system

Abstract

We provide the system. [Solution] A means of collecting data from multiple data sources related to cities, A preprocessing means for preprocessing the collected data, An analytical method for analyzing pre-processed data to identify urban challenges and trends, A simulation method for simulating different scenarios in urban development and predicting their impact, An optimization method that proposes the optimal resource allocation based on simulation results, Means of participation that allow citizens to provide feedback on urban development plans, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to achieve sustainable urban development, appropriate data-based urban planning is required for various issues faced by cities, such as population decline, aging, infrastructure deterioration, financial constraints, and environmental problems. However, conventional urban planning often relies on limited data and intuition based on human experience, and as a result, plans that are not optimal may sometimes be formulated. There is a need to establish a method to solve these problems and conduct more effective and efficient urban planning.

Means for Solving the Problems

[0005] This invention provides a data collection means for automatically collecting data from multiple data sources related to a city. The collected data is processed in a consistent manner by a preprocessing means, and then urban challenges and trends are identified by an analysis means using a machine learning model. Furthermore, different scenarios in urban development are evaluated using a simulation means, and efficient resource allocation is proposed by an optimization means. In addition, a participation means is provided for collecting feedback from citizens, thereby improving the transparency and inclusiveness of urban planning. This series of means makes it possible to formulate a data-driven and citizen-participatory sustainable urban development plan.

[0006] "Data collection means" refers to a function that automatically acquires necessary data from various data sources related to the city.

[0007] "Preprocessing means" refers to a function that verifies the integrity of collected data and performs tasks such as imputing missing values ​​and correcting outliers.

[0008] "Analysis tools" refer to functions that use machine learning models to identify urban challenges and trends based on pre-processed data.

[0009] A "simulation tool" is a function that virtually executes different urban development scenarios and predicts their effects.

[0010] An "optimization tool" is a function that proposes an efficient resource allocation based on simulation results.

[0011] "Means of participation" refers to functions that allow citizens to provide feedback on urban planning proposals. [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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

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

[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, and the like.

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

[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] The AI ​​urban planning system according to the present invention consists of multiple components for collecting various urban-related data and making proposals for sustainable urban development. This system operates according to the following procedure, with the server, terminals, and users each playing their respective roles.

[0034] Data collection

[0035] The server collects data from various data sources within the city, such as traffic sensors, demographic databases, and public institution APIs. This data is updated daily or at a specified frequency and stored in the database.

[0036] Data preprocessing

[0037] The server performs preprocessing to maintain the integrity of the collected data. Specifically, it generates a clean dataset by imputing missing values ​​and removing outliers.

[0038] Data Analysis

[0039] The device performs analysis using pre-processed data. Here, machine learning models are applied to analyze urban demographics and traffic patterns. For example, it can identify population growth trends and traffic congestion patterns in specific areas.

[0040] Running the simulation

[0041] The server uses simulation tools to run different urban development scenarios. For example, it can virtually predict the impact of constructing a new train station on local traffic patterns.

[0042] Optimization of resource allocation

[0043] Based on the simulation results, the terminal proposes efficient resource allocation using optimization methods. For example, it optimizes the location of public facilities and develops a layout plan that maximizes citizen convenience and cost efficiency.

[0044] Promoting citizen participation

[0045] Users can provide feedback on the proposed plan through a citizen participation platform. This user feedback is collected by the system and considered in future plan updates. For example, a user might post an opinion about the need for a new public transport route, and that opinion might be reflected in the plan.

[0046] The system's operation, as described above, enables the realization of more precise and efficient urban development plans. This system employs a data-driven approach and, by utilizing AI technology, is expected to contribute to solving complex urban challenges.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects data from various data sources within the city. Specifically, it obtains real-time traffic volume data from public transport APIs and the latest demographic information from demographic databases. This data is then cleansed and stored in the database.

[0050] Step 2:

[0051] The server performs preprocessing on the collected data. Specifically, it identifies missing values ​​in the data and fills them in using appropriate imputation methods (e.g., imputing the mean or median). It also detects and removes or corrects outliers caused by sensor failures or other issues.

[0052] Step 3:

[0053] The terminal begins analysis using pre-processed data. It applies machine learning algorithms to cluster urban population trends and traffic patterns. For example, it identifies traffic congestion patterns by time of day in a specific area and predicts future traffic congestion locations.

[0054] Step 4:

[0055] The server performs simulations based on the analysis results. It sets up various urban development scenarios and predicts their effects. As a specific example, it models the changes in pedestrian flow to surrounding areas when a new railway station is opened.

[0056] Step 5:

[0057] The terminal receives the simulation results and performs optimization processing to determine efficient resource allocation. Using a genetic algorithm, it calculates the placement of public facilities while considering cost-effectiveness and formulates the optimal resource allocation plan.

[0058] Step 6:

[0059] Users input their opinions and feedback on presented urban planning proposals through a citizen participation platform. This feedback is later analyzed and reflected in future urban development proposals, making it possible to incorporate the opinions of local residents into the planning process.

[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] Modern cities face a variety of complex challenges, including rapid population growth, traffic congestion, and environmental impacts. To comprehensively and efficiently address these challenges, sustainable development plans are needed that collect and analyze vast amounts of data and incorporate residents' opinions. However, traditional methods involve these processes in isolation, resulting in plans that are not reflected in real time and making optimal resource allocation and urban design difficult.

[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 an acquisition means for obtaining information from an information source, a preprocessing means for cleansing the acquired information, an analysis means, a prediction means for simulating different urban design proposals, and a feedback means for providing a participatory platform where residents can post their opinions. This enables real-time data collection and analysis, flexible urban development planning, and rapid incorporation of residents' feedback.

[0065] "Means of acquisition" refers to the means of obtaining necessary information from information sources related to the city.

[0066] "Cleaning methods" refer to techniques used to generate clean data suitable for analysis by imputing missing values ​​and removing outliers from collected information.

[0067] "Analysis methods" refer to means of using generative models, etc., to identify urban issues and trends using pre-processed data.

[0068] A "predictive tool" is a means of simulating different design proposals in urban development and providing prediction results.

[0069] "Allocation optimization methods" are means of deriving the optimal allocation of resources based on simulation results and proposing efficient urban layout plans.

[0070] A "participation platform" is a means of providing a space where residents can post their opinions on urban development plans.

[0071] "Update methods" refer to means of incorporating opinions obtained from information sources and feedback into the next plan update.

[0072] The urban development planning system according to the present invention is designed to collect and analyze diverse urban-related information in real time and propose an optimal development plan. The system is configured in which a server, terminals, and users each play their respective roles and work together.

[0073] The server retrieves necessary data from sources such as traffic sensors installed within the city, databases providing demographic information, and APIs from public institutions. This data is collected via the internet or dedicated communication networks. Following data collection, the server preprocesses the data using cleansing methods to correct for missing or outlier values. A database management system (DBMS) is utilized to ensure data integrity and consistency.

[0074] The terminal uses a generative AI model to analyze the cleansed data. For example, it inputs a prompt such as "Analyze the correlation between traffic flow and population dynamics" into the generative AI model, which then analyzes population dynamics and traffic patterns within the city. Based on the results of this analysis, the server simulates various scenarios for urban development. The terminal also uses the analysis results to derive the optimal resource allocation using allocation optimization methods, employing computational techniques such as evolutionary algorithms.

[0075] Users can provide feedback on proposed urban development plans through a citizen participation platform. For example, in response to a prompt such as "Please propose requirements for new public facilities," users can describe specific needs and suggestions. User feedback is considered by the server when the plan is updated next, contributing to the development of practical and flexible urban development plans.

[0076] In this way, this system utilizes information technology and generative AI models to address complex urban challenges and rapidly formulate feasible development plans.

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

[0078] Step 1:

[0079] The server collects information from urban traffic sensors, demographic databases, and public institution APIs. The input sources are diverse, including traffic conditions and population changes. This collected data is stored in a database. During the data harvesting process, the data is updated according to the collection frequency, ensuring real-time accuracy.

[0080] Step 2:

[0081] The server preprocesses the collected information. This preprocessing includes data cleansing, such as imputing missing values ​​and removing outliers. The input is the collected raw data, and the output is a clean dataset with ensured consistency. This process is automated using scripts.

[0082] Step 3:

[0083] The terminal begins the analysis using cleansed data. The input data is pre-processed data received from the server. This analysis utilizes a generative AI model. The specific prompt "Analyze the correlation between traffic flow and demographics" is input to the model, and demographics and traffic patterns are analyzed. The output provides detailed insights into population growth and traffic congestion patterns in a specific region.

[0084] Step 4:

[0085] The server runs simulations based on the data obtained from the analysis. The input is the analysis results from the previous step, and a hardware system is used to test different urban development scenarios in a virtual environment. For example, predicting the impact of new road infrastructure on traffic. The output is the data provided as the prediction results for each simulated scenario.

[0086] Step 5:

[0087] The terminal initiates optimization processing based on the simulation results. The input is the simulation's predicted data, and allocation optimization methods are applied. This process utilizes evolutionary algorithms to derive the optimal placement of public facilities and transportation systems. The output is an efficient and cost-effective urban planning proposal.

[0088] Step 6:

[0089] Users submit their opinions on the proposed plan through the participating platform. The necessary input for this is the user's own ideas and suggestions. For example, they might submit their opinions through a prompt such as, "Please propose the requirements for the new public facility." The output is that the submitted feedback is stored in a database and reflected in the next plan update.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In modern cities, sustainable development is essential, but achieving optimal urban planning while effectively utilizing diverse data and promoting citizen participation is challenging. In particular, it is necessary to provide citizens with real-time information on the progress and predicted impacts of urban design and to incorporate their opinions into the planning process.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for collecting information from multiple data sources related to the city, means for preprocessing the collected information, and means for visualizing the progress of urban design and providing information in real time. This allows citizens to easily provide their opinions on urban design plans, and enables sustainable urban development that reflects those opinions.

[0095] "Multiple data sources related to a city" refers to diverse and convergent sources of information within a city, such as traffic information, demographic information, and public service information.

[0096] "Means of collection" refers to a method or apparatus for obtaining necessary information from specified data sources and systematically storing it.

[0097] "Preprocessing" refers to the process of converting collected raw data into a format suitable for analysis, and includes processes such as imputing missing values ​​and removing outliers.

[0098] "Means of analysis and identification" refers to methods or devices that use data analysis techniques to identify problems and trends in cities and to obtain data-based insights.

[0099] "Simulation means" refers to a method or apparatus for virtually recreating different scenarios in urban development planning and evaluating their impact.

[0100] "Optimization means" refers to a calculation method or device that uses the results of simulations to determine the optimal allocation and structure of resources.

[0101] "Means for citizens to provide feedback" refers to a platform or method for collecting, analyzing, and incorporating citizen feedback on urban planning.

[0102] "Means of visualization and real-time information provision" refers to technologies or devices that visually display the progress and predicted impacts of urban design and provide users with information immediately.

[0103] The system implementing this invention provides a platform for collecting and analyzing various urban-related data and proposing sustainable urban development to society. It operates with three main components: a server, terminals, and users.

[0104] The server collects urban-related data such as traffic information, demographics, and public institution information from multiple data sources. The collected information is preprocessed using software such as Pandas and NumPy to impute missing values ​​and remove outliers, and then securely stored in the cloud.

[0105] Subsequently, the terminal performs analysis using machine learning frameworks such as TENSORFLOW® and PyTorch. This identifies crowd dynamics and transportation patterns, revealing urban problems and trends. The analysis results are graphed using visualization tools such as Matplotlib and Plotly, making them easily viewable.

[0106] Next, the server simulates different urban design scenarios (for example, the introduction of new public transport lines). Here, it uses evolutionary algorithms to optimize and derive resource allocation. Finally, it generates an optimal configuration model of the urban structure based on the optimized results.

[0107] Users can participate in the process as citizens and offer their opinions. To this end, a smart device interface built with React Native allows them to view the progress and predicted impacts of urban design in real time and submit feedback. This feedback is aggregated on a server and used to inform future planning.

[0108] As a concrete example, a plan for introducing a new bicycle sharing service is presented. In this scenario, users provide feedback on its necessity and benefits. The following prompts are used when utilizing the generative AI model.

[0109] Example prompt: "As a citizen, please give your opinion on the introduction of a bicycle-sharing service as a new public transportation system. What advantages do you see?"

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects traffic information, demographic information, and public institution information from city-related data sources and preprocesses it using Pandas and NumPy. Specifically, it generates a dataset suitable for analysis by imputing missing values ​​and removing outliers. The input is raw data, and the output is a consistent, preprocessed dataset.

[0113] Step 2:

[0114] The terminal performs analysis using a pre-processed dataset. It utilizes machine learning frameworks such as TensorFlow and PyTorch to identify population dynamics and analyze transportation patterns. The analysis identifies urban challenges and trends, which are then output as the analysis results. The input is a pre-processed dataset, and the output is a visualized analysis result.

[0115] Step 3:

[0116] The server uses the obtained analysis results to simulate different urban design scenarios. Using evolutionary algorithms, it virtually evaluates, for example, the impact of introducing new transportation routes on traffic patterns. The input is the analysis results, and the output is the data for each simulation scenario.

[0117] Step 4:

[0118] The server uses simulation results to perform optimization and proposes the optimal resource allocation. It generates a model of the urban structure and derives an efficient allocation of resources. The input is data from the simulation scenario, and the output is the optimized urban structure model.

[0119] Step 5:

[0120] Users can view the progress of urban design and simulation results in real time from a citizen's perspective and provide feedback. This process utilizes an interface built with React Native. Feedback is sent using prompts as an example, and as a result, opinion data is collected. The input is user feedback, and the output is feedback data.

[0121] Step 6:

[0122] The server incorporates the collected feedback into the next urban planning process, contributing to more sustainable urban development. This generates highly feasible urban planning proposals. The input is feedback data, and the output is the improved urban planning proposal.

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

[0124] This invention is an AI-powered urban planning system aimed at recognizing and considering user emotions during the urban development plan proposal process. The specific configuration and embodiments of this system are described below.

[0125] The system consists of a server, terminals, and users. The server collects data from various data sources within the city and stores it in a database for analysis. This data includes traffic volume, demographics, and environmental information. After preprocessing the collected data, the terminals perform analysis using machine learning models. Clustering techniques are used in the analysis to clarify the city's demographics and traffic patterns.

[0126] Furthermore, the server uses these analysis results to perform urban development simulations. Genetic algorithms are used when different development scenarios are simulated and optimal resource allocations are proposed. This generates an optimal placement model for public facilities and infrastructure.

[0127] Users provide opinions and feedback through a citizen participation platform. In response, an emotion engine operates, using natural language processing technology to analyze user comments. The emotional state embedded in the user's opinion (e.g., "satisfied," "dissatisfied," "interested") is recognized. The results of this emotion recognition are used to evaluate and improve urban planning.

[0128] For example, suppose a user adds feedback to a proposed new public transport route, expressing "I'm full of anticipation." In this case, the emotion engine recognizes the positive emotion and uses it as an indicator to strengthen the momentum of this route proposal. On the other hand, if many negative emotions are recognized, the plan proposal will be considered based on that feedback.

[0129] By incorporating user emotions into urban planning in this way, it becomes possible to formulate more flexible and adaptive development plans. This system goes beyond mere data analysis by integrating human emotions into digital analysis, realizing sustainable urban development that reflects social needs and emotions.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server periodically collects real-time data from urban data sources. Specifically, it retrieves the latest information from traffic sensors and demographic databases and securely stores it in the database.

[0133] Step 2:

[0134] The server preprocesses the collected data. This process involves detecting missing values ​​in the dataset and filling them in using imputation techniques. It also detects outliers and applies appropriate filtering to generate a clean dataset.

[0135] Step 3:

[0136] The device applies machine learning models to analyze pre-processed data. It uses clustering algorithms to classify population dynamics and traffic patterns within cities and analyze trends and problems in specific areas.

[0137] Step 4:

[0138] The server simulates multiple urban development scenarios based on the analysis results. For example, it performs modeling to predict the impact on surrounding areas when a new public transport route is planned.

[0139] Step 5:

[0140] The terminal uses the simulation results to perform an optimization process. Based on a genetic algorithm, it calculates the optimal placement of public facilities and transportation infrastructure and generates a specific placement model. This model aims to maximize cost efficiency and citizen convenience.

[0141] Step 6:

[0142] Users provide feedback on urban planning proposals through a citizen participation platform. This feedback is often provided in written form and can include specific opinions and emotions.

[0143] Step 7:

[0144] The device activates an emotion engine to analyze user feedback. Using natural language processing techniques, it analyzes keywords and context in the text to identify the user's emotional state. For example, if the feedback includes positive expressions such as "I'm looking forward to it," the device recognizes a positive attitude towards the plan.

[0145] Step 8:

[0146] The server incorporates the results of sentiment analysis into the final urban development plan proposal. Proposals that receive a large number of positive responses are prioritized, while those with a large number of negative responses are re-evaluated and adjusted. This allows for the implementation of flexible and adaptive plans that take residents' feelings into consideration.

[0147] (Example 2)

[0148] 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".

[0149] In urban development planning, traditional methods often fail to adequately consider citizens' opinions and feelings, resulting in plans that do not align with their needs. Furthermore, effectively analyzing information within the city and achieving optimal resource allocation has been challenging. This has hindered flexible and sustainable urban development.

[0150] 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.

[0151] In this invention, the server includes means for collecting information from multiple sources related to the city, means for preprocessing and analyzing the collected information, and means for analyzing sentiment based on the opinions of participants. This allows urban development plans to be adjusted to take into account the sentiments and opinions of citizens, enabling optimal resource allocation.

[0152] "Information gathering methods" refer to the function of obtaining necessary information from multiple sources related to a city.

[0153] "Preprocessing methods" refer to functions that prepare collected information for analysis by performing tasks such as noise reduction and format standardization before the information is analyzed, thereby creating a state suitable for data analysis.

[0154] "Analysis tools" refer to functions that perform calculations and analyses to identify urban trends and challenges using pre-processed information.

[0155] A "simulation tool" is a function that virtually reproduces different urban development plans and predicts their impact.

[0156] An "optimization tool" is a function that proposes the optimal resource allocation and facility layout based on the results of analysis and simulation.

[0157] "Means of participation" refer to functions used by participants to provide opinions and feedback on urban development plans.

[0158] "Methods for analyzing emotions" refers to analytical functions that extract emotions from participants' opinions and reflect them in the plan.

[0159] This invention is an AI system for formulating flexible and sustainable plans in urban development projects that take into account the opinions and feelings of citizens. The system consists of a server, terminals, and users.

[0160] The server collects data such as traffic volume, demographics, and environmental information from various sources within the city and stores this data in a database. IoT sensors and publicly available government databases are used for data collection. The data is updated in real time, allowing for immediate responses to dynamic changes in the city.

[0161] The terminal preprocesses the data received from the server using a machine learning library such as Python's Scikit-learn, and then analyzes it using clustering techniques. This clarifies urban demographics and traffic patterns, providing data-driven insights.

[0162] The server then runs a simulation using a genetic algorithm based on the analysis results. This process virtually tests various urban development scenarios to find the optimal placement of public facilities and infrastructure. At this stage, a concrete facility placement plan is created, and the necessary resources are appropriately allocated.

[0163] Users provide opinions and feedback in natural language through a citizen participation platform. The server then uses natural language processing technology to analyze the user's emotions in response to these comments. The emotion analysis engine identifies positive expressions such as "full of anticipation" and negative expressions such as "dissatisfied," and incorporates this into the evaluation of urban planning.

[0164] As a concrete example of its use, if a user comments on a proposed new transportation route saying, "I'm full of excitement," this feedback positively influences the plan and strengthens the momentum of the proposal. Conversely, if there are many negative opinions, the plan will be re-evaluated. In this way, flexible urban development that is tailored to the needs of citizens becomes possible.

[0165] An example of a prompt for a generative AI model might be, "Please tell me how to collect and analyze public opinion on new public transport routes."

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The server acquires data such as traffic volume, demographics, and environmental information from various sources within the city. This data collection utilizes IoT sensors and publicly available government databases. The data collected as input is raw, unprocessed data, which is stored in the database. The data output is a database in which this raw data is stored in a structured format.

[0169] Step 2:

[0170] The terminal receives raw data from the server and performs preprocessing such as noise reduction and format standardization. This preprocessing prepares the data for analysis. The input is the raw data provided by the server, and the output is processed, clean data.

[0171] Step 3:

[0172] The terminal performs analysis using clustering techniques with pre-processed data. This analysis utilizes a Python machine learning library. The input is pre-processed data, and the output provides insights into urban demographics and traffic patterns.

[0173] Step 4:

[0174] The server uses the analysis results obtained from the terminal to perform urban development simulations. At this stage, a genetic algorithm is used to test various development scenarios and generate an optimal placement model for public facilities and infrastructure. The input is the analysis results, and the output is the optimized urban development plan.

[0175] Step 5:

[0176] Users provide their opinions and feedback through a civic participation platform. The input provided is the user's natural language comments. The server analyzes this feedback using natural language processing techniques to identify the user's emotions. The output is data indicating their emotional state.

[0177] Step 6:

[0178] The server adjusts urban planning based on analyzed sentiment data to create a final, optimal plan. Inputs are user sentiment data and initial simulation results, while output is an improved urban planning proposal reflecting citizen feedback. This allows for flexible and adaptive urban development based on citizen needs.

[0179] (Application Example 2)

[0180] 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".

[0181] While objective, data-driven analysis is essential in urban development planning, effectively considering the feelings and opinions of individual stakeholders while formulating plans remains a challenging task. A participatory decision-making process in urban development, where stakeholders' feelings are reflected in the planning, is necessary to create more adaptive and sustainable urban plans.

[0182] 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.

[0183] In this invention, the server includes a data collection means for collecting information from multiple data sources related to the city, a data preprocessing means for preprocessing the collected information, and an emotion analysis means for recognizing and evaluating emotional states. This makes it possible to effectively reflect the emotions of residents in urban development plans and to formulate flexible and adaptive urban plans.

[0184] "Information gathering means" refers to a device or method for collecting necessary information from multiple sources related to a city.

[0185] "Preprocessing means" refers to an apparatus or method of processing that is performed to convert collected information into an analyzable format.

[0186] "Analysis means" refers to a device or method for identifying urban problems and trends using pre-processed information.

[0187] A "simulation means" is a device or method for virtually executing different scenarios in urban development and evaluating the predicted impacts.

[0188] An "optimization means" is a device or method that proposes the optimal resource allocation or arrangement based on simulation results.

[0189] "Means of participation" refers to devices or methods for individual members to provide opinions and feedback on urban development plans.

[0190] "Emotional analysis means" refers to a device or method for recognizing and evaluating the emotional state contained in the feedback provided by a member.

[0191] "Evaluation tools" refer to devices or methods for evaluating or modifying urban planning based on the results of sentiment analysis.

[0192] The system implementing this invention combines various technical means to effectively reflect the sentiments of its members in the development of a smart city. A server collects information such as traffic volume, demographics, and environmental data from diverse sources related to the city, using a data collection device. This information is then formatted into an analyzable form by a preprocessor. This process includes data cleaning and integrity checks.

[0193] The pre-processed information is analyzed by an analysis device on the terminal to identify demographic trends and traffic patterns. A learning-based model is used to classify the information. The analysis results are sent to a server, where different development scenarios are tested in a simulation device. The impact of the different scenarios is predicted, and the results are evaluated by an optimization device to propose the optimal resource allocation.

[0194] Individual members can provide feedback on the urban development plan using a participation device. In this process, an emotion analysis device recognizes the emotional state contained in the comments using natural language processing technology. These results are then used by an evaluation device to assess and revise the urban plan. As important feedback, if the emotion contained in a comment is evaluated as "full of anticipation," that item is recognized as a driving force. On the other hand, if negative emotions are recognized, revisions to the development scenario should be considered.

[0195] For example, if a user expresses their expectations regarding a proposed new public transport route, the sentiment analyzer will correctly recognize that emotion and use a prompt message in the form of, "Please recognize the emotion of this feedback and assist in how to reflect it in the urban planning system."

[0196] The hardware and software used include smartphones, servers, Python, and natural language processing libraries. Specifically, libraries such as NLTK and custom models for sentiment recognition are utilized. This enables flexible and sustainable development that realistically and effectively reflects the emotions of residents in urban planning.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server collects information such as traffic volume, demographics, and environmental data from multiple sources related to the city. The collected information is input into the server, where data cleaning and integrity checks are performed before it is stored in the database, and clean data is output.

[0200] Step 2:

[0201] The terminal preprocesses the clean data received from the server. Here, it performs format conversion and removes meaningless data to convert the data into a parseable format, and outputs the preprocessed data.

[0202] Step 3:

[0203] The terminal uses pre-processed data to apply machine learning models and classify demographics and traffic patterns. The input data is processed by an analysis device, and classification results regarding demographics and traffic patterns are output.

[0204] Step 4:

[0205] The server uses the classification results to run simulations of different urban development scenarios. The simulation device receives these simulations, tests the predicted effects of the different scenarios in a virtual environment, and outputs the simulation results.

[0206] Step 5:

[0207] The server uses the simulation results and employs evolutionary algorithms to determine the optimal resource allocation. The optimization device performs optimization calculations according to the scenario allocation and outputs the optimal resource allocation plan.

[0208] Step 6:

[0209] Users input feedback into urban development plans using a participation device. Comments provided by users are input, and an emotion analysis device analyzes those comments and outputs the emotional state.

[0210] Step 7:

[0211] The server receives the output from the emotion analysis device and appropriately evaluates and modifies the urban plan. Based on the outputted emotional state, the evaluation device modifies the plan and outputs a newly adjusted urban plan proposal.

[0212] 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.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] The AI ​​urban planning system according to the present invention consists of multiple components for collecting various urban-related data and making proposals for sustainable urban development. This system operates according to the following procedure, with the server, terminals, and users each playing their respective roles.

[0229] Data collection

[0230] The server collects data from various data sources within the city, such as traffic sensors, demographic databases, and public institution APIs. This data is updated daily or at a specified frequency and stored in the database.

[0231] Data preprocessing

[0232] The server performs preprocessing to maintain the integrity of the collected data. Specifically, it generates a clean dataset by imputing missing values ​​and removing outliers.

[0233] Data Analysis

[0234] The device performs analysis using pre-processed data. Here, machine learning models are applied to analyze urban demographics and traffic patterns. For example, it can identify population growth trends and traffic congestion patterns in specific areas.

[0235] Running the simulation

[0236] The server uses simulation tools to run different urban development scenarios. For example, it can virtually predict the impact of constructing a new train station on local traffic patterns.

[0237] Optimization of resource allocation

[0238] Based on the simulation results, the terminal proposes efficient resource allocation using optimization methods. For example, it optimizes the location of public facilities and develops a layout plan that maximizes citizen convenience and cost efficiency.

[0239] Promoting citizen participation

[0240] Users can provide feedback on the proposed plan through a citizen participation platform. This user feedback is collected by the system and considered in future plan updates. For example, a user might post an opinion about the need for a new public transport route, and that opinion might be reflected in the plan.

[0241] The system's operation, as described above, enables the realization of more precise and efficient urban development plans. This system employs a data-driven approach and, by utilizing AI technology, is expected to contribute to solving complex urban challenges.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The server collects data from various data sources within the city. Specifically, it obtains real-time traffic volume data from public transport APIs and the latest demographic information from demographic databases. This data is then cleansed and stored in the database.

[0245] Step 2:

[0246] The server performs preprocessing on the collected data. Specifically, it identifies missing values ​​in the data and fills them in using appropriate imputation methods (e.g., imputing the mean or median). It also detects and removes or corrects outliers caused by sensor failures or other issues.

[0247] Step 3:

[0248] The terminal begins analysis using pre-processed data. It applies machine learning algorithms to cluster urban population trends and traffic patterns. For example, it identifies traffic congestion patterns by time of day in a specific area and predicts future traffic congestion locations.

[0249] Step 4:

[0250] The server performs simulations based on the analysis results. It sets up various urban development scenarios and predicts their effects. As a specific example, it models the changes in pedestrian flow to surrounding areas when a new railway station is opened.

[0251] Step 5:

[0252] The terminal receives the simulation results and performs optimization processing to determine efficient resource allocation. Using a genetic algorithm, it calculates the placement of public facilities while considering cost-effectiveness and formulates the optimal resource allocation plan.

[0253] Step 6:

[0254] Users input their opinions and feedback on presented urban planning proposals through a citizen participation platform. This feedback is later analyzed and reflected in future urban development proposals, making it possible to incorporate the opinions of local residents into the planning process.

[0255] (Example 1)

[0256] 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."

[0257] Modern cities face a variety of complex challenges, including rapid population growth, traffic congestion, and environmental impacts. To comprehensively and efficiently address these challenges, sustainable development plans are needed that collect and analyze vast amounts of data and incorporate residents' opinions. However, traditional methods involve these processes in isolation, resulting in plans that are not reflected in real time and making optimal resource allocation and urban design difficult.

[0258] 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.

[0259] In this invention, the server includes an acquisition means for obtaining information from an information source, a preprocessing means for cleansing the acquired information, an analysis means, a prediction means for simulating different urban design proposals, and a feedback means for providing a participatory platform where residents can post their opinions. This enables real-time data collection and analysis, flexible urban development planning, and rapid incorporation of residents' feedback.

[0260] "Means of acquisition" refers to the means of obtaining necessary information from information sources related to the city.

[0261] "Cleaning methods" refer to techniques used to generate clean data suitable for analysis by imputing missing values ​​and removing outliers from collected information.

[0262] "Analysis methods" refer to means of using generative models, etc., to identify urban issues and trends using pre-processed data.

[0263] A "predictive tool" is a means of simulating different design proposals in urban development and providing prediction results.

[0264] "Allocation optimization methods" are means of deriving the optimal allocation of resources based on simulation results and proposing efficient urban layout plans.

[0265] A "participation platform" is a means of providing a space where residents can post their opinions on urban development plans.

[0266] "Update methods" refer to means of incorporating opinions obtained from information sources and feedback into the next plan update.

[0267] The urban development planning system according to the present invention is designed to collect and analyze diverse urban-related information in real time and propose an optimal development plan. The system is configured in which a server, terminals, and users each play their respective roles and work together.

[0268] The server retrieves necessary data from sources such as traffic sensors installed within the city, databases providing demographic information, and APIs from public institutions. This data is collected via the internet or dedicated communication networks. Following data collection, the server preprocesses the data using cleansing methods to correct for missing or outlier values. A database management system (DBMS) is utilized to ensure data integrity and consistency.

[0269] The terminal uses a generative AI model to analyze the cleansed data. For example, it inputs a prompt such as "Analyze the correlation between traffic flow and population dynamics" into the generative AI model, which then analyzes population dynamics and traffic patterns within the city. Based on the results of this analysis, the server simulates various scenarios for urban development. The terminal also uses the analysis results to derive the optimal resource allocation using allocation optimization methods, employing computational techniques such as evolutionary algorithms.

[0270] Users can provide feedback on proposed urban development plans through a citizen participation platform. For example, in response to a prompt such as "Please propose requirements for new public facilities," users can describe specific needs and suggestions. User feedback is considered by the server when the plan is updated next, contributing to the development of practical and flexible urban development plans.

[0271] In this way, this system utilizes information technology and generative AI models to address complex urban challenges and rapidly formulate feasible development plans.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The server collects information from urban traffic sensors, demographic databases, and public institution APIs. The input sources are diverse, including traffic conditions and population changes. This collected data is stored in a database. During the data harvesting process, the data is updated according to the collection frequency, ensuring real-time accuracy.

[0275] Step 2:

[0276] The server preprocesses the collected information. This preprocessing includes data cleansing, such as imputing missing values ​​and removing outliers. The input is the collected raw data, and the output is a clean dataset with ensured consistency. This process is automated using scripts.

[0277] Step 3:

[0278] The terminal begins the analysis using cleansed data. The input data is pre-processed data received from the server. This analysis utilizes a generative AI model. The specific prompt "Analyze the correlation between traffic flow and demographics" is input to the model, and demographics and traffic patterns are analyzed. The output provides detailed insights into population growth and traffic congestion patterns in a specific region.

[0279] Step 4:

[0280] The server runs simulations based on the data obtained from the analysis. The input is the analysis results from the previous step, and a hardware system is used to test different urban development scenarios in a virtual environment. For example, predicting the impact of new road infrastructure on traffic. The output is the data provided as the prediction results for each simulated scenario.

[0281] Step 5:

[0282] The terminal starts the optimization process based on the simulation results. The input is the predicted data of the simulation, and the distribution optimization means is applied. In this process, an evolutionary algorithm is used to guide the optimal layout of public facilities and transportation facilities. The output is an efficient and cost-effective urban planning plan.

[0283] Step 6:

[0284] The user posts opinions on the plan through the participation platform. The necessary input for this is the user's own ideas and suggestions. For example, opinions are submitted through a prompt such as "Please propose the requirements for the new public facilities". The output is that the posted feedback is accumulated in the database and reflected in the next plan update.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In modern cities, sustainable development is required, but it is difficult to effectively utilize various data and realize optimal urban planning while promoting citizen participation. In particular, it is necessary to provide citizens with the progress of urban design and the predicted impacts in real time and reflect their opinions in the plan.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for collecting information from a plurality of data sources related to the city, means for preprocessing the collected information, and means for visualizing the progress of urban design and providing information in real time. Thereby, citizens can easily provide opinions on the urban design plan, and sustainable urban development that reflects their opinions becomes possible.

[0290] "Multiple data sources related to a city" refers to diverse and convergent sources of information within a city, such as traffic information, demographic information, and public service information.

[0291] "Means of collection" refers to a method or apparatus for obtaining necessary information from specified data sources and systematically storing it.

[0292] "Preprocessing" refers to the process of converting collected raw data into a format suitable for analysis, and includes processes such as imputing missing values ​​and removing outliers.

[0293] "Means of analysis and identification" refers to methods or devices that use data analysis techniques to identify problems and trends in cities and to obtain data-based insights.

[0294] "Simulation means" refers to a method or apparatus for virtually recreating different scenarios in urban development planning and evaluating their impact.

[0295] "Optimization means" refers to a calculation method or device that uses the results of simulations to determine the optimal allocation and structure of resources.

[0296] "Means for citizens to provide feedback" refers to a platform or method for collecting, analyzing, and incorporating citizen feedback on urban planning.

[0297] "Means of visualization and real-time information provision" refers to technologies or devices that visually display the progress and predicted impacts of urban design and provide users with information immediately.

[0298] The system implementing this invention provides a platform for collecting and analyzing various urban-related data and proposing sustainable urban development to society. It operates with three main components: a server, terminals, and users.

[0299] The server collects urban-related data such as traffic information, demographics, and public institution information from multiple data sources. The collected information is preprocessed using software such as Pandas and NumPy to impute missing values ​​and remove outliers, and then securely stored in the cloud.

[0300] Subsequently, the device performs analysis using machine learning frameworks such as TensorFlow and PyTorch. This identifies patterns in population dynamics and transportation, revealing urban problems and trends. The analysis results are then graphed using visualization tools such as Matplotlib and Plotly, making them easily viewable.

[0301] Next, the server simulates different urban design scenarios (for example, the introduction of new public transport lines). Here, it uses evolutionary algorithms to optimize and derive resource allocation. Finally, it generates an optimal configuration model of the urban structure based on the optimized results.

[0302] Users can participate in the process as citizens and offer their opinions. To this end, a smart device interface built with React Native allows them to view the progress and predicted impacts of urban design in real time and submit feedback. This feedback is aggregated on a server and used to inform future planning.

[0303] As a concrete example, a plan for introducing a new bicycle sharing service is presented. In this scenario, users provide feedback on its necessity and benefits. The following prompts are used when utilizing the generative AI model.

[0304] Example prompt: "As a citizen, please give your opinion on the introduction of a bicycle-sharing service as a new public transportation system. What advantages do you see?"

[0305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0306] Step 1:

[0307] The server collects traffic information, demographic information, public institution information, etc. from data sources related to cities and preprocesses them using Pandas and NumPy. Specifically, it generates a dataset in a state suitable for analysis by complementing missing values and removing outliers. The input is raw data, and the output is a consistent preprocessed dataset.

[0308] Step 2:

[0309] The terminal performs analysis using the preprocessed dataset. It uses machine learning frameworks such as TensorFlow and PyTorch to identify group dynamics and analyze transportation patterns. Through the analysis, urban issues and trends can be identified and output as analysis results. The input is the preprocessed dataset, and the output is visualizable analysis results.

[0310] Step 3:

[0311] The server performs simulations of different urban design scenarios based on the obtained analysis results. Using evolutionary algorithms, it virtually evaluates, for example, the impact of introducing new transportation routes on traffic patterns. The input is the analysis results, and the output is data for each simulation scenario.

[0312] Step 4:

[0313] The server performs optimization processing using the simulation results and proposes optimal resource allocation. It generates a model related to the urban structure and derives efficient allocation of resources. The input is data for the simulation scenario, and the output is an optimized urban structure model.

[0314] Step 5:

[0315] Users can view the progress of urban design and simulation results in real time from a citizen's perspective and provide feedback. This process utilizes an interface built with React Native. Feedback is sent using prompts as an example, and as a result, opinion data is collected. The input is user feedback, and the output is feedback data.

[0316] Step 6:

[0317] The server incorporates the collected feedback into the next urban planning process, contributing to more sustainable urban development. This generates highly feasible urban planning proposals. The input is feedback data, and the output is the improved urban planning proposal.

[0318] 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.

[0319] This invention is an AI-powered urban planning system aimed at recognizing and considering user emotions during the urban development plan proposal process. The specific configuration and embodiments of this system are described below.

[0320] The system consists of a server, terminals, and users. The server collects data from various data sources within the city and stores it in a database for analysis. This data includes traffic volume, demographics, and environmental information. After preprocessing the collected data, the terminals perform analysis using machine learning models. Clustering techniques are used in the analysis to clarify the city's demographics and traffic patterns.

[0321] Furthermore, the server uses these analysis results to perform urban development simulations. Genetic algorithms are used when different development scenarios are simulated and optimal resource allocations are proposed. This generates an optimal placement model for public facilities and infrastructure.

[0322] Users provide opinions and feedback through a citizen participation platform. In response, an emotion engine operates, using natural language processing technology to analyze user comments. The emotional state embedded in the user's opinion (e.g., "satisfied," "dissatisfied," "interested") is recognized. The results of this emotion recognition are used to evaluate and improve urban planning.

[0323] For example, suppose a user adds feedback to a proposed new public transport route, expressing "I'm full of anticipation." In this case, the emotion engine recognizes the positive emotion and uses it as an indicator to strengthen the momentum of this route proposal. On the other hand, if many negative emotions are recognized, the plan proposal will be considered based on that feedback.

[0324] By incorporating user emotions into urban planning in this way, it becomes possible to formulate more flexible and adaptive development plans. This system goes beyond mere data analysis by integrating human emotions into digital analysis, realizing sustainable urban development that reflects social needs and emotions.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The server periodically collects real-time data from urban data sources. Specifically, it retrieves the latest information from traffic sensors and demographic databases and securely stores it in the database.

[0328] Step 2:

[0329] The server preprocesses the collected data. This process involves detecting missing values ​​in the dataset and filling them in using imputation techniques. It also detects outliers and applies appropriate filtering to generate a clean dataset.

[0330] Step 3:

[0331] The device applies machine learning models to analyze pre-processed data. It uses clustering algorithms to classify population dynamics and traffic patterns within cities and analyze trends and problems in specific areas.

[0332] Step 4:

[0333] The server simulates multiple urban development scenarios based on the analysis results. For example, it performs modeling to predict the impact on surrounding areas when a new public transport route is planned.

[0334] Step 5:

[0335] The terminal uses the simulation results to perform an optimization process. Based on a genetic algorithm, it calculates the optimal placement of public facilities and transportation infrastructure and generates a specific placement model. This model aims to maximize cost efficiency and citizen convenience.

[0336] Step 6:

[0337] Users provide feedback on urban planning proposals through a citizen participation platform. This feedback is often provided in written form and can include specific opinions and emotions.

[0338] Step 7:

[0339] The device activates an emotion engine to analyze user feedback. Using natural language processing techniques, it analyzes keywords and context in the text to identify the user's emotional state. For example, if the feedback includes positive expressions such as "I'm looking forward to it," the device recognizes a positive attitude towards the plan.

[0340] Step 8:

[0341] The server incorporates the results of sentiment analysis into the final urban development plan proposal. Proposals that receive a large number of positive responses are prioritized, while those with a large number of negative responses are re-evaluated and adjusted. This allows for the implementation of flexible and adaptive plans that take residents' feelings into consideration.

[0342] (Example 2)

[0343] 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".

[0344] In urban development planning, traditional methods often fail to adequately consider citizens' opinions and feelings, resulting in plans that do not align with their needs. Furthermore, effectively analyzing information within the city and achieving optimal resource allocation has been challenging. This has hindered flexible and sustainable urban development.

[0345] 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.

[0346] In this invention, the server includes means for collecting information from multiple sources related to the city, means for preprocessing and analyzing the collected information, and means for analyzing sentiment based on the opinions of participants. This allows urban development plans to be adjusted to take into account the sentiments and opinions of citizens, enabling optimal resource allocation.

[0347] "Information gathering methods" refer to the function of obtaining necessary information from multiple sources related to a city.

[0348] "Preprocessing methods" refer to functions that prepare collected information for analysis by performing tasks such as noise reduction and format standardization before the information is analyzed, thereby creating a state suitable for data analysis.

[0349] "Analysis tools" refer to functions that perform calculations and analyses to identify urban trends and challenges using pre-processed information.

[0350] A "simulation tool" is a function that virtually reproduces different urban development plans and predicts their impact.

[0351] An "optimization tool" is a function that proposes the optimal resource allocation and facility layout based on the results of analysis and simulation.

[0352] "Means of participation" refer to functions used by participants to provide opinions and feedback on urban development plans.

[0353] "Methods for analyzing emotions" refers to analytical functions that extract emotions from participants' opinions and reflect them in the plan.

[0354] This invention is an AI system for formulating flexible and sustainable plans in urban development projects that take into account the opinions and feelings of citizens. The system consists of a server, terminals, and users.

[0355] The server collects data such as traffic volume, demographics, and environmental information from various sources within the city and stores this data in a database. IoT sensors and publicly available government databases are used for data collection. The data is updated in real time, allowing for immediate responses to dynamic changes in the city.

[0356] The terminal preprocesses the data received from the server using a machine learning library such as Python's Scikit-learn, and then analyzes it using clustering techniques. This clarifies urban demographics and traffic patterns, providing data-driven insights.

[0357] The server then runs a simulation using a genetic algorithm based on the analysis results. This process virtually tests various urban development scenarios to find the optimal placement of public facilities and infrastructure. At this stage, a concrete facility placement plan is created, and the necessary resources are appropriately allocated.

[0358] Users provide opinions and feedback in natural language through a citizen participation platform. The server then uses natural language processing technology to analyze the user's emotions in response to these comments. The emotion analysis engine identifies positive expressions such as "full of anticipation" and negative expressions such as "dissatisfied," and incorporates this into the evaluation of urban planning.

[0359] As a concrete example of its use, if a user comments on a proposed new transportation route saying, "I'm full of excitement," this feedback positively influences the plan and strengthens the momentum of the proposal. Conversely, if there are many negative opinions, the plan will be re-evaluated. In this way, flexible urban development that is tailored to the needs of citizens becomes possible.

[0360] An example of a prompt for a generative AI model might be, "Please tell me how to collect and analyze public opinion on new public transport routes."

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The server acquires data such as traffic volume, demographics, and environmental information from various sources within the city. This data collection utilizes IoT sensors and publicly available government databases. The data collected as input is raw, unprocessed data, which is stored in the database. The data output is a database in which this raw data is stored in a structured format.

[0364] Step 2:

[0365] The terminal receives raw data from the server and performs preprocessing such as noise reduction and format standardization. This preprocessing prepares the data for analysis. The input is the raw data provided by the server, and the output is processed, clean data.

[0366] Step 3:

[0367] The terminal performs analysis using clustering techniques with pre-processed data. This analysis utilizes a Python machine learning library. The input is pre-processed data, and the output provides insights into urban demographics and traffic patterns.

[0368] Step 4:

[0369] The server uses the analysis results obtained from the terminal to perform urban development simulations. At this stage, a genetic algorithm is used to test various development scenarios and generate an optimal placement model for public facilities and infrastructure. The input is the analysis results, and the output is the optimized urban development plan.

[0370] Step 5:

[0371] Users provide their opinions and feedback through a civic participation platform. The input provided is the user's natural language comments. The server analyzes this feedback using natural language processing techniques to identify the user's emotions. The output is data indicating their emotional state.

[0372] Step 6:

[0373] The server adjusts urban planning based on analyzed sentiment data to create a final, optimal plan. Inputs are user sentiment data and initial simulation results, while output is an improved urban planning proposal reflecting citizen feedback. This allows for flexible and adaptive urban development based on citizen needs.

[0374] (Application Example 2)

[0375] 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."

[0376] While objective, data-driven analysis is essential in urban development planning, effectively considering the feelings and opinions of individual stakeholders while formulating plans remains a challenging task. A participatory decision-making process in urban development, where stakeholders' feelings are reflected in the planning, is necessary to create more adaptive and sustainable urban plans.

[0377] 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.

[0378] In this invention, the server includes a data collection means for collecting information from multiple data sources related to the city, a data preprocessing means for preprocessing the collected information, and an emotion analysis means for recognizing and evaluating emotional states. This makes it possible to effectively reflect the emotions of residents in urban development plans and to formulate flexible and adaptive urban plans.

[0379] "Information gathering means" refers to a device or method for collecting necessary information from multiple sources related to a city.

[0380] "Preprocessing means" refers to an apparatus or method of processing that is performed to convert collected information into an analyzable format.

[0381] "Analysis means" refers to a device or method for identifying urban problems and trends using pre-processed information.

[0382] A "simulation means" is a device or method for virtually executing different scenarios in urban development and evaluating the predicted impacts.

[0383] An "optimization means" is a device or method that proposes the optimal resource allocation or arrangement based on simulation results.

[0384] "Means of participation" refers to devices or methods for individual members to provide opinions and feedback on urban development plans.

[0385] "Emotional analysis means" refers to a device or method for recognizing and evaluating the emotional state contained in the feedback provided by a member.

[0386] "Evaluation tools" refer to devices or methods for evaluating or modifying urban planning based on the results of sentiment analysis.

[0387] The system implementing this invention combines various technical means to effectively reflect the sentiments of its members in the development of a smart city. A server collects information such as traffic volume, demographics, and environmental data from diverse sources related to the city, using a data collection device. This information is then formatted into an analyzable form by a preprocessor. This process includes data cleaning and integrity checks.

[0388] The pre-processed information is analyzed by an analysis device on the terminal to identify demographic trends and traffic patterns. A learning-based model is used to classify the information. The analysis results are sent to a server, where different development scenarios are tested in a simulation device. The impact of the different scenarios is predicted, and the results are evaluated by an optimization device to propose the optimal resource allocation.

[0389] Individual members can provide feedback on the urban development plan using a participation device. In this process, an emotion analysis device recognizes the emotional state contained in the comments using natural language processing technology. These results are then used by an evaluation device to assess and revise the urban plan. As important feedback, if the emotion contained in a comment is evaluated as "full of anticipation," that item is recognized as a driving force. On the other hand, if negative emotions are recognized, revisions to the development scenario should be considered.

[0390] For example, if a user expresses their expectations regarding a proposed new public transport route, the sentiment analyzer will correctly recognize that emotion and use a prompt message in the form of, "Please recognize the emotion of this feedback and assist in how to reflect it in the urban planning system."

[0391] The hardware and software used include smartphones, servers, Python, and natural language processing libraries. Specifically, libraries such as NLTK and custom models for sentiment recognition are utilized. This enables flexible and sustainable development that realistically and effectively reflects the emotions of residents in urban planning.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The server collects information such as traffic volume, demographics, and environmental data from multiple sources related to the city. The collected information is input into the server, where data cleaning and integrity checks are performed before it is stored in the database, and clean data is output.

[0395] Step 2:

[0396] The terminal preprocesses the clean data received from the server. Here, it performs format conversion and removes meaningless data to convert the data into a parseable format, and outputs the preprocessed data.

[0397] Step 3:

[0398] The terminal uses pre-processed data to apply machine learning models and classify demographics and traffic patterns. The input data is processed by an analysis device, and classification results regarding demographics and traffic patterns are output.

[0399] Step 4:

[0400] The server uses the classification results to run simulations of different urban development scenarios. The simulation device receives these simulations, tests the predicted effects of the different scenarios in a virtual environment, and outputs the simulation results.

[0401] Step 5:

[0402] The server uses the simulation results and employs evolutionary algorithms to determine the optimal resource allocation. The optimization device performs optimization calculations according to the scenario allocation and outputs the optimal resource allocation plan.

[0403] Step 6:

[0404] Users input feedback into urban development plans using a participation device. Comments provided by users are input, and an emotion analysis device analyzes those comments and outputs the emotional state.

[0405] Step 7:

[0406] The server receives the output from the emotion analysis device and appropriately evaluates and modifies the urban plan. Based on the outputted emotional state, the evaluation device modifies the plan and outputs a newly adjusted urban plan proposal.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] The AI ​​urban planning system according to the present invention consists of multiple components for collecting various urban-related data and making proposals for sustainable urban development. This system operates according to the following procedure, with the server, terminals, and users each playing their respective roles.

[0424] Data collection

[0425] The server collects data from various data sources within the city, such as traffic sensors, demographic databases, and public institution APIs. This data is updated daily or at a specified frequency and stored in the database.

[0426] Data preprocessing

[0427] The server performs preprocessing to maintain the integrity of the collected data. Specifically, it generates a clean dataset by imputing missing values ​​and removing outliers.

[0428] Data Analysis

[0429] The device performs analysis using pre-processed data. Here, machine learning models are applied to analyze urban demographics and traffic patterns. For example, it can identify population growth trends and traffic congestion patterns in specific areas.

[0430] Running the simulation

[0431] The server uses simulation tools to run different urban development scenarios. For example, it can virtually predict the impact of constructing a new train station on local traffic patterns.

[0432] Optimization of resource allocation

[0433] Based on the simulation results, the terminal proposes efficient resource allocation using optimization methods. For example, it optimizes the location of public facilities and develops a layout plan that maximizes citizen convenience and cost efficiency.

[0434] Promoting citizen participation

[0435] Users can provide feedback on the proposed plan through a citizen participation platform. This user feedback is collected by the system and considered in future plan updates. For example, a user might post an opinion about the need for a new public transport route, and that opinion might be reflected in the plan.

[0436] The system's operation, as described above, enables the realization of more precise and efficient urban development plans. This system employs a data-driven approach and, by utilizing AI technology, is expected to contribute to solving complex urban challenges.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The server collects data from various data sources within the city. Specifically, it obtains real-time traffic volume data from public transport APIs and the latest demographic information from demographic databases. This data is then cleansed and stored in the database.

[0440] Step 2:

[0441] The server performs preprocessing on the collected data. Specifically, it identifies missing values ​​in the data and fills them in using appropriate imputation methods (e.g., imputing the mean or median). It also detects and removes or corrects outliers caused by sensor failures or other issues.

[0442] Step 3:

[0443] The terminal begins analysis using pre-processed data. It applies machine learning algorithms to cluster urban population trends and traffic patterns. For example, it identifies traffic congestion patterns by time of day in a specific area and predicts future traffic congestion locations.

[0444] Step 4:

[0445] The server performs simulations based on the analysis results. It sets up various urban development scenarios and predicts their effects. As a specific example, it models the changes in pedestrian flow to surrounding areas when a new railway station is opened.

[0446] Step 5:

[0447] The terminal receives the simulation results and performs optimization processing to determine efficient resource allocation. Using a genetic algorithm, it calculates the placement of public facilities while considering cost-effectiveness and formulates the optimal resource allocation plan.

[0448] Step 6:

[0449] Users input their opinions and feedback on presented urban planning proposals through a citizen participation platform. This feedback is later analyzed and reflected in future urban development proposals, making it possible to incorporate the opinions of local residents into the planning process.

[0450] (Example 1)

[0451] 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."

[0452] Modern cities face a variety of complex challenges, including rapid population growth, traffic congestion, and environmental impacts. To comprehensively and efficiently address these challenges, sustainable development plans are needed that collect and analyze vast amounts of data and incorporate residents' opinions. However, traditional methods involve these processes in isolation, resulting in plans that are not reflected in real time and making optimal resource allocation and urban design difficult.

[0453] 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.

[0454] In this invention, the server includes an acquisition means for obtaining information from an information source, a preprocessing means for cleansing the acquired information, an analysis means, a prediction means for simulating different urban design proposals, and a feedback means for providing a participatory platform where residents can post their opinions. This enables real-time data collection and analysis, flexible urban development planning, and rapid incorporation of residents' feedback.

[0455] "Means of acquisition" refers to the means of obtaining necessary information from information sources related to the city.

[0456] "Cleaning methods" refer to techniques used to generate clean data suitable for analysis by imputing missing values ​​and removing outliers from collected information.

[0457] "Analysis methods" refer to means of using generative models, etc., to identify urban issues and trends using pre-processed data.

[0458] A "predictive tool" is a means of simulating different design proposals in urban development and providing prediction results.

[0459] "Allocation optimization methods" are means of deriving the optimal allocation of resources based on simulation results and proposing efficient urban layout plans.

[0460] A "participation platform" is a means of providing a space where residents can post their opinions on urban development plans.

[0461] "Update methods" refer to means of incorporating opinions obtained from information sources and feedback into the next plan update.

[0462] The urban development planning system according to the present invention is designed to collect and analyze diverse urban-related information in real time and propose an optimal development plan. The system is configured in which a server, terminals, and users each play their respective roles and work together.

[0463] The server retrieves necessary data from sources such as traffic sensors installed within the city, databases providing demographic information, and APIs from public institutions. This data is collected via the internet or dedicated communication networks. Following data collection, the server preprocesses the data using cleansing methods to correct for missing or outlier values. A database management system (DBMS) is utilized to ensure data integrity and consistency.

[0464] The terminal uses a generative AI model to analyze the cleansed data. For example, it inputs a prompt such as "Analyze the correlation between traffic flow and population dynamics" into the generative AI model, which then analyzes population dynamics and traffic patterns within the city. Based on the results of this analysis, the server simulates various scenarios for urban development. The terminal also uses the analysis results to derive the optimal resource allocation using allocation optimization methods, employing computational techniques such as evolutionary algorithms.

[0465] Users can provide feedback on proposed urban development plans through a citizen participation platform. For example, in response to a prompt such as "Please propose requirements for new public facilities," users can describe specific needs and suggestions. User feedback is considered by the server when the plan is updated next, contributing to the development of practical and flexible urban development plans.

[0466] In this way, this system utilizes information technology and generative AI models to address complex urban challenges and rapidly formulate feasible development plans.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The server collects information from urban traffic sensors, demographic databases, and public institution APIs. The input sources are diverse, including traffic conditions and population changes. This collected data is stored in a database. During the data harvesting process, the data is updated according to the collection frequency, ensuring real-time accuracy.

[0470] Step 2:

[0471] The server preprocesses the collected information. This preprocessing includes data cleansing, such as imputing missing values ​​and removing outliers. The input is the collected raw data, and the output is a clean dataset with ensured consistency. This process is automated using scripts.

[0472] Step 3:

[0473] The terminal begins the analysis using cleansed data. The input data is pre-processed data received from the server. This analysis utilizes a generative AI model. The specific prompt "Analyze the correlation between traffic flow and demographics" is input to the model, and demographics and traffic patterns are analyzed. The output provides detailed insights into population growth and traffic congestion patterns in a specific region.

[0474] Step 4:

[0475] The server runs simulations based on the data obtained from the analysis. The input is the analysis results from the previous step, and a hardware system is used to test different urban development scenarios in a virtual environment. For example, predicting the impact of new road infrastructure on traffic. The output is the data provided as the prediction results for each simulated scenario.

[0476] Step 5:

[0477] The terminal initiates optimization processing based on the simulation results. The input is the simulation's predicted data, and allocation optimization methods are applied. This process utilizes evolutionary algorithms to derive the optimal placement of public facilities and transportation systems. The output is an efficient and cost-effective urban planning proposal.

[0478] Step 6:

[0479] Users submit their opinions on the proposed plan through the participating platform. The necessary input for this is the user's own ideas and suggestions. For example, they might submit their opinions through a prompt such as, "Please propose the requirements for the new public facility." The output is that the submitted feedback is stored in a database and reflected in the next plan update.

[0480] (Application Example 1)

[0481] 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."

[0482] In modern cities, sustainable development is essential, but achieving optimal urban planning while effectively utilizing diverse data and promoting citizen participation is challenging. In particular, it is necessary to provide citizens with real-time information on the progress and predicted impacts of urban design and to incorporate their opinions into the planning process.

[0483] 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.

[0484] In this invention, the server includes means for collecting information from multiple data sources related to the city, means for preprocessing the collected information, and means for visualizing the progress of urban design and providing information in real time. This allows citizens to easily provide their opinions on urban design plans, and enables sustainable urban development that reflects those opinions.

[0485] "Multiple data sources related to a city" refers to diverse and convergent sources of information within a city, such as traffic information, demographic information, and public service information.

[0486] "Means of collection" refers to a method or apparatus for obtaining necessary information from specified data sources and systematically storing it.

[0487] "Preprocessing" refers to the process of converting collected raw data into a format suitable for analysis, and includes processes such as imputing missing values ​​and removing outliers.

[0488] "Means of analysis and identification" refers to methods or devices that use data analysis techniques to identify problems and trends in cities and to obtain data-based insights.

[0489] "Simulation means" refers to a method or apparatus for virtually recreating different scenarios in urban development planning and evaluating their impact.

[0490] "Optimization means" refers to a calculation method or device that uses the results of simulations to determine the optimal allocation and structure of resources.

[0491] "Means for citizens to provide feedback" refers to a platform or method for collecting, analyzing, and incorporating citizen feedback on urban planning.

[0492] "Means of visualization and real-time information provision" refers to technologies or devices that visually display the progress and predicted impacts of urban design and provide users with information immediately.

[0493] The system implementing this invention provides a platform for collecting and analyzing various urban-related data and proposing sustainable urban development to society. It operates with three main components: a server, terminals, and users.

[0494] The server collects urban-related data such as traffic information, demographics, and public institution information from multiple data sources. The collected information is preprocessed using software such as Pandas and NumPy to impute missing values ​​and remove outliers, and then securely stored in the cloud.

[0495] Subsequently, the device performs analysis using machine learning frameworks such as TensorFlow and PyTorch. This identifies patterns in population dynamics and transportation, revealing urban problems and trends. The analysis results are then graphed using visualization tools such as Matplotlib and Plotly, making them easily viewable.

[0496] Next, the server simulates different urban design scenarios (for example, the introduction of new public transport lines). Here, it uses evolutionary algorithms to optimize and derive resource allocation. Finally, it generates an optimal configuration model of the urban structure based on the optimized results.

[0497] Users can participate in the process as citizens and offer their opinions. To this end, a smart device interface built with React Native allows them to view the progress and predicted impacts of urban design in real time and submit feedback. This feedback is aggregated on a server and used to inform future planning.

[0498] As a concrete example, a plan for introducing a new bicycle sharing service is presented. In this scenario, users provide feedback on its necessity and benefits. The following prompts are used when utilizing the generative AI model.

[0499] Example prompt: "As a citizen, please give your opinion on the introduction of a bicycle-sharing service as a new public transportation system. What advantages do you see?"

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The server collects traffic information, demographic information, and public institution information from city-related data sources and preprocesses it using Pandas and NumPy. Specifically, it generates a dataset suitable for analysis by imputing missing values ​​and removing outliers. The input is raw data, and the output is a consistent, preprocessed dataset.

[0503] Step 2:

[0504] The terminal performs analysis using a pre-processed dataset. It utilizes machine learning frameworks such as TensorFlow and PyTorch to identify population dynamics and analyze transportation patterns. The analysis identifies urban challenges and trends, which are then output as the analysis results. The input is a pre-processed dataset, and the output is a visualized analysis result.

[0505] Step 3:

[0506] The server uses the obtained analysis results to simulate different urban design scenarios. Using evolutionary algorithms, it virtually evaluates, for example, the impact of introducing new transportation routes on traffic patterns. The input is the analysis results, and the output is the data for each simulation scenario.

[0507] Step 4:

[0508] The server uses simulation results to perform optimization and proposes the optimal resource allocation. It generates a model of the urban structure and derives an efficient allocation of resources. The input is data from the simulation scenario, and the output is the optimized urban structure model.

[0509] Step 5:

[0510] Users can view the progress of urban design and simulation results in real time from a citizen's perspective and provide feedback. This process utilizes an interface built with React Native. Feedback is sent using prompts as an example, and as a result, opinion data is collected. The input is user feedback, and the output is feedback data.

[0511] Step 6:

[0512] The server incorporates the collected feedback into the next urban planning process, contributing to more sustainable urban development. This generates highly feasible urban planning proposals. The input is feedback data, and the output is the improved urban planning proposal.

[0513] 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.

[0514] This invention is an AI-powered urban planning system aimed at recognizing and considering user emotions during the urban development plan proposal process. The specific configuration and embodiments of this system are described below.

[0515] The system consists of a server, terminals, and users. The server collects data from various data sources within the city and stores it in a database for analysis. This data includes traffic volume, demographics, and environmental information. After preprocessing the collected data, the terminals perform analysis using machine learning models. Clustering techniques are used in the analysis to clarify the city's demographics and traffic patterns.

[0516] Furthermore, the server uses these analysis results to perform urban development simulations. Genetic algorithms are used when different development scenarios are simulated and optimal resource allocations are proposed. This generates an optimal placement model for public facilities and infrastructure.

[0517] Users provide opinions and feedback through a citizen participation platform. In response, an emotion engine operates, using natural language processing technology to analyze user comments. The emotional state embedded in the user's opinion (e.g., "satisfied," "dissatisfied," "interested") is recognized. The results of this emotion recognition are used to evaluate and improve urban planning.

[0518] For example, suppose a user adds feedback to a proposed new public transport route, expressing "I'm full of anticipation." In this case, the emotion engine recognizes the positive emotion and uses it as an indicator to strengthen the momentum of this route proposal. On the other hand, if many negative emotions are recognized, the plan proposal will be considered based on that feedback.

[0519] By incorporating user emotions into urban planning in this way, it becomes possible to formulate more flexible and adaptive development plans. This system goes beyond mere data analysis by integrating human emotions into digital analysis, realizing sustainable urban development that reflects social needs and emotions.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The server periodically collects real-time data from urban data sources. Specifically, it retrieves the latest information from traffic sensors and demographic databases and securely stores it in the database.

[0523] Step 2:

[0524] The server preprocesses the collected data. This process involves detecting missing values ​​in the dataset and filling them in using imputation techniques. It also detects outliers and applies appropriate filtering to generate a clean dataset.

[0525] Step 3:

[0526] The device applies machine learning models to analyze pre-processed data. It uses clustering algorithms to classify population dynamics and traffic patterns within cities and analyze trends and problems in specific areas.

[0527] Step 4:

[0528] The server simulates multiple urban development scenarios based on the analysis results. For example, it performs modeling to predict the impact on surrounding areas when a new public transport route is planned.

[0529] Step 5:

[0530] The terminal uses the simulation results to perform an optimization process. Based on a genetic algorithm, it calculates the optimal placement of public facilities and transportation infrastructure and generates a specific placement model. This model aims to maximize cost efficiency and citizen convenience.

[0531] Step 6:

[0532] Users provide feedback on urban planning proposals through a citizen participation platform. This feedback is often provided in written form and can include specific opinions and emotions.

[0533] Step 7:

[0534] The device activates an emotion engine to analyze user feedback. Using natural language processing techniques, it analyzes keywords and context in the text to identify the user's emotional state. For example, if the feedback includes positive expressions such as "I'm looking forward to it," the device recognizes a positive attitude towards the plan.

[0535] Step 8:

[0536] The server incorporates the results of sentiment analysis into the final urban development plan proposal. Proposals that receive a large number of positive responses are prioritized, while those with a large number of negative responses are re-evaluated and adjusted. This allows for the implementation of flexible and adaptive plans that take residents' feelings into consideration.

[0537] (Example 2)

[0538] 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."

[0539] In urban development planning, traditional methods often fail to adequately consider citizens' opinions and feelings, resulting in plans that do not align with their needs. Furthermore, effectively analyzing information within the city and achieving optimal resource allocation has been challenging. This has hindered flexible and sustainable urban development.

[0540] 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.

[0541] In this invention, the server includes means for collecting information from multiple sources related to the city, means for preprocessing and analyzing the collected information, and means for analyzing sentiment based on the opinions of participants. This allows urban development plans to be adjusted to take into account the sentiments and opinions of citizens, enabling optimal resource allocation.

[0542] "Information gathering methods" refer to the function of obtaining necessary information from multiple sources related to a city.

[0543] "Preprocessing methods" refer to functions that prepare collected information for analysis by performing tasks such as noise reduction and format standardization before the information is analyzed, thereby creating a state suitable for data analysis.

[0544] "Analysis tools" refer to functions that perform calculations and analyses to identify urban trends and challenges using pre-processed information.

[0545] A "simulation tool" is a function that virtually reproduces different urban development plans and predicts their impact.

[0546] An "optimization tool" is a function that proposes the optimal resource allocation and facility layout based on the results of analysis and simulation.

[0547] "Means of participation" refer to functions used by participants to provide opinions and feedback on urban development plans.

[0548] "Methods for analyzing emotions" refers to analytical functions that extract emotions from participants' opinions and reflect them in the plan.

[0549] This invention is an AI system for formulating flexible and sustainable plans in urban development projects that take into account the opinions and feelings of citizens. The system consists of a server, terminals, and users.

[0550] The server collects data such as traffic volume, demographics, and environmental information from various sources within the city and stores this data in a database. IoT sensors and publicly available government databases are used for data collection. The data is updated in real time, allowing for immediate responses to dynamic changes in the city.

[0551] The terminal preprocesses the data received from the server using a machine learning library such as Python's Scikit-learn, and then analyzes it using clustering techniques. This clarifies urban demographics and traffic patterns, providing data-driven insights.

[0552] The server then runs a simulation using a genetic algorithm based on the analysis results. This process virtually tests various urban development scenarios to find the optimal placement of public facilities and infrastructure. At this stage, a concrete facility placement plan is created, and the necessary resources are appropriately allocated.

[0553] Users provide opinions and feedback in natural language through a citizen participation platform. The server then uses natural language processing technology to analyze the user's emotions in response to these comments. The emotion analysis engine identifies positive expressions such as "full of anticipation" and negative expressions such as "dissatisfied," and incorporates this into the evaluation of urban planning.

[0554] As a concrete example of its use, if a user comments on a proposed new transportation route saying, "I'm full of excitement," this feedback positively influences the plan and strengthens the momentum of the proposal. Conversely, if there are many negative opinions, the plan will be re-evaluated. In this way, flexible urban development that is tailored to the needs of citizens becomes possible.

[0555] An example of a prompt for a generative AI model might be, "Please tell me how to collect and analyze public opinion on new public transport routes."

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The server acquires data such as traffic volume, demographics, and environmental information from various sources within the city. This data collection utilizes IoT sensors and publicly available government databases. The data collected as input is raw, unprocessed data, which is stored in the database. The data output is a database in which this raw data is stored in a structured format.

[0559] Step 2:

[0560] The terminal receives raw data from the server and performs preprocessing such as noise reduction and format standardization. This preprocessing prepares the data for analysis. The input is the raw data provided by the server, and the output is processed, clean data.

[0561] Step 3:

[0562] The terminal performs analysis using clustering techniques with pre-processed data. This analysis utilizes a Python machine learning library. The input is pre-processed data, and the output provides insights into urban demographics and traffic patterns.

[0563] Step 4:

[0564] The server uses the analysis results obtained from the terminal to perform urban development simulations. At this stage, a genetic algorithm is used to test various development scenarios and generate an optimal placement model for public facilities and infrastructure. The input is the analysis results, and the output is the optimized urban development plan.

[0565] Step 5:

[0566] Users provide their opinions and feedback through a civic participation platform. The input provided is the user's natural language comments. The server analyzes this feedback using natural language processing techniques to identify the user's emotions. The output is data indicating their emotional state.

[0567] Step 6:

[0568] The server adjusts urban planning based on analyzed sentiment data to create a final, optimal plan. Inputs are user sentiment data and initial simulation results, while output is an improved urban planning proposal reflecting citizen feedback. This allows for flexible and adaptive urban development based on citizen needs.

[0569] (Application Example 2)

[0570] 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."

[0571] While objective, data-driven analysis is essential in urban development planning, effectively considering the feelings and opinions of individual stakeholders while formulating plans remains a challenging task. A participatory decision-making process in urban development, where stakeholders' feelings are reflected in the planning, is necessary to create more adaptive and sustainable urban plans.

[0572] 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.

[0573] In this invention, the server includes a data collection means for collecting information from multiple data sources related to the city, a data preprocessing means for preprocessing the collected information, and an emotion analysis means for recognizing and evaluating emotional states. This makes it possible to effectively reflect the emotions of residents in urban development plans and to formulate flexible and adaptive urban plans.

[0574] "Information gathering means" refers to a device or method for collecting necessary information from multiple sources related to a city.

[0575] "Preprocessing means" refers to an apparatus or method of processing that is performed to convert collected information into an analyzable format.

[0576] "Analysis means" refers to a device or method for identifying urban problems and trends using pre-processed information.

[0577] A "simulation means" is a device or method for virtually executing different scenarios in urban development and evaluating the predicted impacts.

[0578] An "optimization means" is a device or method that proposes the optimal resource allocation or arrangement based on simulation results.

[0579] "Means of participation" refers to devices or methods for individual members to provide opinions and feedback on urban development plans.

[0580] "Emotional analysis means" refers to a device or method for recognizing and evaluating the emotional state contained in the feedback provided by a member.

[0581] "Evaluation tools" refer to devices or methods for evaluating or modifying urban planning based on the results of sentiment analysis.

[0582] The system implementing this invention combines various technical means to effectively reflect the sentiments of its members in the development of a smart city. A server collects information such as traffic volume, demographics, and environmental data from diverse sources related to the city, using a data collection device. This information is then formatted into an analyzable form by a preprocessor. This process includes data cleaning and integrity checks.

[0583] The pre-processed information is analyzed by an analysis device on the terminal to identify demographic trends and traffic patterns. A learning-based model is used to classify the information. The analysis results are sent to a server, where different development scenarios are tested in a simulation device. The impact of the different scenarios is predicted, and the results are evaluated by an optimization device to propose the optimal resource allocation.

[0584] Individual members can provide feedback on the urban development plan using a participation device. In this process, an emotion analysis device recognizes the emotional state contained in the comments using natural language processing technology. These results are then used by an evaluation device to assess and revise the urban plan. As important feedback, if the emotion contained in a comment is evaluated as "full of anticipation," that item is recognized as a driving force. On the other hand, if negative emotions are recognized, revisions to the development scenario should be considered.

[0585] For example, if a user expresses their expectations regarding a proposed new public transport route, the sentiment analyzer will correctly recognize that emotion and use a prompt message in the form of, "Please recognize the emotion of this feedback and assist in how to reflect it in the urban planning system."

[0586] The hardware and software used include smartphones, servers, Python, and natural language processing libraries. Specifically, libraries such as NLTK and custom models for sentiment recognition are utilized. This enables flexible and sustainable development that realistically and effectively reflects the emotions of residents in urban planning.

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The server collects information such as traffic volume, demographics, and environmental data from multiple sources related to the city. The collected information is input into the server, where data cleaning and integrity checks are performed before it is stored in the database, and clean data is output.

[0590] Step 2:

[0591] The terminal preprocesses the clean data received from the server. Here, it performs format conversion and removes meaningless data to convert the data into a parseable format, and outputs the preprocessed data.

[0592] Step 3:

[0593] The terminal uses pre-processed data to apply machine learning models and classify demographics and traffic patterns. The input data is processed by an analysis device, and classification results regarding demographics and traffic patterns are output.

[0594] Step 4:

[0595] The server uses the classification results to run simulations of different urban development scenarios. The simulation device receives these simulations, tests the predicted effects of the different scenarios in a virtual environment, and outputs the simulation results.

[0596] Step 5:

[0597] The server uses the simulation results and employs evolutionary algorithms to determine the optimal resource allocation. The optimization device performs optimization calculations according to the scenario allocation and outputs the optimal resource allocation plan.

[0598] Step 6:

[0599] Users input feedback into urban development plans using a participation device. Comments provided by users are input, and an emotion analysis device analyzes those comments and outputs the emotional state.

[0600] Step 7:

[0601] The server receives the output from the emotion analysis device and appropriately evaluates and modifies the urban plan. Based on the outputted emotional state, the evaluation device modifies the plan and outputs a newly adjusted urban plan proposal.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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".

[0619] The AI ​​urban planning system according to the present invention consists of multiple components for collecting various urban-related data and making proposals for sustainable urban development. This system operates according to the following procedure, with the server, terminals, and users each playing their respective roles.

[0620] Data collection

[0621] The server collects data from various data sources within the city, such as traffic sensors, demographic databases, and public institution APIs. This data is updated daily or at a specified frequency and stored in the database.

[0622] Data preprocessing

[0623] The server performs preprocessing to maintain the integrity of the collected data. Specifically, it generates a clean dataset by imputing missing values ​​and removing outliers.

[0624] Data Analysis

[0625] The device performs analysis using pre-processed data. Here, machine learning models are applied to analyze urban demographics and traffic patterns. For example, it can identify population growth trends and traffic congestion patterns in specific areas.

[0626] Running the simulation

[0627] The server uses simulation tools to run different urban development scenarios. For example, it can virtually predict the impact of constructing a new train station on local traffic patterns.

[0628] Optimization of resource allocation

[0629] Based on the simulation results, the terminal proposes efficient resource allocation using optimization methods. For example, it optimizes the location of public facilities and develops a layout plan that maximizes citizen convenience and cost efficiency.

[0630] Promoting citizen participation

[0631] Users can provide feedback on the proposed plan through a citizen participation platform. This user feedback is collected by the system and considered in future plan updates. For example, a user might post an opinion about the need for a new public transport route, and that opinion might be reflected in the plan.

[0632] The system's operation, as described above, enables the realization of more precise and efficient urban development plans. This system employs a data-driven approach and, by utilizing AI technology, is expected to contribute to solving complex urban challenges.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The server collects data from various data sources within the city. Specifically, it obtains real-time traffic volume data from public transport APIs and the latest demographic information from demographic databases. This data is then cleansed and stored in the database.

[0636] Step 2:

[0637] The server performs preprocessing on the collected data. Specifically, it identifies missing values ​​in the data and fills them in using appropriate imputation methods (e.g., imputing the mean or median). It also detects and removes or corrects outliers caused by sensor failures or other issues.

[0638] Step 3:

[0639] The terminal begins analysis using pre-processed data. It applies machine learning algorithms to cluster urban population trends and traffic patterns. For example, it identifies traffic congestion patterns by time of day in a specific area and predicts future traffic congestion locations.

[0640] Step 4:

[0641] The server performs simulations based on the analysis results. It sets up various urban development scenarios and predicts their effects. As a specific example, it models the changes in pedestrian flow to surrounding areas when a new railway station is opened.

[0642] Step 5:

[0643] The terminal receives the simulation results and performs optimization processing to determine efficient resource allocation. Using a genetic algorithm, it calculates the placement of public facilities while considering cost-effectiveness and formulates the optimal resource allocation plan.

[0644] Step 6:

[0645] Users input their opinions and feedback on presented urban planning proposals through a citizen participation platform. This feedback is later analyzed and reflected in future urban development proposals, making it possible to incorporate the opinions of local residents into the planning process.

[0646] (Example 1)

[0647] 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".

[0648] Modern cities face a variety of complex challenges, including rapid population growth, traffic congestion, and environmental impacts. To comprehensively and efficiently address these challenges, sustainable development plans are needed that collect and analyze vast amounts of data and incorporate residents' opinions. However, traditional methods involve these processes in isolation, resulting in plans that are not reflected in real time and making optimal resource allocation and urban design difficult.

[0649] 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.

[0650] In this invention, the server includes an acquisition means for obtaining information from an information source, a preprocessing means for cleansing the acquired information, an analysis means, a prediction means for simulating different urban design proposals, and a feedback means for providing a participatory platform where residents can post their opinions. This enables real-time data collection and analysis, flexible urban development planning, and rapid incorporation of residents' feedback.

[0651] "Means of acquisition" refers to the means of obtaining necessary information from information sources related to the city.

[0652] "Cleaning methods" refer to techniques used to generate clean data suitable for analysis by imputing missing values ​​and removing outliers from collected information.

[0653] "Analysis methods" refer to means of using generative models, etc., to identify urban issues and trends using pre-processed data.

[0654] A "predictive tool" is a means of simulating different design proposals in urban development and providing prediction results.

[0655] "Allocation optimization methods" are means of deriving the optimal allocation of resources based on simulation results and proposing efficient urban layout plans.

[0656] A "participation platform" is a means of providing a space where residents can post their opinions on urban development plans.

[0657] "Update methods" refer to means of incorporating opinions obtained from information sources and feedback into the next plan update.

[0658] The urban development planning system according to the present invention is designed to collect and analyze diverse urban-related information in real time and propose an optimal development plan. The system is configured in which a server, terminals, and users each play their respective roles and work together.

[0659] The server retrieves necessary data from sources such as traffic sensors installed within the city, databases providing demographic information, and APIs from public institutions. This data is collected via the internet or dedicated communication networks. Following data collection, the server preprocesses the data using cleansing methods to correct for missing or outlier values. A database management system (DBMS) is utilized to ensure data integrity and consistency.

[0660] The terminal uses a generative AI model to analyze the cleansed data. For example, it inputs a prompt such as "Analyze the correlation between traffic flow and population dynamics" into the generative AI model, which then analyzes population dynamics and traffic patterns within the city. Based on the results of this analysis, the server simulates various scenarios for urban development. The terminal also uses the analysis results to derive the optimal resource allocation using allocation optimization methods, employing computational techniques such as evolutionary algorithms.

[0661] Users can provide feedback on proposed urban development plans through a citizen participation platform. For example, in response to a prompt such as "Please propose requirements for new public facilities," users can describe specific needs and suggestions. User feedback is considered by the server when the plan is updated next, contributing to the development of practical and flexible urban development plans.

[0662] In this way, this system utilizes information technology and generative AI models to address complex urban challenges and rapidly formulate feasible development plans.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The server collects information from urban traffic sensors, demographic databases, and public institution APIs. The input sources are diverse, including traffic conditions and population changes. This collected data is stored in a database. During the data harvesting process, the data is updated according to the collection frequency, ensuring real-time accuracy.

[0666] Step 2:

[0667] The server preprocesses the collected information. This preprocessing includes data cleansing, such as imputing missing values ​​and removing outliers. The input is the collected raw data, and the output is a clean dataset with ensured consistency. This process is automated using scripts.

[0668] Step 3:

[0669] The terminal begins the analysis using cleansed data. The input data is pre-processed data received from the server. This analysis utilizes a generative AI model. The specific prompt "Analyze the correlation between traffic flow and demographics" is input to the model, and demographics and traffic patterns are analyzed. The output provides detailed insights into population growth and traffic congestion patterns in a specific region.

[0670] Step 4:

[0671] The server runs simulations based on the data obtained from the analysis. The input is the analysis results from the previous step, and a hardware system is used to test different urban development scenarios in a virtual environment. For example, predicting the impact of new road infrastructure on traffic. The output is the data provided as the prediction results for each simulated scenario.

[0672] Step 5:

[0673] The terminal initiates optimization processing based on the simulation results. The input is the simulation's predicted data, and allocation optimization methods are applied. This process utilizes evolutionary algorithms to derive the optimal placement of public facilities and transportation systems. The output is an efficient and cost-effective urban planning proposal.

[0674] Step 6:

[0675] Users submit their opinions on the proposed plan through the participating platform. The necessary input for this is the user's own ideas and suggestions. For example, they might submit their opinions through a prompt such as, "Please propose the requirements for the new public facility." The output is that the submitted feedback is stored in a database and reflected in the next plan update.

[0676] (Application Example 1)

[0677] 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".

[0678] In modern cities, sustainable development is essential, but achieving optimal urban planning while effectively utilizing diverse data and promoting citizen participation is challenging. In particular, it is necessary to provide citizens with real-time information on the progress and predicted impacts of urban design and to incorporate their opinions into the planning process.

[0679] 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.

[0680] In this invention, the server includes means for collecting information from multiple data sources related to the city, means for preprocessing the collected information, and means for visualizing the progress of urban design and providing information in real time. This allows citizens to easily provide their opinions on urban design plans, and enables sustainable urban development that reflects those opinions.

[0681] "Multiple data sources related to a city" refers to diverse and convergent sources of information within a city, such as traffic information, demographic information, and public service information.

[0682] "Means of collection" refers to a method or apparatus for obtaining necessary information from specified data sources and systematically storing it.

[0683] "Preprocessing" refers to the process of converting collected raw data into a format suitable for analysis, and includes processes such as imputing missing values ​​and removing outliers.

[0684] "Means of analysis and identification" refers to methods or devices that use data analysis techniques to identify problems and trends in cities and to obtain data-based insights.

[0685] "Simulation means" refers to a method or apparatus for virtually recreating different scenarios in urban development planning and evaluating their impact.

[0686] "Optimization means" refers to a calculation method or device that uses the results of simulations to determine the optimal allocation and structure of resources.

[0687] "Means for citizens to provide feedback" refers to a platform or method for collecting, analyzing, and incorporating citizen feedback on urban planning.

[0688] "Means of visualization and real-time information provision" refers to technologies or devices that visually display the progress and predicted impacts of urban design and provide users with information immediately.

[0689] The system implementing this invention provides a platform for collecting and analyzing various urban-related data and proposing sustainable urban development to society. It operates with three main components: a server, terminals, and users.

[0690] The server collects urban-related data such as traffic information, demographics, and public institution information from multiple data sources. The collected information is preprocessed using software such as Pandas and NumPy to impute missing values ​​and remove outliers, and then securely stored in the cloud.

[0691] Subsequently, the device performs analysis using machine learning frameworks such as TensorFlow and PyTorch. This identifies patterns in population dynamics and transportation, revealing urban problems and trends. The analysis results are then graphed using visualization tools such as Matplotlib and Plotly, making them easily viewable.

[0692] Next, the server simulates different urban design scenarios (for example, the introduction of new public transport lines). Here, it uses evolutionary algorithms to optimize and derive resource allocation. Finally, it generates an optimal configuration model of the urban structure based on the optimized results.

[0693] Users can participate in the process as citizens and offer their opinions. To this end, a smart device interface built with React Native allows them to view the progress and predicted impacts of urban design in real time and submit feedback. This feedback is aggregated on a server and used to inform future planning.

[0694] As a concrete example, a plan for introducing a new bicycle sharing service is presented. In this scenario, users provide feedback on its necessity and benefits. The following prompts are used when utilizing the generative AI model.

[0695] Example prompt: "As a citizen, please give your opinion on the introduction of a bicycle-sharing service as a new public transportation system. What advantages do you see?"

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The server collects traffic information, demographic information, and public institution information from city-related data sources and preprocesses it using Pandas and NumPy. Specifically, it generates a dataset suitable for analysis by imputing missing values ​​and removing outliers. The input is raw data, and the output is a consistent, preprocessed dataset.

[0699] Step 2:

[0700] The terminal performs analysis using a pre-processed dataset. It utilizes machine learning frameworks such as TensorFlow and PyTorch to identify population dynamics and analyze transportation patterns. The analysis identifies urban challenges and trends, which are then output as the analysis results. The input is a pre-processed dataset, and the output is a visualized analysis result.

[0701] Step 3:

[0702] The server uses the obtained analysis results to simulate different urban design scenarios. Using evolutionary algorithms, it virtually evaluates, for example, the impact of introducing new transportation routes on traffic patterns. The input is the analysis results, and the output is the data for each simulation scenario.

[0703] Step 4:

[0704] The server uses simulation results to perform optimization and proposes the optimal resource allocation. It generates a model of the urban structure and derives an efficient allocation of resources. The input is data from the simulation scenario, and the output is the optimized urban structure model.

[0705] Step 5:

[0706] Users can view the progress of urban design and simulation results in real time from a citizen's perspective and provide feedback. This process utilizes an interface built with React Native. Feedback is sent using prompts as an example, and as a result, opinion data is collected. The input is user feedback, and the output is feedback data.

[0707] Step 6:

[0708] The server incorporates the collected feedback into the next urban planning process, contributing to more sustainable urban development. This generates highly feasible urban planning proposals. The input is feedback data, and the output is the improved urban planning proposal.

[0709] 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.

[0710] This invention is an AI-powered urban planning system aimed at recognizing and considering user emotions during the urban development plan proposal process. The specific configuration and embodiments of this system are described below.

[0711] The system consists of a server, terminals, and users. The server collects data from various data sources within the city and stores it in a database for analysis. This data includes traffic volume, demographics, and environmental information. After preprocessing the collected data, the terminals perform analysis using machine learning models. Clustering techniques are used in the analysis to clarify the city's demographics and traffic patterns.

[0712] Furthermore, the server uses these analysis results to perform urban development simulations. Genetic algorithms are used when different development scenarios are simulated and optimal resource allocations are proposed. This generates an optimal placement model for public facilities and infrastructure.

[0713] Users provide opinions and feedback through a citizen participation platform. In response, an emotion engine operates, using natural language processing technology to analyze user comments. The emotional state embedded in the user's opinion (e.g., "satisfied," "dissatisfied," "interested") is recognized. The results of this emotion recognition are used to evaluate and improve urban planning.

[0714] For example, suppose a user adds feedback to a proposed new public transport route, expressing "I'm full of anticipation." In this case, the emotion engine recognizes the positive emotion and uses it as an indicator to strengthen the momentum of this route proposal. On the other hand, if many negative emotions are recognized, the plan proposal will be considered based on that feedback.

[0715] By incorporating user emotions into urban planning in this way, it becomes possible to formulate more flexible and adaptive development plans. This system goes beyond mere data analysis by integrating human emotions into digital analysis, realizing sustainable urban development that reflects social needs and emotions.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] The server periodically collects real-time data from urban data sources. Specifically, it retrieves the latest information from traffic sensors and demographic databases and securely stores it in the database.

[0719] Step 2:

[0720] The server preprocesses the collected data. This process involves detecting missing values ​​in the dataset and filling them in using imputation techniques. It also detects outliers and applies appropriate filtering to generate a clean dataset.

[0721] Step 3:

[0722] The device applies machine learning models to analyze pre-processed data. It uses clustering algorithms to classify population dynamics and traffic patterns within cities and analyze trends and problems in specific areas.

[0723] Step 4:

[0724] The server simulates multiple urban development scenarios based on the analysis results. For example, it performs modeling to predict the impact on surrounding areas when a new public transport route is planned.

[0725] Step 5:

[0726] The terminal uses the simulation results to perform an optimization process. Based on a genetic algorithm, it calculates the optimal placement of public facilities and transportation infrastructure and generates a specific placement model. This model aims to maximize cost efficiency and citizen convenience.

[0727] Step 6:

[0728] Users provide feedback on urban planning proposals through a citizen participation platform. This feedback is often provided in written form and can include specific opinions and emotions.

[0729] Step 7:

[0730] The device activates an emotion engine to analyze user feedback. Using natural language processing techniques, it analyzes keywords and context in the text to identify the user's emotional state. For example, if the feedback includes positive expressions such as "I'm looking forward to it," the device recognizes a positive attitude towards the plan.

[0731] Step 8:

[0732] The server incorporates the results of sentiment analysis into the final urban development plan proposal. Proposals that receive a large number of positive responses are prioritized, while those with a large number of negative responses are re-evaluated and adjusted. This allows for the implementation of flexible and adaptive plans that take residents' feelings into consideration.

[0733] (Example 2)

[0734] 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".

[0735] In urban development planning, traditional methods often fail to adequately consider citizens' opinions and feelings, resulting in plans that do not align with their needs. Furthermore, effectively analyzing information within the city and achieving optimal resource allocation has been challenging. This has hindered flexible and sustainable urban development.

[0736] 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.

[0737] In this invention, the server includes means for collecting information from multiple sources related to the city, means for preprocessing and analyzing the collected information, and means for analyzing sentiment based on the opinions of participants. This allows urban development plans to be adjusted to take into account the sentiments and opinions of citizens, enabling optimal resource allocation.

[0738] "Information gathering methods" refer to the function of obtaining necessary information from multiple sources related to a city.

[0739] "Preprocessing methods" refer to functions that prepare collected information for analysis by performing tasks such as noise reduction and format standardization before the information is analyzed, thereby creating a state suitable for data analysis.

[0740] "Analysis tools" refer to functions that perform calculations and analyses to identify urban trends and challenges using pre-processed information.

[0741] A "simulation tool" is a function that virtually reproduces different urban development plans and predicts their impact.

[0742] An "optimization tool" is a function that proposes the optimal resource allocation and facility layout based on the results of analysis and simulation.

[0743] "Means of participation" refer to functions used by participants to provide opinions and feedback on urban development plans.

[0744] "Methods for analyzing emotions" refers to analytical functions that extract emotions from participants' opinions and reflect them in the plan.

[0745] This invention is an AI system for formulating flexible and sustainable plans in urban development projects that take into account the opinions and feelings of citizens. The system consists of a server, terminals, and users.

[0746] The server collects data such as traffic volume, demographics, and environmental information from various sources within the city and stores this data in a database. IoT sensors and publicly available government databases are used for data collection. The data is updated in real time, allowing for immediate responses to dynamic changes in the city.

[0747] The terminal preprocesses the data received from the server using a machine learning library such as Python's Scikit-learn, and then analyzes it using clustering techniques. This clarifies urban demographics and traffic patterns, providing data-driven insights.

[0748] The server then runs a simulation using a genetic algorithm based on the analysis results. This process virtually tests various urban development scenarios to find the optimal placement of public facilities and infrastructure. At this stage, a concrete facility placement plan is created, and the necessary resources are appropriately allocated.

[0749] Users provide opinions and feedback in natural language through a citizen participation platform. The server then uses natural language processing technology to analyze the user's emotions in response to these comments. The emotion analysis engine identifies positive expressions such as "full of anticipation" and negative expressions such as "dissatisfied," and incorporates this into the evaluation of urban planning.

[0750] As a concrete example of its use, if a user comments on a proposed new transportation route saying, "I'm full of excitement," this feedback positively influences the plan and strengthens the momentum of the proposal. Conversely, if there are many negative opinions, the plan will be re-evaluated. In this way, flexible urban development that is tailored to the needs of citizens becomes possible.

[0751] An example of a prompt for a generative AI model might be, "Please tell me how to collect and analyze public opinion on new public transport routes."

[0752] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0753] Step 1:

[0754] The server acquires data such as traffic volume, demographics, and environmental information from various sources within the city. This data collection utilizes IoT sensors and publicly available government databases. The data collected as input is raw, unprocessed data, which is stored in the database. The data output is a database in which this raw data is stored in a structured format.

[0755] Step 2:

[0756] The terminal receives raw data from the server and performs preprocessing such as noise reduction and format standardization. This preprocessing prepares the data for analysis. The input is the raw data provided by the server, and the output is processed, clean data.

[0757] Step 3:

[0758] The terminal performs analysis using clustering techniques with pre-processed data. This analysis utilizes a Python machine learning library. The input is pre-processed data, and the output provides insights into urban demographics and traffic patterns.

[0759] Step 4:

[0760] The server uses the analysis results obtained from the terminal to perform urban development simulations. At this stage, a genetic algorithm is used to test various development scenarios and generate an optimal placement model for public facilities and infrastructure. The input is the analysis results, and the output is the optimized urban development plan.

[0761] Step 5:

[0762] Users provide their opinions and feedback through a civic participation platform. The input provided is the user's natural language comments. The server analyzes this feedback using natural language processing techniques to identify the user's emotions. The output is data indicating their emotional state.

[0763] Step 6:

[0764] The server adjusts urban planning based on analyzed sentiment data to create a final, optimal plan. Inputs are user sentiment data and initial simulation results, while output is an improved urban planning proposal reflecting citizen feedback. This allows for flexible and adaptive urban development based on citizen needs.

[0765] (Application Example 2)

[0766] 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".

[0767] While objective, data-driven analysis is essential in urban development planning, effectively considering the feelings and opinions of individual stakeholders while formulating plans remains a challenging task. A participatory decision-making process in urban development, where stakeholders' feelings are reflected in the planning, is necessary to create more adaptive and sustainable urban plans.

[0768] 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.

[0769] In this invention, the server includes a data collection means for collecting information from multiple data sources related to the city, a data preprocessing means for preprocessing the collected information, and an emotion analysis means for recognizing and evaluating emotional states. This makes it possible to effectively reflect the emotions of residents in urban development plans and to formulate flexible and adaptive urban plans.

[0770] "Information gathering means" refers to a device or method for collecting necessary information from multiple sources related to a city.

[0771] "Preprocessing means" refers to an apparatus or method of processing that is performed to convert collected information into an analyzable format.

[0772] "Analysis means" refers to a device or method for identifying urban problems and trends using pre-processed information.

[0773] A "simulation means" is a device or method for virtually executing different scenarios in urban development and evaluating the predicted impacts.

[0774] An "optimization means" is a device or method that proposes the optimal resource allocation or arrangement based on simulation results.

[0775] "Means of participation" refers to devices or methods for individual members to provide opinions and feedback on urban development plans.

[0776] "Emotional analysis means" refers to a device or method for recognizing and evaluating the emotional state contained in the feedback provided by a member.

[0777] "Evaluation tools" refer to devices or methods for evaluating or modifying urban planning based on the results of sentiment analysis.

[0778] The system implementing this invention combines various technical means to effectively reflect the sentiments of its members in the development of a smart city. A server collects information such as traffic volume, demographics, and environmental data from diverse sources related to the city, using a data collection device. This information is then formatted into an analyzable form by a preprocessor. This process includes data cleaning and integrity checks.

[0779] The pre-processed information is analyzed by an analysis device on the terminal to identify demographic trends and traffic patterns. A learning-based model is used to classify the information. The analysis results are sent to a server, where different development scenarios are tested in a simulation device. The impact of the different scenarios is predicted, and the results are evaluated by an optimization device to propose the optimal resource allocation.

[0780] Individual members can provide feedback on the urban development plan using a participation device. In this process, an emotion analysis device recognizes the emotional state contained in the comments using natural language processing technology. These results are then used by an evaluation device to assess and revise the urban plan. As important feedback, if the emotion contained in a comment is evaluated as "full of anticipation," that item is recognized as a driving force. On the other hand, if negative emotions are recognized, revisions to the development scenario should be considered.

[0781] For example, if a user expresses their expectations regarding a proposed new public transport route, the sentiment analyzer will correctly recognize that emotion and use a prompt message in the form of, "Please recognize the emotion of this feedback and assist in how to reflect it in the urban planning system."

[0782] The hardware and software used include smartphones, servers, Python, and natural language processing libraries. Specifically, libraries such as NLTK and custom models for sentiment recognition are utilized. This enables flexible and sustainable development that realistically and effectively reflects the emotions of residents in urban planning.

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The server collects information such as traffic volume, demographics, and environmental data from multiple sources related to the city. The collected information is input into the server, where data cleaning and integrity checks are performed before it is stored in the database, and clean data is output.

[0786] Step 2:

[0787] The terminal preprocesses the clean data received from the server. Here, it performs format conversion and removes meaningless data to convert the data into a parseable format, and outputs the preprocessed data.

[0788] Step 3:

[0789] The terminal uses pre-processed data to apply machine learning models and classify demographics and traffic patterns. The input data is processed by an analysis device, and classification results regarding demographics and traffic patterns are output.

[0790] Step 4:

[0791] The server uses the classification results to run simulations of different urban development scenarios. The simulation device receives these simulations, tests the predicted effects of the different scenarios in a virtual environment, and outputs the simulation results.

[0792] Step 5:

[0793] The server uses the simulation results and employs evolutionary algorithms to determine the optimal resource allocation. The optimization device performs optimization calculations according to the scenario allocation and outputs the optimal resource allocation plan.

[0794] Step 6:

[0795] Users input feedback into urban development plans using a participation device. Comments provided by users are input, and an emotion analysis device analyzes those comments and outputs the emotional state.

[0796] Step 7:

[0797] The server receives the output from the emotion analysis device and appropriately evaluates and modifies the urban plan. Based on the outputted emotional state, the evaluation device modifies the plan and outputs a newly adjusted urban plan proposal.

[0798] 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.

[0799] 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.

[0800] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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."

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] The following is further disclosed regarding the embodiments described above.

[0820] (Claim 1)

[0821] A means of collecting data from multiple data sources related to cities,

[0822] A preprocessing means for preprocessing the collected data,

[0823] An analytical method for analyzing pre-processed data to identify urban challenges and trends,

[0824] A simulation method for simulating different scenarios in urban development and predicting their impact,

[0825] An optimization method that proposes the optimal resource allocation based on simulation results,

[0826] Means of participation that allow citizens to provide feedback on urban development plans,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, wherein the data analysis means clusters population dynamics and traffic patterns using a machine learning model.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the optimization means generates an optimal arrangement model of urban infrastructure using a genetic algorithm.

[0832] "Example 1"

[0833] (Claim 1)

[0834] A means of obtaining information from multiple sources related to the city,

[0835] A cleansing means for preprocessing the acquired information,

[0836] An analytical means for analyzing pre-processed information to identify urban challenges and trends,

[0837] A prediction method that simulates different design proposals in urban development and provides prediction results,

[0838] A resource allocation optimization method that proposes the optimal resource allocation based on simulation results,

[0839] A feedback mechanism that provides a participatory platform where residents can post their opinions on urban development plans,

[0840] A means of updating the plan to take into account the opinions obtained from information sources and feedback,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the analysis means classifies population dynamics and traffic patterns using a generative model.

[0844] (Claim 3)

[0845] The system according to claim 1, wherein the allocation optimization means uses an evolutionary algorithm to create an optimal arrangement plan for urban functions.

[0846] "Application Example 1"

[0847] (Claim 1)

[0848] Means of collecting information from multiple data sources related to cities,

[0849] Means for preprocessing the collected information,

[0850] A means of analyzing pre-processed information to identify urban challenges and trends,

[0851] A simulation method for predicting different scenarios in urban planning and evaluating their impact,

[0852] A means of proposing the optimal allocation of resources based on simulation results,

[0853] Means by which citizens can provide opinions on urban design plans,

[0854] A means to visualize the progress of urban design and provide information in real time,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the information analysis means classifies collective dynamics and transportation patterns using a machine learning model.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the optimization means generates an optimal configuration model of the urban structure using an evolutionary algorithm.

[0860] "Example 2 of combining an emotion engine"

[0861] (Claim 1)

[0862] A means of collecting information from multiple sources related to the city,

[0863] Means for preprocessing the collected information,

[0864] An analytical means for analyzing pre-processed information to identify urban trends,

[0865] A simulation method for simulating different plans in urban development and predicting their impact,

[0866] An optimization method that proposes the optimal arrangement based on simulation results,

[0867] A means of participation that allows participants to offer their opinions on urban development plans,

[0868] An analytical method for analyzing emotions based on participants' opinions,

[0869] Means for reflecting analyzed emotions in urban planning,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein the information analysis means classifies population movements and traffic patterns using a learning algorithm.

[0873] (Claim 3)

[0874] The system according to claim 1, wherein the optimization means generates the optimal arrangement of urban facilities using an evolutionary algorithm.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A means of collecting information from multiple data sources related to cities,

[0878] A preprocessing means for preprocessing the collected information,

[0879] An analytical means for analyzing pre-processed information to identify urban problems and trends,

[0880] A simulation method for simulating different scenarios in urban development and predicting their impact,

[0881] An optimization method that proposes the optimal resource allocation based on simulation results,

[0882] Means of participation that allow individual members to provide feedback on urban development plans,

[0883] A means of emotion analysis that recognizes the emotional state contained in the feedback,

[0884] An evaluation method that applies the results of sentiment analysis to the evaluation and modification of urban planning,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, wherein the data analysis means classifies population dynamics and traffic patterns using a learning-based model.

[0888] (Claim 3)

[0889] The system according to claim 1, wherein the optimization means generates an optimal layout model of urban infrastructure using an evolutionary algorithm. [Explanation of symbols]

[0890] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A collection method for gathering data from multiple data sources related to cities, A preprocessing means for preprocessing the collected data, An analytical method for analyzing pre-processed data to identify urban challenges and trends, A simulation method for simulating different scenarios in urban development and predicting their impact, An optimization method that proposes the optimal resource allocation based on simulation results, Means of participation that allow citizens to provide feedback on urban development plans, A system that includes this.

2. The system according to claim 1, wherein the data analysis means clusters population dynamics and traffic patterns using a machine learning model.

3. The system according to claim 1, wherein the optimization means generates an optimal arrangement model of urban infrastructure using a genetic algorithm.

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