MySQL).

[0412] The server then applies machine learning algorithms to analyze the accumulated data, extracting trends in personnel dynamics and social conditions. Data analysis libraries (e.g., TensorFlow) are used for this analysis. This enables the prediction of the city's future and the proposal of appropriate policies.

[0413] The terminal provides an interface for collecting opinions and emotional data from residents. Residents use a smartphone application to input their opinions, and their emotions are analyzed in real time through facial expressions and voice. An emotion recognition library (e.g., OpenCV) is used in this process. Specifically, the terminal accurately recognizes the emotional states of residents, such as anxiety and expectations.

[0414] The server synthesizes residents' opinions and sentiment data to generate specific policy proposals. It can utilize virtual reality or augmented reality technology to visually simulate the effects of these policies. This simulation is generated using a VR / AR development platform such as Unity.

[0415] One concrete example is collecting real-time feedback on how residents feel about a new public transport system and integrating it with sentiment data to verify the effectiveness of policy proposals. An example of a prompt might be, "How do you feel about the new urban transport system? Please tell us if you are excited, interested, or anxious, and why." This approach ensures that policy proposals better align with residents' needs and enables more effective urban management.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The server collects city-related information from existing systems and sensor devices at the administrative level. Inputs include traffic flow, environmental data, and resident activity. This information is acquired in real time and stored in a database. The database management system used in this process is, for example, MySQL.

[0419] Step 2:

[0420] The server stores the collected data in a database and performs cleaning and formatting. The input is raw data, and the output is formatted, clean data. Data formatting includes removing duplicate data, standardizing the format, and handling missing values.

[0421] Step 3:

[0422] The server analyzes accumulated data using machine learning algorithms to extract demographic and economic trends. The input is formatted data, and the output is the analysis results. Here, data analysis libraries such as TensorFlow are used to extract trends and patterns from the data.

[0423] Step 4:

[0424] The terminal provides an interface for collecting opinion and emotion data from residents. Input consists of opinions and emotions (facial expressions, voice) obtained directly from residents. Output consists of structured opinion data and emotion recognition data. At this stage, a smartphone application and emotion recognition libraries such as OpenCV are used.

[0425] Step 5:

[0426] The server integrates residents' opinion and sentiment data and generates specific policy proposals based on this data. The input is integrated opinion and sentiment data, and the output is policy proposal data. These proposals meticulously reflect the needs of the residents.

[0427] Step 6:

[0428] The server uses virtual reality or augmented reality technology to visually simulate the generated policy proposals. The input is policy proposal data, and the output is a virtually visualized simulation. Using VR / AR development platforms such as Unity, simulations are generated that allow for concrete predictions of the future of cities.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Third Embodiment]

[0433] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] This invention relates to a system that analyzes the current state of a city using existing data and sensor data from local governments, aggregates residents' opinions, and makes efficient policy proposals in order to realize data-driven policy making. The following describes the processing of the system's program in natural language, along with specific examples.

[0446] First, the server periodically collects urban-related data, such as traffic volume and environmental sensor data, from existing local government systems via APIs. This ensures that the latest urban condition information is constantly updated.

[0447] Next, the server cleans the collected data and stores it in a database. This includes a normalization process for time and location information to maintain data consistency. The server then feeds this data into machine learning algorithms to analyze demographics, economic trends, traffic patterns, and more.

[0448] The terminal application is designed for conducting surveys and soliciting opinions from residents. Users can use their smartphones or PCs to input their thoughts and opinions on local issues into the app. These opinions are then immediately sent to the server and processed in real time.

[0449] Subsequently, the server integrates the residents' opinions and analysis results to generate policy proposals. These policy proposals include specific strategies for addressing issues of interest to residents and critical urban challenges.

[0450] Furthermore, the server uses VR and AR technologies to visually simulate the impact of proposed urban plans and policies. These simulation results can be viewed through smartphones and VR devices, helping users visualize what the future city might look like if the policies were implemented.

[0451] As a concrete example, when proposing a new public transport route in a certain area, the server analyzes traffic data to identify peak hours and major traffic flows. Users use a terminal app to provide feedback on their willingness to use the new route. Based on this feedback and the analysis data, the server generates several policy proposals, including the most effective route placement. Users can then evaluate each proposal by viewing a simulation through VR.

[0452] In this way, by combining data collection and analysis, collection and integration of residents' opinions, policy proposals, and their simulations, it is possible to build a system that enables local governments and residents to collaborate in data-driven decision-making and promote concrete and efficient urban planning.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The server connects to the local government's data management system and collects the latest city-related data via API. This includes traffic volume data, environmental sensor data, and usage data for public facilities.

[0456] Step 2:

[0457] The server stores the collected data in a database for centralization and cleans up any inconsistent data. Normalization of time and location information is also performed at this stage.

[0458] Step 3:

[0459] The server inputs the accumulated data into machine learning algorithms to analyze demographic changes and economic trends. This allows for a numerical understanding of urban challenges.

[0460] Step 4:

[0461] The terminal app sends notifications to residents regarding surveys and requests for opinions. It is designed to be accessible from smartphones and PCs.

[0462] Step 5:

[0463] Users submit their opinions and suggestions regarding local issues through the application. Submitted opinions are immediately uploaded to the server.

[0464] Step 6:

[0465] The server integrates analysis results and user feedback to automatically generate policy proposals for high-priority issues.

[0466] Step 7:

[0467] The server uses VR and AR technologies to create urban planning simulations based on the proposed policies. This allows users to visually confirm the future impact of the policies.

[0468] Step 8:

[0469] Users can use VR devices or smartphones to view simulation results and intuitively understand how the city would change if the policies were implemented.

[0470] Step 9:

[0471] The device collects additional feedback after the user views the simulation and sends it to the server to be used in the next data analysis.

[0472] (Example 1)

[0473] 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."

[0474] In modern urban management, efficiently utilizing vast amounts of data and formulating policies that reflect residents' opinions is a challenging task. Furthermore, simulating policy effects in advance and providing residents with concrete visuals is also difficult. Traditional methods tend to fragment the processes from data collection and analysis to policy proposal creation and promoting resident participation; therefore, there is a need for methods that realize more integrated and participatory urban management.

[0475] 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.

[0476] In this invention, the server includes data collection means for collecting urban-related information from existing systems of local governments, data storage means for storing the collected information in a database and performing cleaning and format conversion, and data analysis means for analyzing social trends and economic indicators using machine learning methods. This enables integrated data collection and analysis, and facilitates modern urban management that promotes citizen participation through effective policy formulation that reflects citizens' opinions and its visual simulation.

[0477] "Data collection means" refers to a process or device for acquiring city-related information from existing systems and sensor devices of local governments.

[0478] A "data storage means" is a process or device that efficiently stores collected data and prepares it for analysis by performing cleaning and format conversion.

[0479] "Data analysis tools" refer to processes or devices that use machine learning and statistical methods to analyze social trends and economic indicators from urban-related data and extract useful information.

[0480] "Means of citizen participation" refer to processes or mechanisms for collecting opinions and responses from residents and reflecting them in policy-making.

[0481] A "policy proposal tool" is a process or device that combines data analysis results with residents' opinions to generate concrete policy proposals.

[0482] A "simulation method" is a process or device that uses virtual reality or augmented reality technologies to visually reproduce proposed policies or urban plans.

[0483] The system of this invention is designed to support smart urban management by local governments. The following steps illustrate how the invention is specifically implemented.

[0484] The server collects city-related information from existing local government systems and sensor devices via APIs. Specifically, it uses a data collection application running on a cloud platform to periodically acquire traffic volume and environmental sensor data. This platform utilizes publicly available cloud infrastructure (e.g., cloud computing services).

[0485] Next, the server uses Python and its libraries (e.g., pandas and numpy) to clean and format the collected data and store it in a database. This process improves data consistency and availability. A widely used data management system (e.g., a relational database system) is used for the database.

[0486] The terminal functions as an interface for resident participation. Users can easily input and submit their opinions through applications for smartphones and PCs. This application is built using cross-platform development tools such as React Native and Flutter.

[0487] The server then uses machine learning algorithms (e.g., clustering, regression analysis) for data analysis to analyze various trends. It is common to use libraries such as Python's scikit-learn and tensorflow. Subsequently, a generative AI model is used to automatically generate policy proposals that integrate opinions and analysis results. Natural language processing techniques are utilized in this process.

[0488] Finally, the server generates simulations of the proposed policies using virtual reality and augmented reality technologies, allowing users to visually experience what the future city will look like through their smartphones or VR devices. This visual information is created using Unity or Unreal Engine.

[0489] A concrete example is the proposal of new public transport routes. The server analyzes real-time traffic data to identify major traffic flows in detail. Users input their opinions indicating their willingness to use the service through a terminal application. Based on the collected opinions and data, the server generates multiple policy proposals, including the optimal route layout.

[0490] An example of a prompt is, "Please explain the methods of traffic data analysis necessary to collect residents' opinions on proposed new public transport routes and propose the optimal route layout." In this way, the system integrates data and residents' opinions to support effective urban management.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The server collects urban-related information from existing local government systems and sensor devices using APIs. The input consists of raw log data such as traffic volume and environmental sensor data. The server acquires this data and, instead of simply storing it, prepares it for later processing by cleaning it.

[0494] Step 2:

[0495] The server uses Python's pandas and numpy libraries to clean the collected data. Specifically, it imputes missing values, removes outliers, and standardizes the format to ensure data consistency from different data sources. The input for this step is the raw data before cleaning, and the output is a clean dataset prepared for analysis.

[0496] Step 3:

[0497] The server stores clean data in a database. Specifically, it uses a relational database management system to store clean data. The input is the clean dataset generated in step 2, and the output is the stored data used in subsequent analysis processes.

[0498] Step 4:

[0499] The server performs data analysis. It uses machine learning algorithms to analyze traffic patterns, demographics, and economic trends. It utilizes libraries such as Python's scikit-learn and tensorflow. The input is data stored in a database, and the analysis output provides indicators showing urban trends.

[0500] Step 5:

[0501] The terminal provides an application for conducting surveys and soliciting opinions from residents, who are the users. Users input their opinions on local issues using their smartphones or PCs. This data is transmitted to the server in real time, and the output is a collection of opinion data.

[0502] Step 6:

[0503] The server integrates residents' opinions and analysis results, and uses a generative AI model to generate policy proposals. The input consists of user opinion data and the analysis results from step 4. Based on these, policy proposals are output using natural language processing technology.

[0504] Step 7:

[0505] The server visually simulates policies generated using virtual reality and augmented reality technologies. This is done using platforms such as Unity and Unreal Engine, and users can receive visual information from the simulation via smartphones or VR devices. The input is the policy proposal generated in step 6, and the output is a visual scenario of the policy after implementation.

[0506] (Application Example 1)

[0507] 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."

[0508] In modern cities, the collection and analysis of diverse data are essential for policymaking, but this process is often inefficient, and residents' opinions are frequently not adequately reflected. Furthermore, residents have limited means of understanding and critiquing the consequences of implemented policies beforehand. Therefore, promoting citizen participation and implementing data-driven, efficient policymaking are crucial.

[0509] 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.

[0510] In this invention, the server includes, as a data collection means, means for collecting urban-related data from existing systems of local governments; as a data analysis means, means for providing analysis results of demographics and economic trends using machine learning algorithms; and as a simulation means, means for generating urban planning simulations using VR or AR technology. This enables residents to visually understand the validity of policy proposals and facilitates a more inclusive policy-making process.

[0511] "Data collection methods" refer to technologies for obtaining necessary city-related data from existing systems of local governments.

[0512] A "data storage method" is a technology that centralizes collected information, cleanses and formats it, and then stores it.

[0513] "Data analysis methods" refer to technologies that use machine learning algorithms to analyze demographic trends and economic trends from collected information and provide the results.

[0514] "Methods of citizen participation" refer to technologies that collect opinions and survey responses from residents and incorporate them into the system.

[0515] A "policy proposal tool" is a technology that integrates collected and analyzed data with residents' opinions to generate effective policy proposals.

[0516] "Simulation methods" refer to technologies that use VR or AR technology to visually simulate the impact of urban planning and policies.

[0517] A "smart device" is a portable electronic device that allows residents to access information and participate in activities.

[0518] A "user interface" is the technology that provides the screens and operating methods that residents use when interacting with a system.

[0519] The system of this invention is primarily composed of a server, a terminal application, and user interaction. The server periodically acquires urban-related data, such as traffic volume and environmental data, from existing local government systems using APIs as a data collection means. Sensor devices are also used as needed.

[0520] The acquired data undergoes data cleansing as a data storage method and is stored in a database. Database management systems such as MySQL are used for data storage. After normalizing the time and location information of the data, various analyses, including demographics and traffic patterns, are performed using machine learning libraries (e.g., TensorFlow).

[0521] The terminal application is designed as a means of citizen participation, allowing users to input their opinions and participate in surveys using smart devices (smartphones, tablets, PCs, etc.) and send them to the server. The input opinions are then integrated with analysis results by the policy proposal system and generated as concrete policy proposals.

[0522] Furthermore, the system incorporates a simulation mechanism that visually recreates urban scenarios using VR / AR technologies such as Unity, to show how policy proposals would unfold if implemented. Users can experience this simulation through VR headsets or AR-enabled devices.

[0523] For example, if there is a proposal for a new public transport route, the server analyzes traffic data to identify major traffic flows. Users can then input their opinions and questions about the proposed route through the application. This allows residents to visually evaluate the validity of the proposal in real time and provide feedback.

[0524] An example of a prompt might be, "Please provide your opinion on the new public transport system in your area. Which route do you think would be most effective?" Users can provide their opinions in response to such prompts, and the results contribute to further optimization of the generative AI model.

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The server collects urban-related data from existing local government systems using APIs. Inputs include traffic volume and environmental sensor data provided by local governments. The server periodically retrieves this data and maintains it as up-to-date information. Outputs are sets of collected urban-related data.

[0528] Step 2:

[0529] The server applies a data cleansing process to the collected data. The input is the raw data obtained from step 1. To maintain the consistency of this data, the server normalizes the time and location information and removes noise. The output is cleansed, well-formed data.

[0530] Step 3:

[0531] The server stores well-formed data in the database system. The input is the cleansed data created in step 2. The output is the data successfully stored in the database. This makes the data available for subsequent analysis.

[0532] Step 4:

[0533] The server performs data analysis using machine learning algorithms. The input is all the data stored in the database. The server utilizes a generative AI model to perform analysis including demographics and economic trends. The output is detailed analysis results.

[0534] Step 5:

[0535] Through a terminal application, users input their opinions and participate in surveys. The input consists of text data (opinions and responses) provided by the user. The terminal application transmits this data to the server in real time. The output is data representing residents' opinions. An example of a prompt is: "Please provide your opinion on the new public transportation system in your area. Which route do you think would be most effective?"

[0536] Step 6:

[0537] The server integrates the analysis results and residents' opinions to generate policy proposals. The inputs are the analysis results from step 4 and the residents' opinions from step 5. The output is the integrated policy proposal, which includes specific policy improvements and multiple policy scenarios.

[0538] Step 7:

[0539] The server uses VR or AR technology to generate simulations of policy proposals. The input is the policy proposal generated in step 6. The output is a visualized simulation. Users can view and evaluate what the future city would look like if the policy were implemented through a VR device.

[0540] 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.

[0541] This invention is a system that incorporates an emotion engine that recognizes user emotions, in addition to analyzing data acquired from existing systems of local governments, in order to conduct data-driven policy making and urban planning. As a result, the emotional aspects of residents are taken into consideration in policy proposals, enabling more comprehensive and effective decision-making.

[0542] First, the server collects city-related data from local government data management systems and sensor devices. This data includes information related to traffic, the environment, and resident activities. After collection, the data is stored in a database and cleaned and normalized. At this stage, the data is prepared in a format suitable for analysis while maintaining consistency.

[0543] Next, the server analyzes the collected data using machine learning algorithms. This reveals demographic trends and economic trends within the city, enabling a better understanding of the situation.

[0544] The terminal provides an interface for collecting opinions and feelings from residents. Residents access surveys and opinion forms using their smartphones or PCs. As users input their opinions, an emotion engine analyzes their facial expressions and voice in real time to recognize their emotional state. For example, positive reactions to suggestions and concerns are identified and taken into consideration.

[0545] The server integrates analysis results, feedback from residents, and sentiment data analyzed by the sentiment engine to generate policy proposals. These proposals, based on data including sentiment information, can respond more sensitively to residents' needs and opinions.

[0546] Furthermore, the server uses VR and AR technology to visually simulate the effects of the proposed policies. This simulation also incorporates emotional information, allowing users to intuitively understand the potential impact of policy implementation on urban life.

[0547] As a concrete example, a user provides feedback on a new commercial facility design plan using a terminal app, and the emotion engine judges that feedback as positive. This emotion data is collected by a server and analyzed together with data from other users with similar opinions. As a result, a policy proposal is generated indicating that the facility plan is likely to be accepted by many residents. This proposal is then presented to the user through a VR simulation, and further feedback is collected.

[0548] In this way, by combining an emotion engine with a series of processes, it is possible to build a system that supports the realization of smart cities through collaboration between local governments and residents.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The server uses APIs to collect urban-related data such as traffic volume, environmental sensor data, and public facility usage data from local government data management systems and sensor devices. Once the data is collected, it is automatically sent to a database within the system.

[0552] Step 2:

[0553] The server cleans the data stored in the database to ensure consistency and accuracy. It normalizes time and location information and formats the data into an analyzable format.

[0554] Step 3:

[0555] The server uses machine learning algorithms to analyze the collected data. The analysis results include demographic changes, economic trends, and traffic patterns, which are used as foundational information for policy decisions.

[0556] Step 4:

[0557] The terminal provides an interface for soliciting opinions and feedback from residents. Residents can use applications on their smartphones or PCs to answer surveys and submit their opinions.

[0558] Step 5:

[0559] When a user submits feedback, an emotion engine activates, analyzing the user's emotional state in real time based on their facial expressions and voice. Positive feedback and concerns are identified and stored along with the feedback data.

[0560] Step 6:

[0561] The server integrates residents' opinions, including analyzed sentiment data, with data analysis results to generate policy proposals for specific issues. These proposals are then prioritized and refined based on the residents' sentiment data.

[0562] Step 7:

[0563] The server visualizes the proposed policies using VR and AR technologies and creates urban planning simulations. This visually represents the changes the city would undergo if the proposals were implemented.

[0564] Step 8:

[0565] Users can view the simulation results through a VR device or smartphone and intuitively understand how the proposal will specifically impact the city.

[0566] Step 9:

[0567] The terminal collects additional user feedback after the simulation is confirmed and sends it to the server to be incorporated into the next data collection and analysis cycle.

[0568] (Example 2)

[0569] 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."

[0570] In urban policymaking and planning, conventional data analysis methods often fail to adequately consider the emotional aspects of residents, leading to a disconnect between policies and their needs. Furthermore, a lack of intuitive means to understand the effectiveness of policy proposals hinders effective resident participation.

[0571] 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.

[0572] In this invention, the server includes data collection means, data storage means, and emotion recognition means. This enables the generation of comprehensive policy proposals that take into account the opinions and emotions of residents. Furthermore, by using virtual reality or augmented reality technology, the effects of the policy can be visually simulated, allowing residents to gain an intuitive and concrete understanding of the policy.

[0573] "Data collection methods" refer to technologies for acquiring information about cities from local government information systems and sensor devices.

[0574] "Data storage means" refers to the technology of storing acquired data in a storage device and then cleaning and formatting the data.

[0575] "Data analysis methods" refer to techniques that use machine learning methods to analyze complex data such as demographic trends and economic trends in order to gain specific insights.

[0576] "Means of citizen participation" refers to a system that allows residents to provide opinions and survey information through mobile information terminal applications or other methods.

[0577] "Emotion recognition means" refers to technology that detects the emotional state of residents in real time by analyzing their opinions and expressions.

[0578] "Policy proposal tools" refer to technologies that integrate data analysis results with residents' opinions and sentiments to propose policies that better meet the needs of the residents.

[0579] A "simulation method" is a technique that uses virtual reality or augmented reality technology to visually reproduce urban planning policies and proposals, and to evaluate the effectiveness of those policies in advance.

[0580] Embodiments for carrying out this invention are described below.

[0581] This system provides a technological foundation to support local governments in formulating effective urban policies. First, the server collects urban-related data from the local government's information systems and various sensor devices. For example, it uses APIs to obtain traffic data, environmental data, and resident activity data. The acquired data is stored in a database and cleansed and formatted using the "Pandas" library.

[0582] Next, the server analyzes the collected data using machine learning techniques with "Scikit-learn." In particular, it analyzes data related to demographics and economic trends to understand urban trends. Based on these analysis results, it forms the foundational data for new urban planning.

[0583] The terminal provides an easily accessible interface for residents. Residents can use their portable information terminals to participate in opinion polls and surveys via web applications. The interface is built using "React" and features a user-friendly design. After submitting opinions, the information is analyzed in real time by a sentiment engine.

[0584] When a user enters an opinion, the emotion recognition engine uses natural language processing technology to analyze the content of the opinion and classify the user's emotion as positive, negative, or neutral. For example, "TextBlob" can be used to enable quick emotion determination.

[0585] Subsequently, the server integrates these analysis results with residents' opinions and sentiment data to generate optimal policy proposals. Using a generative AI model, new policy scenarios are created, aiming for higher resident satisfaction by incorporating residents' emotions and opinions into the policies. These policy proposals are visualized using VR and AR technologies. For example, using "Unity" or "Unreal Engine," simulations are created to allow citizens to intuitively understand the impact of the proposed policies. This makes it possible to evaluate the effectiveness of policies in advance and incorporate further resident feedback.

[0586] One concrete example involves generating policy proposals for a new commercial facility. Users answer questionnaires on their mobile devices, and an emotion engine analyzes their opinions as positive. Once this emotion information is collected, it is integrated with the analysis results on a server, and the proposals are visualized using VR technology. Residents can then view the impact of the new facility in a virtual environment and provide additional feedback.

[0587] An example of a prompt would be: "Gather residents' opinions on the construction of a new commercial facility in the city, use a sentiment analysis engine to understand their emotional responses, and then generate policy proposals based on that."

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The server collects urban-related data from local government information systems and sensor devices. Inputs include traffic, environmental, and resident activity data obtained via APIs. This data is stored in a database, and data cleaning, such as imputing missing values ​​and standardizing data formats, is performed using the Pandas library. The output is analyzable, formatted data.

[0591] Step 2:

[0592] The server analyzes the formatted data using machine learning algorithms. The input is the data cleaned in step 1. Using the Scikit-learn library, it performs clustering and regression analysis to discover patterns and trends in the data. The output is the analysis results, including insights into demographics and economic trends.

[0593] Step 3:

[0594] The terminal provides an interface for residents to submit their opinions. Input consists of opinions and feedback entered by users via smartphone or web applications. A React-based interface receives this data and sends it to the backend in real time. Output is raw data for sentiment analysis.

[0595] Step 4:

[0596] The opinions and feedback entered by the user are analyzed by the emotion engine. The input is the opinion data obtained in step 3. Using TextBlob or similar natural language processing tools, emotion analysis is performed to classify the opinions as positive, negative, or neutral. The output is a numerical representation of the user's emotion.

[0597] Step 5:

[0598] The server integrates the analyzed sentiment data with the results of existing urban data analysis. The input is the analysis and sentiment data obtained in steps 2 and 4. The integration process uses a data frame to merge relevant data and generate a consistent dataset for policy proposals. The output is the integrated data required for policy proposals.

[0599] Step 6:

[0600] The server generates policy proposals based on integrated data. The input is the dataset integrated in step 5. A generative AI model is used to create urban policy scenarios. The model proposes policies that reflect the sentiments and opinions of residents. The output is specific policy proposals and their underlying data.

[0601] Step 7:

[0602] The server visualizes the generated policy proposals using VR and AR technology. The input is the policy proposals generated in step 6. A virtual environment is built using Unity or Unreal Engine to simulate the effects of the policies. The output is visual simulation data that residents can experience.

[0603] (Application Example 2)

[0604] 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."

[0605] While a data-driven approach is essential in modern urban planning and policymaking, existing methods fail to adequately consider the emotional aspects of residents, making it difficult to propose effective policies that meet their needs. Furthermore, there is a lack of visual means to understand the effects of policy proposals, hindering efforts to increase resident participation.

[0606] 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.

[0607] In this invention, the server includes means for collecting city-related information from existing systems at the administrative level as a data collection means, means for analyzing residents' facial expressions and voices in real time to recognize their emotional state as an emotion recognition means, and means for visually simulating the effects of generated policy proposals as a virtual visualization means. This makes it possible to make data-driven policy proposals that reflect the emotions of residents, and to make the effects of policies intuitively understandable to residents, thereby promoting their active participation.

[0608] A "data collection method" is a device used to collect city-related information from existing systems at the administrative level.

[0609] A "data storage means" is a device for storing collected information on a recording medium and for organizing and formatting that information.

[0610] A "data analysis tool" is a device that uses machine learning algorithms to provide analysis results on personnel dynamics and social conditions.

[0611] "Means of citizen participation" refer to devices for receiving opinions and survey responses from residents.

[0612] An "emotion recognition device" is a device that analyzes residents' facial expressions and voices in real time to recognize their emotional state.

[0613] A "policy proposal tool" is a device for generating policy proposals by integrating analysis results with residents' opinions and sentiment data.

[0614] A "simulation means" is a device for generating urban planning simulations using virtual reality or augmented reality technology.

[0615] A "virtual visualization tool" is a device for visually simulating the effects of generated policy proposals.

[0616] A specific system for implementing this invention is designed to comprehensively perform data collection, storage, analysis, public opinion gathering, sentiment recognition, policy proposals, and simulations.

[0617] The server collects city-related information from existing systems and sensor devices at the administrative level. The collected data is stored in a database, where it is organized and formatted. For example, traffic flow, environmental data, and records of resident activities are collected. In this process, the server uses a high-performance processor as hardware and a database management system as software (e.g., MySQL).

[0618] The server then applies machine learning algorithms to analyze the accumulated data, extracting trends in personnel dynamics and social conditions. Data analysis libraries (e.g., TensorFlow) are used for this analysis. This enables the prediction of the city's future and the proposal of appropriate policies.

[0619] The terminal provides an interface for collecting opinions and emotional data from residents. Residents use a smartphone application to input their opinions, and their emotions are analyzed in real time through facial expressions and voice. An emotion recognition library (e.g., OpenCV) is used in this process. Specifically, the terminal accurately recognizes the emotional states of residents, such as anxiety and expectations.

[0620] The server synthesizes residents' opinions and sentiment data to generate specific policy proposals. It can utilize virtual reality or augmented reality technology to visually simulate the effects of these policies. This simulation is generated using a VR / AR development platform such as Unity.

[0621] One concrete example is collecting real-time feedback on how residents feel about a new public transport system and integrating it with sentiment data to verify the effectiveness of policy proposals. An example of a prompt might be, "How do you feel about the new urban transport system? Please tell us if you are excited, interested, or anxious, and why." This approach ensures that policy proposals better align with residents' needs and enables more effective urban management.

[0622] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0623] Step 1:

[0624] The server collects city-related information from existing systems and sensor devices at the administrative level. Inputs include traffic flow, environmental data, and resident activity. This information is acquired in real time and stored in a database. The database management system used in this process is, for example, MySQL.

[0625] Step 2:

[0626] The server stores the collected data in a database and performs cleaning and formatting. The input is raw data, and the output is formatted, clean data. Data formatting includes removing duplicate data, standardizing the format, and handling missing values.

[0627] Step 3:

[0628] The server analyzes accumulated data using machine learning algorithms to extract demographic and economic trends. The input is formatted data, and the output is the analysis results. Here, data analysis libraries such as TensorFlow are used to extract trends and patterns from the data.

[0629] Step 4:

[0630] The terminal provides an interface for collecting opinion and emotion data from residents. Input consists of opinions and emotions (facial expressions, voice) obtained directly from residents. Output consists of structured opinion data and emotion recognition data. At this stage, a smartphone application and emotion recognition libraries such as OpenCV are used.

[0631] Step 5:

[0632] The server integrates residents' opinion and sentiment data and generates specific policy proposals based on this data. The input is integrated opinion and sentiment data, and the output is policy proposal data. These proposals meticulously reflect the needs of the residents.

[0633] Step 6:

[0634] The server uses virtual reality or augmented reality technology to visually simulate the generated policy proposals. The input is policy proposal data, and the output is a virtually visualized simulation. Using VR / AR development platforms such as Unity, simulations are generated that allow for concrete predictions of the future of cities.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] [Fourth Embodiment]

[0639] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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".

[0652] This invention relates to a system that analyzes the current state of a city using existing data and sensor data from local governments, aggregates residents' opinions, and makes efficient policy proposals in order to realize data-driven policy making. The following describes the processing of the system's program in natural language, along with specific examples.

[0653] First, the server periodically collects urban-related data, such as traffic volume and environmental sensor data, from existing local government systems via APIs. This ensures that the latest urban condition information is constantly updated.

[0654] Next, the server cleans the collected data and stores it in a database. This includes a normalization process for time and location information to maintain data consistency. The server then feeds this data into machine learning algorithms to analyze demographics, economic trends, traffic patterns, and more.

[0655] The terminal application is designed for conducting surveys and soliciting opinions from residents. Users can use their smartphones or PCs to input their thoughts and opinions on local issues into the app. These opinions are then immediately sent to the server and processed in real time.

[0656] Subsequently, the server integrates the residents' opinions and analysis results to generate policy proposals. These policy proposals include specific strategies for addressing issues of interest to residents and critical urban challenges.

[0657] Furthermore, the server uses VR and AR technologies to visually simulate the impact of proposed urban plans and policies. These simulation results can be viewed through smartphones and VR devices, helping users visualize what the future city might look like if the policies were implemented.

[0658] As a concrete example, when proposing a new public transport route in a certain area, the server analyzes traffic data to identify peak hours and major traffic flows. Users use a terminal app to provide feedback on their willingness to use the new route. Based on this feedback and the analysis data, the server generates several policy proposals, including the most effective route placement. Users can then evaluate each proposal by viewing a simulation through VR.

[0659] In this way, by combining data collection and analysis, collection and integration of residents' opinions, policy proposals, and their simulations, it is possible to build a system that enables local governments and residents to collaborate in data-driven decision-making and promote concrete and efficient urban planning.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The server connects to the local government's data management system and collects the latest city-related data via API. This includes traffic volume data, environmental sensor data, and usage data for public facilities.

[0663] Step 2:

[0664] The server stores the collected data in a database for centralization and cleans up any inconsistent data. Normalization of time and location information is also performed at this stage.

[0665] Step 3:

[0666] The server inputs the accumulated data into machine learning algorithms to analyze demographic changes and economic trends. This allows for a numerical understanding of urban challenges.

[0667] Step 4:

[0668] The terminal app sends notifications to residents regarding surveys and requests for opinions. It is designed to be accessible from smartphones and PCs.

[0669] Step 5:

[0670] Users submit their opinions and suggestions regarding local issues through the application. Submitted opinions are immediately uploaded to the server.

[0671] Step 6:

[0672] The server integrates analysis results and user feedback to automatically generate policy proposals for high-priority issues.

[0673] Step 7:

[0674] The server uses VR and AR technologies to create urban planning simulations based on the proposed policies. This allows users to visually confirm the future impact of the policies.

[0675] Step 8:

[0676] Users can use VR devices or smartphones to view simulation results and intuitively understand how the city would change if the policies were implemented.

[0677] Step 9:

[0678] The device collects additional feedback after the user views the simulation and sends it to the server to be used in the next data analysis.

[0679] (Example 1)

[0680] 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".

[0681] In modern urban management, efficiently utilizing vast amounts of data and formulating policies that reflect residents' opinions is a challenging task. Furthermore, simulating policy effects in advance and providing residents with concrete visuals is also difficult. Traditional methods tend to fragment the processes from data collection and analysis to policy proposal creation and promoting resident participation; therefore, there is a need for methods that realize more integrated and participatory urban management.

[0682] 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.

[0683] In this invention, the server includes data collection means for collecting urban-related information from existing systems of local governments, data storage means for storing the collected information in a database and performing cleaning and format conversion, and data analysis means for analyzing social trends and economic indicators using machine learning methods. This enables integrated data collection and analysis, and facilitates modern urban management that promotes citizen participation through effective policy formulation that reflects citizens' opinions and its visual simulation.

[0684] "Data collection means" refers to a process or device for acquiring city-related information from existing systems and sensor devices of local governments.

[0685] A "data storage means" is a process or device that efficiently stores collected data and prepares it for analysis by performing cleaning and format conversion.

[0686] "Data analysis tools" refer to processes or devices that use machine learning and statistical methods to analyze social trends and economic indicators from urban-related data and extract useful information.

[0687] "Means of citizen participation" refer to processes or mechanisms for collecting opinions and responses from residents and reflecting them in policy-making.

[0688] A "policy proposal tool" is a process or device that combines data analysis results with residents' opinions to generate concrete policy proposals.

[0689] A "simulation method" is a process or device that uses virtual reality or augmented reality technologies to visually reproduce proposed policies or urban plans.

[0690] The system of this invention is designed to support smart urban management by local governments. The following steps illustrate how the invention is specifically implemented.

[0691] The server collects city-related information from existing local government systems and sensor devices via APIs. Specifically, it uses a data collection application running on a cloud platform to periodically acquire traffic volume and environmental sensor data. This platform utilizes publicly available cloud infrastructure (e.g., cloud computing services).

[0692] Next, the server uses Python and its libraries (e.g., pandas and numpy) to clean and format the collected data and store it in a database. This process improves data consistency and availability. A widely used data management system (e.g., a relational database system) is used for the database.

[0693] The terminal functions as an interface for resident participation. Users can easily input and submit their opinions through applications for smartphones and PCs. This application is built using cross-platform development tools such as React Native and Flutter.

[0694] The server then uses machine learning algorithms (e.g., clustering, regression analysis) for data analysis to analyze various trends. It is common to use libraries such as Python's scikit-learn and tensorflow. Subsequently, a generative AI model is used to automatically generate policy proposals that integrate opinions and analysis results. Natural language processing techniques are utilized in this process.

[0695] Finally, the server generates simulations of the proposed policies using virtual reality and augmented reality technologies, allowing users to visually experience what the future city will look like through their smartphones or VR devices. This visual information is created using Unity or Unreal Engine.

[0696] A concrete example is the proposal of new public transport routes. The server analyzes real-time traffic data to identify major traffic flows in detail. Users input their opinions indicating their willingness to use the service through a terminal application. Based on the collected opinions and data, the server generates multiple policy proposals, including the optimal route layout.

[0697] An example of a prompt is, "Please explain the methods of traffic data analysis necessary to collect residents' opinions on proposed new public transport routes and propose the optimal route layout." In this way, the system integrates data and residents' opinions to support effective urban management.

[0698] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0699] Step 1:

[0700] The server collects urban-related information from existing local government systems and sensor devices using APIs. The input consists of raw log data such as traffic volume and environmental sensor data. The server acquires this data and, instead of simply storing it, prepares it for later processing by cleaning it.

[0701] Step 2:

[0702] The server uses Python's pandas and numpy libraries to clean the collected data. Specifically, it imputes missing values, removes outliers, and standardizes the format to ensure data consistency from different data sources. The input for this step is the raw data before cleaning, and the output is a clean dataset prepared for analysis.

[0703] Step 3:

[0704] The server stores clean data in a database. Specifically, it uses a relational database management system to store clean data. The input is the clean dataset generated in step 2, and the output is the stored data used in subsequent analysis processes.

[0705] Step 4:

[0706] The server performs data analysis. It uses machine learning algorithms to analyze traffic patterns, demographics, and economic trends. It utilizes libraries such as Python's scikit-learn and tensorflow. The input is data stored in a database, and the analysis output provides indicators showing urban trends.

[0707] Step 5:

[0708] The terminal provides an application for conducting surveys and soliciting opinions from residents, who are the users. Users input their opinions on local issues using their smartphones or PCs. This data is transmitted to the server in real time, and the output is a collection of opinion data.

[0709] Step 6:

[0710] The server integrates residents' opinions and analysis results, and uses a generative AI model to generate policy proposals. The input consists of user opinion data and the analysis results from step 4. Based on these, policy proposals are output using natural language processing technology.

[0711] Step 7:

[0712] The server visually simulates policies generated using virtual reality and augmented reality technologies. This is done using platforms such as Unity and Unreal Engine, and users can receive visual information from the simulation via smartphones or VR devices. The input is the policy proposal generated in step 6, and the output is a visual scenario of the policy after implementation.

[0713] (Application Example 1)

[0714] 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".

[0715] In modern cities, the collection and analysis of diverse data are essential for policymaking, but this process is often inefficient, and residents' opinions are frequently not adequately reflected. Furthermore, residents have limited means of understanding and critiquing the consequences of implemented policies beforehand. Therefore, promoting citizen participation and implementing data-driven, efficient policymaking are crucial.

[0716] 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.

[0717] In this invention, the server includes, as a data collection means, means for collecting urban-related data from existing systems of local governments; as a data analysis means, means for providing analysis results of demographics and economic trends using machine learning algorithms; and as a simulation means, means for generating urban planning simulations using VR or AR technology. This enables residents to visually understand the validity of policy proposals and facilitates a more inclusive policy-making process.

[0718] "Data collection methods" refer to technologies for obtaining necessary city-related data from existing systems of local governments.

[0719] A "data storage method" is a technology that centralizes collected information, cleanses and formats it, and then stores it.

[0720] "Data analysis methods" refer to technologies that use machine learning algorithms to analyze demographic trends and economic trends from collected information and provide the results.

[0721] "Methods of citizen participation" refer to technologies that collect opinions and survey responses from residents and incorporate them into the system.

[0722] A "policy proposal tool" is a technology that integrates collected and analyzed data with residents' opinions to generate effective policy proposals.

[0723] "Simulation methods" refer to technologies that use VR or AR technology to visually simulate the impact of urban planning and policies.

[0724] A "smart device" is a portable electronic device that allows residents to access information and participate in activities.

[0725] A "user interface" is the technology that provides the screens and operating methods that residents use when interacting with a system.

[0726] The system of this invention is primarily composed of a server, a terminal application, and user interaction. The server periodically acquires urban-related data, such as traffic volume and environmental data, from existing local government systems using APIs as a data collection means. Sensor devices are also used as needed.

[0727] The acquired data undergoes data cleansing as a data storage method and is stored in a database. Database management systems such as MySQL are used for data storage. After normalizing the time and location information of the data, various analyses, including demographics and traffic patterns, are performed using machine learning libraries (e.g., TensorFlow).

[0728] The terminal application is designed as a means of citizen participation, allowing users to input their opinions and participate in surveys using smart devices (smartphones, tablets, PCs, etc.) and send them to the server. The input opinions are then integrated with analysis results by the policy proposal system and generated as concrete policy proposals.

[0729] Furthermore, the system incorporates a simulation mechanism that visually recreates urban scenarios using VR / AR technologies such as Unity, to show how policy proposals would unfold if implemented. Users can experience this simulation through VR headsets or AR-enabled devices.

[0730] For example, if there is a proposal for a new public transport route, the server analyzes traffic data to identify major traffic flows. Users can then input their opinions and questions about the proposed route through the application. This allows residents to visually evaluate the validity of the proposal in real time and provide feedback.

[0731] An example of a prompt might be, "Please provide your opinion on the new public transport system in your area. Which route do you think would be most effective?" Users can provide their opinions in response to such prompts, and the results contribute to further optimization of the generative AI model.

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] The server collects urban-related data from existing local government systems using APIs. Inputs include traffic volume and environmental sensor data provided by local governments. The server periodically retrieves this data and maintains it as up-to-date information. Outputs are sets of collected urban-related data.

[0735] Step 2:

[0736] The server applies a data cleansing process to the collected data. The input is the raw data obtained from step 1. To maintain the consistency of this data, the server normalizes the time and location information and removes noise. The output is cleansed, well-formed data.

[0737] Step 3:

[0738] The server stores well-formed data in the database system. The input is the cleansed data created in step 2. The output is the data successfully stored in the database. This makes the data available for subsequent analysis.

[0739] Step 4:

[0740] The server performs data analysis using machine learning algorithms. The input is all the data stored in the database. The server utilizes a generative AI model to perform analysis including demographics and economic trends. The output is detailed analysis results.

[0741] Step 5:

[0742] Through a terminal application, users input their opinions and participate in surveys. The input consists of text data (opinions and responses) provided by the user. The terminal application transmits this data to the server in real time. The output is data representing residents' opinions. An example of a prompt is: "Please provide your opinion on the new public transportation system in your area. Which route do you think would be most effective?"

[0743] Step 6:

[0744] The server integrates the analysis results and residents' opinions to generate policy proposals. The inputs are the analysis results from step 4 and the residents' opinions from step 5. The output is the integrated policy proposal, which includes specific policy improvements and multiple policy scenarios.

[0745] Step 7:

[0746] The server uses VR or AR technology to generate simulations of policy proposals. The input is the policy proposal generated in step 6. The output is a visualized simulation. Users can view and evaluate what the future city would look like if the policy were implemented through a VR device.

[0747] 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.

[0748] This invention is a system that incorporates an emotion engine that recognizes user emotions, in addition to analyzing data acquired from existing systems of local governments, in order to conduct data-driven policy making and urban planning. As a result, the emotional aspects of residents are taken into consideration in policy proposals, enabling more comprehensive and effective decision-making.

[0749] First, the server collects city-related data from local government data management systems and sensor devices. This data includes information related to traffic, the environment, and resident activities. After collection, the data is stored in a database and cleaned and normalized. At this stage, the data is prepared in a format suitable for analysis while maintaining consistency.

[0750] Next, the server analyzes the collected data using machine learning algorithms. This reveals demographic trends and economic trends within the city, enabling a better understanding of the situation.

[0751] The terminal provides an interface for collecting opinions and feelings from residents. Residents access surveys and opinion forms using their smartphones or PCs. As users input their opinions, an emotion engine analyzes their facial expressions and voice in real time to recognize their emotional state. For example, positive reactions to suggestions and concerns are identified and taken into consideration.

[0752] The server integrates analysis results, feedback from residents, and sentiment data analyzed by the sentiment engine to generate policy proposals. These proposals, based on data including sentiment information, can respond more sensitively to residents' needs and opinions.

[0753] Furthermore, the server uses VR and AR technology to visually simulate the effects of the proposed policies. This simulation also incorporates emotional information, allowing users to intuitively understand the potential impact of policy implementation on urban life.

[0754] As a concrete example, a user provides feedback on a new commercial facility design plan using a terminal app, and the emotion engine judges that feedback as positive. This emotion data is collected by a server and analyzed together with data from other users with similar opinions. As a result, a policy proposal is generated indicating that the facility plan is likely to be accepted by many residents. This proposal is then presented to the user through a VR simulation, and further feedback is collected.

[0755] In this way, by combining an emotion engine with a series of processes, it is possible to build a system that supports the realization of smart cities through collaboration between local governments and residents.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] The server uses APIs to collect urban-related data such as traffic volume, environmental sensor data, and public facility usage data from local government data management systems and sensor devices. Once the data is collected, it is automatically sent to a database within the system.

[0759] Step 2:

[0760] The server cleans the data stored in the database to ensure consistency and accuracy. It normalizes time and location information and formats the data into an analyzable format.

[0761] Step 3:

[0762] The server uses machine learning algorithms to analyze the collected data. The analysis results include demographic changes, economic trends, and traffic patterns, which are used as foundational information for policy decisions.

[0763] Step 4:

[0764] The terminal provides an interface for soliciting opinions and feedback from residents. Residents can use applications on their smartphones or PCs to answer surveys and submit their opinions.

[0765] Step 5:

[0766] When a user submits feedback, an emotion engine activates, analyzing the user's emotional state in real time based on their facial expressions and voice. Positive feedback and concerns are identified and stored along with the feedback data.

[0767] Step 6:

[0768] The server integrates residents' opinions, including analyzed sentiment data, with data analysis results to generate policy proposals for specific issues. These proposals are then prioritized and refined based on the residents' sentiment data.

[0769] Step 7:

[0770] The server visualizes the proposed policies using VR and AR technologies and creates urban planning simulations. This visually represents the changes the city would undergo if the proposals were implemented.

[0771] Step 8:

[0772] Users can view the simulation results through a VR device or smartphone and intuitively understand how the proposal will specifically impact the city.

[0773] Step 9:

[0774] The terminal collects additional user feedback after the simulation is confirmed and sends it to the server to be incorporated into the next data collection and analysis cycle.

[0775] (Example 2)

[0776] 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".

[0777] In urban policymaking and planning, conventional data analysis methods often fail to adequately consider the emotional aspects of residents, leading to a disconnect between policies and their needs. Furthermore, a lack of intuitive means to understand the effectiveness of policy proposals hinders effective resident participation.

[0778] 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.

[0779] In this invention, the server includes data collection means, data storage means, and emotion recognition means. This enables the generation of comprehensive policy proposals that take into account the opinions and emotions of residents. Furthermore, by using virtual reality or augmented reality technology, the effects of the policy can be visually simulated, allowing residents to gain an intuitive and concrete understanding of the policy.

[0780] "Data collection methods" refer to technologies for acquiring information about cities from local government information systems and sensor devices.

[0781] "Data storage means" refers to the technology of storing acquired data in a storage device and then cleaning and formatting the data.

[0782] "Data analysis methods" refer to techniques that use machine learning methods to analyze complex data such as demographic trends and economic trends in order to gain specific insights.

[0783] "Means of citizen participation" refers to a system that allows residents to provide opinions and survey information through mobile information terminal applications or other methods.

[0784] "Emotion recognition means" refers to technology that detects the emotional state of residents in real time by analyzing their opinions and expressions.

[0785] "Policy proposal tools" refer to technologies that integrate data analysis results with residents' opinions and sentiments to propose policies that better meet the needs of the residents.

[0786] A "simulation method" is a technique that uses virtual reality or augmented reality technology to visually reproduce urban planning policies and proposals, and to evaluate the effectiveness of those policies in advance.

[0787] Embodiments for carrying out this invention are described below.

[0788] This system provides a technological foundation to support local governments in formulating effective urban policies. First, the server collects urban-related data from the local government's information systems and various sensor devices. For example, it uses APIs to obtain traffic data, environmental data, and resident activity data. The acquired data is stored in a database and cleansed and formatted using the "Pandas" library.

[0789] Next, the server analyzes the collected data using machine learning techniques with "Scikit-learn." In particular, it analyzes data related to demographics and economic trends to understand urban trends. Based on these analysis results, it forms the foundational data for new urban planning.

[0790] The terminal provides an easily accessible interface for residents. Residents can use their portable information terminals to participate in opinion polls and surveys via web applications. The interface is built using "React" and features a user-friendly design. After submitting opinions, the information is analyzed in real time by a sentiment engine.

[0791] When a user enters an opinion, the emotion recognition engine uses natural language processing technology to analyze the content of the opinion and classify the user's emotion as positive, negative, or neutral. For example, "TextBlob" can be used to enable quick emotion determination.

[0792] Subsequently, the server integrates these analysis results with residents' opinions and sentiment data to generate optimal policy proposals. Using a generative AI model, new policy scenarios are created, aiming for higher resident satisfaction by incorporating residents' emotions and opinions into the policies. These policy proposals are visualized using VR and AR technologies. For example, using "Unity" or "Unreal Engine," simulations are created to allow citizens to intuitively understand the impact of the proposed policies. This makes it possible to evaluate the effectiveness of policies in advance and incorporate further resident feedback.

[0793] One concrete example involves generating policy proposals for a new commercial facility. Users answer questionnaires on their mobile devices, and an emotion engine analyzes their opinions as positive. Once this emotion information is collected, it is integrated with the analysis results on a server, and the proposals are visualized using VR technology. Residents can then view the impact of the new facility in a virtual environment and provide additional feedback.

[0794] An example of a prompt would be: "Gather residents' opinions on the construction of a new commercial facility in the city, use a sentiment analysis engine to understand their emotional responses, and then generate policy proposals based on that."

[0795] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0796] Step 1:

[0797] The server collects urban-related data from local government information systems and sensor devices. Inputs include traffic, environmental, and resident activity data obtained via APIs. This data is stored in a database, and data cleaning, such as imputing missing values ​​and standardizing data formats, is performed using the Pandas library. The output is analyzable, formatted data.

[0798] Step 2:

[0799] The server analyzes the formatted data using machine learning algorithms. The input is the data cleaned in step 1. Using the Scikit-learn library, it performs clustering and regression analysis to discover patterns and trends in the data. The output is the analysis results, including insights into demographics and economic trends.

[0800] Step 3:

[0801] The terminal provides an interface for residents to submit their opinions. Input consists of opinions and feedback entered by users via smartphone or web applications. A React-based interface receives this data and sends it to the backend in real time. Output is raw data for sentiment analysis.

[0802] Step 4:

[0803] The opinions and feedback entered by the user are analyzed by the emotion engine. The input is the opinion data obtained in step 3. Using TextBlob or similar natural language processing tools, emotion analysis is performed to classify the opinions as positive, negative, or neutral. The output is a numerical representation of the user's emotion.

[0804] Step 5:

[0805] The server integrates the analyzed sentiment data with the results of existing urban data analysis. The input is the analysis and sentiment data obtained in steps 2 and 4. The integration process uses a data frame to merge relevant data and generate a consistent dataset for policy proposals. The output is the integrated data required for policy proposals.

[0806] Step 6:

[0807] The server generates policy proposals based on integrated data. The input is the dataset integrated in step 5. A generative AI model is used to create urban policy scenarios. The model proposes policies that reflect the sentiments and opinions of residents. The output is specific policy proposals and their underlying data.

[0808] Step 7:

[0809] The server visualizes the generated policy proposals using VR and AR technology. The input is the policy proposals generated in step 6. A virtual environment is built using Unity or Unreal Engine to simulate the effects of the policies. The output is visual simulation data that residents can experience.

[0810] (Application Example 2)

[0811] 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".

[0812] While a data-driven approach is essential in modern urban planning and policymaking, existing methods fail to adequately consider the emotional aspects of residents, making it difficult to propose effective policies that meet their needs. Furthermore, there is a lack of visual means to understand the effects of policy proposals, hindering efforts to increase resident participation.

[0813] 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.

[0814] In this invention, the server includes means for collecting city-related information from existing systems at the administrative level as a data collection means, means for analyzing residents' facial expressions and voices in real time to recognize their emotional state as an emotion recognition means, and means for visually simulating the effects of generated policy proposals as a virtual visualization means. This makes it possible to make data-driven policy proposals that reflect the emotions of residents, and to make the effects of policies intuitively understandable to residents, thereby promoting their active participation.

[0815] A "data collection method" is a device used to collect city-related information from existing systems at the administrative level.

[0816] A "data storage means" is a device for storing collected information on a recording medium and for organizing and formatting that information.

[0817] A "data analysis tool" is a device that uses machine learning algorithms to provide analysis results on personnel dynamics and social conditions.

[0818] "Means of citizen participation" refer to devices for receiving opinions and survey responses from residents.

[0819] An "emotion recognition device" is a device that analyzes residents' facial expressions and voices in real time to recognize their emotional state.

[0820] A "policy proposal tool" is a device for generating policy proposals by integrating analysis results with residents' opinions and sentiment data.

[0821] A "simulation means" is a device for generating urban planning simulations using virtual reality or augmented reality technology.

[0822] A "virtual visualization tool" is a device for visually simulating the effects of generated policy proposals.

[0823] A specific system for implementing this invention is designed to comprehensively perform data collection, storage, analysis, public opinion gathering, sentiment recognition, policy proposals, and simulations.

[0824] The server collects city-related information from existing systems and sensor devices at the administrative level. The collected data is stored in a database, where it is organized and formatted. For example, traffic flow, environmental data, and records of resident activities are collected. In this process, the server uses a high-performance processor as hardware and a database management system as software (e.g., MySQL).

[0825] The server then applies machine learning algorithms to analyze the accumulated data, extracting trends in personnel dynamics and social conditions. Data analysis libraries (e.g., TensorFlow) are used for this analysis. This enables the prediction of the city's future and the proposal of appropriate policies.

[0826] The terminal provides an interface for collecting opinions and emotional data from residents. Residents use a smartphone application to input their opinions, and their emotions are analyzed in real time through facial expressions and voice. An emotion recognition library (e.g., OpenCV) is used in this process. Specifically, the terminal accurately recognizes the emotional states of residents, such as anxiety and expectations.

[0827] The server synthesizes residents' opinions and sentiment data to generate specific policy proposals. It can utilize virtual reality or augmented reality technology to visually simulate the effects of these policies. This simulation is generated using a VR / AR development platform such as Unity.

[0828] One concrete example is collecting real-time feedback on how residents feel about a new public transport system and integrating it with sentiment data to verify the effectiveness of policy proposals. An example of a prompt might be, "How do you feel about the new urban transport system? Please tell us if you are excited, interested, or anxious, and why." This approach ensures that policy proposals better align with residents' needs and enables more effective urban management.

[0829] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0830] Step 1:

[0831] The server collects city-related information from existing systems and sensor devices at the administrative level. Inputs include traffic flow, environmental data, and resident activity. This information is acquired in real time and stored in a database. The database management system used in this process is, for example, MySQL.

[0832] Step 2:

[0833] The server stores the collected data in a database and performs cleaning and formatting. The input is raw data, and the output is formatted, clean data. Data formatting includes removing duplicate data, standardizing the format, and handling missing values.

[0834] Step 3:

[0835] The server analyzes accumulated data using machine learning algorithms to extract demographic and economic trends. The input is formatted data, and the output is the analysis results. Here, data analysis libraries such as TensorFlow are used to extract trends and patterns from the data.

[0836] Step 4:

[0837] The terminal provides an interface for collecting opinion and emotion data from residents. Input consists of opinions and emotions (facial expressions, voice) obtained directly from residents. Output consists of structured opinion data and emotion recognition data. At this stage, a smartphone application and emotion recognition libraries such as OpenCV are used.

[0838] Step 5:

[0839] The server integrates residents' opinion and sentiment data and generates specific policy proposals based on this data. The input is integrated opinion and sentiment data, and the output is policy proposal data. These proposals meticulously reflect the needs of the residents.

[0840] Step 6:

[0841] The server uses virtual reality or augmented reality technology to visually simulate the generated policy proposals. The input is policy proposal data, and the output is a virtually visualized simulation. Using VR / AR development platforms such as Unity, simulations are generated that allow for concrete predictions of the future of cities.

[0842] 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.

[0843] 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.

[0844] 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 robot 414.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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."

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] The following is further disclosed regarding the embodiments described above.

[0864] (Claim 1)

[0865] As a means of data collection, a device for collecting city-related data from existing systems of local governments,

[0866] As a means of data storage, the device stores the collected data in a database and performs data cleaning and formatting.

[0867] A device that provides analysis results of demographics and economic trends by using machine learning algorithms as a data analysis method,

[0868] As a means of resident participation, a device for receiving opinions and surveys from residents,

[0869] As a means of proposing policies, a device that integrates analysis results and residents' opinions to generate policy proposals,

[0870] As a simulation method, a device that generates urban planning simulations using VR or AR technology,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, further comprising means for using a sensor device as a means for data collection.

[0874] (Claim 3)

[0875] The system according to claim 1, comprising means of using a smartphone application as a means of resident participation.

[0876] "Example 1"

[0877] (Claim 1)

[0878] A data collection method for collecting city-related information from existing systems of local governments,

[0879] A data storage means that stores collected information in a database and performs cleaning and format conversion,

[0880] A data analysis method that uses machine learning techniques to analyze social trends and economic indicators,

[0881] A means of citizen participation to collect opinions and responses from citizens,

[0882] A policy proposal tool that integrates analysis results and citizens' opinions to create policy proposals,

[0883] A simulation means that provides predictive simulations of urban planning using virtual reality or augmented reality technology,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, further comprising means for using an information collection device as a means for data collection.

[0887] (Claim 3)

[0888] The system according to claim 1, comprising means for using portable device application software as a means of resident participation.

[0889] "Application Example 1"

[0890] (Claim 1)

[0891] As a means of data collection, a device for collecting city-related data from existing systems of local governments,

[0892] As a means of data storage, the device stores the collected data in a database and performs data cleaning and formatting.

[0893] A device that provides analysis results of demographics and economic trends by using machine learning algorithms as a data analysis method,

[0894] As a means of resident participation, a device for receiving opinions and surveys from residents,

[0895] As a means of proposing policies, a device that integrates analysis results and residents' opinions to generate policy proposals,

[0896] As a simulation method, a device that generates urban planning simulations using VR or AR technology,

[0897] A means for residents to evaluate policy proposals through VR or AR technology,

[0898] A means of providing a user interface for residents to participate through smart devices,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, comprising means for collecting traffic and environmental data using sensor devices.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising means for collecting opinions from residents using a smart device application and presenting the results of a policy proposal simulation.

[0904] "Example 2 of combining an emotion engine"

[0905] (Claim 1)

[0906] As a means of data collection, there is a means of obtaining information about cities from the information systems of local governments,

[0907] As a means of data storage, the acquired data is stored in a storage device, and means are used to organize and format the data.

[0908] As a data analysis method, it provides a means of providing analytical information on demographics and economic trends using machine learning techniques.

[0909] As a means of resident participation, there are methods for receiving opinions and survey information from residents,

[0910] As a means of recognizing emotions, a means of analyzing residents' opinions and expressions to detect their emotional state,

[0911] As a means of policy proposal, a means of generating policy proposals by integrating analytical information, residents' opinions, and sentiment information,

[0912] As a simulation means, a means for generating urban planning simulations using virtual reality or augmented reality technology,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, comprising means for collecting data using a sensor device.

[0916] (Claim 3)

[0917] The system according to claim 1, further comprising means for using a portable information terminal application as a means of resident participation.

[0918] "Application example 2 when combining with an emotional engine"

[0919] (Claim 1)

[0920] As a means of data collection, a device for collecting city-related information from existing systems at the administrative level,

[0921] As a means of data storage, the device stores collected information on a recording medium and organizes and formats the information.

[0922] A device that provides analysis results of personnel dynamics and social conditions by using machine learning algorithms as a data analysis method,

[0923] As a means of resident participation, a device for receiving opinions and surveys from residents,

[0924] As a means of recognizing emotions, a device is used that analyzes residents' facial expressions and voices in real time to recognize their emotional state.

[0925] As a means of proposing policies, a device that generates policy proposals by integrating analysis results with residents' opinions and sentiment data,

[0926] As a simulation means, a device that generates urban planning simulations using virtual reality or augmented reality technology,

[0927] As a means of virtual visualization, a device is used to visually simulate the effects of the generated policy proposals,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, comprising means for collecting information using a sensor device.

[0931] (Claim 3)

[0932] The system according to claim 1, comprising means for collecting residents' opinions using a mobile information terminal application. [Explanation of Symbols]

[0933] 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. As a means of data collection, a device for collecting city-related data from existing systems of local governments, As a means of data storage, the device stores the collected data in a database and performs data cleaning and formatting. A device that provides analysis results of demographics and economic trends by using machine learning algorithms as a data analysis method, As a means of resident participation, a device for receiving opinions and surveys from residents, As a means of proposing policies, a device that integrates analysis results and residents' opinions to generate policy proposals, As a simulation method, a device that generates urban planning simulations using VR or AR technology, A means for residents to evaluate policy proposals through VR or AR technology, A means of providing a user interface for residents to participate through smart devices, A system that includes this.

2. The system according to claim 1, comprising means for collecting traffic and environmental data using sensor devices.

3. The system according to claim 1, comprising means for collecting opinions from residents using a smart device application and presenting the results of a policy proposal simulation.

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