system

Abstract

The system according to this embodiment aims to efficiently generate and compare urban development scenarios and propose the optimal solution. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a scenario generation unit, a comparison unit, and an optimization unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The scenario generation unit generates urban development scenarios based on the data analyzed by the analysis unit. The comparison unit compares the scenarios generated by the scenario generation unit. The optimization unit proposes the optimal solution based on the results obtained by the comparison unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 the conventional technology, the generation and comparison of urban development scenarios have not been performed efficiently enough, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently generate and compare urban development scenarios and propose an optimal solution.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a scenario generation unit, a comparison unit, and an optimization unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The scenario generation unit generates urban development scenarios based on the data analyzed by the analysis unit. The comparison unit compares the scenarios generated by the scenario generation unit. The optimization unit proposes the optimal solution based on the results obtained by the comparison unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate and compare urban development scenarios and propose the optimal solution. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides an advanced simulator for innovating urban development using AI. This simulator creates a digital twin of the urban environment in real time, enabling planners, policymakers, and citizens to visualize, test, and optimize urban development scenarios. The simulator integrates real-time data from IoT sensors, satellite imagery, and urban databases, simultaneously generating and comparing multiple urban development scenarios, predicting the long-term impact of planning decisions, and includes a portal where citizens can view proposed changes and provide feedback. AI-driven optimization proposes optimal solutions to complex urban challenges. For example, the urban development simulator targets urban planning departments, urban development agencies, private urban planners, and citizens aged 20 to 70 interested in regional development, environmental issues, and local politics in Japanese cities with populations ranging from 100,000 to 10 million. This simulator addresses issues such as delays in the urban planning process, inaccuracies in conventional simulations, limitations in citizen participation, and unique challenges faced by Japanese cities (aging infrastructure, population decline in specific areas, and resilience to natural disasters). This simulator can reduce planning time by up to 70%, improve the accuracy of impact predictions, promote citizen participation, optimize resource allocation, and be customizable to address the unique challenges of Japanese cities. The simulator leverages 5G networks and IoT infrastructure, geospatial data and user interface expertise, and a secure cloud infrastructure. It will initially target 10 major Japanese cities, expanding to 50 cities across Japan within three years, and to 200 cities worldwide by 2028. By 2025, it is projected to capture a 40% share of the Japanese urban planning software market, with annual recurring revenue of 500 million yen per major city. The global urban planning software market is projected to reach $5.3 billion by 2025. The simulator uses generative AI for scenario generation, pattern recognition, natural language processing, predictive modeling, and optimization algorithms. The market size is projected to reach $5.3 billion by 2025, with the initial serviceable market in Japan estimated at 50 billion yen per year.Factors such as rapid urbanization, technological maturity, aging infrastructure, adaptation to climate change, post-pandemic urban redesign, and government smart city initiatives make now the optimal time. The goal is to accelerate sustainable development, increase citizen participation, optimize resource allocation, create disaster-resilient cities, promote data-driven governance, and achieve global impact. As a result, urban development simulators can significantly improve the efficiency and accuracy of urban development.

[0029] The urban development simulator according to this embodiment comprises a data collection unit, an analysis unit, a scenario generation unit, a comparison unit, and an optimization unit. The data collection unit collects data. For example, the data collection unit can collect urban environmental data using IoT sensors. The data collection unit can also acquire satellite images and collect urban geographic information. Furthermore, the data collection unit can collect population data and traffic data from urban databases. For example, the data collection unit collects environmental data using temperature and humidity sensors. Satellite images provide high-resolution images to understand the urban geographic information in detail. Urban databases provide demographic and traffic flow data to understand urban dynamics. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can grasp data trends using statistical analysis. Furthermore, the analysis unit can recognize data patterns using machine learning algorithms. Furthermore, the analysis unit can perform simulations to predict the impact of urban development. For example, the analysis unit analyzes data trends using regression analysis. Machine learning algorithms learn from large amounts of data and recognize patterns. The simulation predicts the impact of urban development and improves the accuracy of the plan. The scenario generation unit generates urban development scenarios based on data analyzed by the analysis unit. The scenario generation unit can generate multiple scenarios, for example, using a generation AI. It can also adjust the level of detail of the scenarios based on specific urban challenges. Furthermore, the scenario generation unit can apply different generation algorithms depending on the urban category. For example, the generation AI can generate traffic scenarios and environmental scenarios. The scenario generation unit adjusts the level of detail of the scenarios based on specific urban challenges. Depending on the urban category, it applies different generation algorithms, such as residential or commercial areas. The comparison unit compares the scenarios generated by the scenario generation unit. For example, the comparison unit can perform cost comparisons and effectiveness comparisons. It can also improve the accuracy of the comparison by considering the interrelationships between scenarios. Furthermore, the comparison unit can perform comparisons considering the attribute information of the scenario submitters. For example, the comparison unit compares scenarios based on cost efficiency and environmental impact.The system prioritizes comparing scenarios with the greatest impact, taking into account their interrelationships. It evaluates the reliability of scenarios, considering the expertise and experience of the scenario submitters. The optimization unit proposes the optimal solution based on the results obtained by the comparison unit. The optimization unit can, for example, propose a solution using an optimization algorithm. Furthermore, the optimization unit can improve the accuracy of optimization by considering the interrelationships of scenarios. In addition, the optimization unit can perform optimization while considering the attribute information of the scenario submitters. For example, the optimization unit proposes the optimal solution based on cost efficiency and environmental impact. The system prioritizes optimizing scenarios with the greatest impact, taking into account their interrelationships. It evaluates the reliability of optimization by considering the expertise and experience of the scenario submitters. As a result, the urban development simulator according to this embodiment can efficiently generate, compare, and optimize urban development scenarios.

[0030] The data collection unit collects data. For example, the data collection unit can collect urban environmental data using IoT sensors. Specifically, temperature sensors, humidity sensors, air quality sensors, etc., are installed in various locations throughout the city, and data is collected from these sensors in real time. Temperature sensors provide detailed information on temperature fluctuations within the city, and humidity sensors monitor humidity fluctuations. Air quality sensors measure the concentrations of air pollutants such as PM2.5 and CO2, providing data for evaluating the environmental conditions of the city. The data collection unit can also acquire satellite imagery and collect geographical information about the city. Satellite imagery provides high-resolution images, allowing for detailed understanding of the city's topography, building layout, and distribution of green spaces. This makes it possible to monitor the city's geographical features and changes in real time. Furthermore, the data collection unit can also collect population data and traffic data from a city database. The city database contains demographic information on residents and traffic flow data, and this data can be used to understand the dynamics of the city. For example, the data collection unit can monitor traffic congestion conditions during specific time periods and fluctuations in population density in specific areas in real time. This allows the data collection unit to gather a wide range of data from diverse data sources, enabling it to gain a detailed understanding of the current state of the city.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use statistical analysis to understand data trends. Specifically, it statistically analyzes collected temperature and humidity data to understand seasonal climate change and the frequency of extreme weather events. The analysis unit can also recognize data patterns using machine learning algorithms. For example, it can use traffic data to learn traffic congestion patterns at specific times of day or on specific days of the week to predict future traffic conditions. Furthermore, the analysis unit can perform simulations to predict the impact of urban development. For example, when constructing a new commercial facility in a specific area of ​​a city, it can simulate the impact to predict increased traffic volume and environmental effects. The analysis unit uses regression analysis to analyze data trends and predict future fluctuations. Machine learning algorithms learn from large amounts of data and recognize patterns to make more accurate predictions. Simulation is an important tool for predicting the impact of urban development and improving the accuracy of planning. This allows the analysis unit to quickly and accurately analyze collected data and understand the current state and future fluctuations of the city.

[0032] The scenario generation unit generates urban development scenarios based on data analyzed by the analysis unit. The scenario generation unit can generate multiple scenarios, for example, using a generation AI. The generation AI can adjust the level of detail in the scenarios based on specific urban challenges. For example, it can generate scenarios tailored to different purposes, such as scenarios aimed at mitigating traffic congestion or scenarios emphasizing environmental protection. Furthermore, the scenario generation unit can apply different generation algorithms depending on the urban category. For example, it can select an algorithm to generate the optimal scenario for different categories such as residential, commercial, and industrial areas. When generating traffic and environmental scenarios, the generation AI utilizes historical data and statistical information to generate realistic and actionable scenarios. The scenario generation unit can adjust the level of detail in the scenarios based on specific urban challenges and modify the scenario content as needed. This allows the scenario generation unit to generate diverse scenarios that address the current state and future challenges of the city, supporting urban development planning.

[0033] The comparison unit compares the scenarios generated by the scenario generation unit. For example, the comparison unit can perform cost comparisons and effectiveness comparisons. Specifically, it compares development and operational costs in each scenario to identify the most cost-effective scenario. It can also evaluate the environmental and social impacts of each scenario and select the most effective one. The comparison unit can also improve the accuracy of comparisons by considering the interrelationships between scenarios. For example, it evaluates the impact one scenario has on others and prioritizes selecting scenarios that complement each other. Furthermore, the comparison unit can perform comparisons by considering the attribute information of the scenario submitters. For example, it evaluates the expertise and experience of the scenario submitters and selects highly reliable scenarios. The comparison unit compares scenarios based on cost efficiency and environmental impact to select the most effective urban development plan. Considering the interrelationships between scenarios, it prioritizes comparing scenarios with the greatest impact and selects the optimal plan. It also evaluates the reliability of scenarios by considering the expertise and experience of the scenario submitters and selects the most reliable scenario. This allows the comparison unit to efficiently and accurately compare urban development scenarios and select the optimal plan.

[0034] The optimization unit proposes the optimal solution based on the results obtained by the comparison unit. For example, the optimization unit can propose a solution using an optimization algorithm. Specifically, it uses optimization methods such as genetic algorithms and linear programming to derive the optimal solution for urban development planning. Furthermore, the optimization unit can improve the accuracy of optimization by considering the interrelationships of scenarios. For example, if multiple scenarios influence each other, it evaluates these influences and proposes the most effective solution overall. In addition, the optimization unit can perform optimization while considering the attribute information of the scenario submitters. For example, it evaluates the expertise and experience of the scenario submitters and proposes a highly reliable solution. The optimization unit proposes the optimal solution based on cost efficiency and environmental impact, maximizing the effectiveness of urban development planning. Considering the interrelationships of scenarios, it prioritizes the optimization of scenarios with the greatest impact and proposes the most effective plan overall. It evaluates the reliability of the optimization by considering the expertise and experience of the scenario submitters and proposes the most reliable solution. In this way, the optimization unit can efficiently and accurately propose the optimal solution for urban development planning, supporting the sustainable development of cities.

[0035] The data collection unit can collect data from IoT sensors, satellite imagery, and urban databases. For example, the data collection unit can collect urban environmental data using IoT sensors. For example, it can collect environmental data using temperature sensors and humidity sensors. The data collection unit can also acquire satellite imagery and collect urban geographic information. For example, satellite imagery provides high-resolution images, allowing for a detailed understanding of urban geography. Furthermore, the data collection unit can collect population data and traffic data from urban databases. For example, urban databases provide demographic and traffic flow data, allowing for an understanding of urban dynamics. This enables data collection from diverse data sources. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from IoT sensors into AI and have the AI ​​perform data collection and analysis.

[0036] The analysis unit can analyze the collected data and predict the impact of urban development. The analysis unit can, for example, use statistical analysis to understand data trends. For example, it can use regression analysis to analyze data trends. The analysis unit can also recognize data patterns using machine learning algorithms. For example, machine learning algorithms can learn from large amounts of data and recognize patterns. Furthermore, the analysis unit can run simulations to predict the impact of urban development. For example, simulations can predict the impact of urban development and improve the accuracy of plans. This improves the accuracy of plans by predicting the impact of urban development. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform data analysis and prediction.

[0037] The scenario generation unit can generate multiple urban development scenarios. The scenario generation unit generates multiple scenarios, for example, using a generation AI. For example, the generation AI can generate traffic scenarios and environmental scenarios. Furthermore, the scenario generation unit can adjust the level of detail of the scenarios based on specific urban challenges. For example, the scenario generation unit can generate detailed scenarios for environmental problems. In addition, the scenario generation unit can apply different generation algorithms depending on the urban category. For example, the scenario generation unit can apply different generation algorithms for residential areas and commercial areas. This allows for the exploration of various urban development possibilities by generating multiple scenarios. Some or all of the above-described processes in the scenario generation unit may be performed using a generation AI, or without one. For example, the scenario generation unit can have the generation AI generate scenarios based on specific urban challenges.

[0038] The comparison unit can compare multiple generated scenarios. For example, it can perform cost comparisons or effectiveness comparisons. For instance, it can compare scenarios based on cost efficiency or environmental impact. Furthermore, the comparison unit can improve the accuracy of the comparison by considering the interrelationships between scenarios. For example, it can analyze the interrelationships between scenarios to perform a highly accurate comparison. Additionally, the comparison unit can perform comparisons considering the attribute information of the scenario submitters. For example, it can evaluate the reliability of a scenario by considering the submitter's expertise and experience. This allows for the selection of the optimal scenario by comparing multiple scenarios. Some or all of the above processing in the comparison unit may be performed using AI, or not. For example, the comparison unit can input the generated scenarios into an AI and have the AI ​​perform the scenario comparison.

[0039] The optimization unit can propose the optimal solution based on the comparison results. For example, the optimization unit can propose a solution using an optimization algorithm. For example, the optimization unit can propose the optimal solution based on cost efficiency and environmental impact. The optimization unit can also improve the accuracy of optimization by considering the interrelationships between scenarios. For example, the optimization unit can analyze the interrelationships between scenarios and perform highly accurate optimization. Furthermore, the optimization unit can perform optimization while considering the attribute information of the scenario submitters. For example, the optimization unit can evaluate the reliability of the optimization by considering the submitter's expertise and experience. This improves the efficiency of urban development by proposing the optimal solution. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the comparison results into AI and have the AI ​​execute the proposal of the optimal solution.

[0040] The Citizen Feedback Department can provide a portal for citizens to view proposed changes and provide feedback. For example, citizens can view proposed changes and provide opinions through an online portal. For example, the Citizen Feedback Department can collect citizens' opinions through surveys. Furthermore, the Citizen Feedback Department can collect citizen feedback in real time and reflect it in urban development scenarios. For example, citizens can view proposed changes and provide opinions through an online portal. Surveys are a means of collecting citizens' opinions and reflecting them in urban development scenarios. Real-time feedback collection allows for the rapid reflection of citizens' opinions. This enables citizens to view proposed changes and provide feedback. Some or all of the above processes in the Citizen Feedback Department may be performed using AI, or not. For example, the Citizen Feedback Department can input citizens' opinions collected through the online portal into AI and have the AI ​​analyze the feedback.

[0041] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. Furthermore, the data collection unit can identify areas for improvement in the collection method based on past data collection history and optimize it. In addition, the data collection unit can analyze past data collection history, find patterns in collection methods, and select the optimal method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. It can identify areas for improvement in the collection method and optimize it. It can find patterns in collection methods and select the optimal method. Thus, by analyzing past data collection history, the optimal collection method can be selected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0042] The data collection unit can filter data based on specific areas or time zones within a city. For example, the unit can prioritize data collection in specific areas of a city to collect important data. It can also collect data during specific time zones to understand fluctuations between time zones. Furthermore, the unit can combine city areas and time zones for efficient data collection. For instance, the unit prioritizes data collection in specific areas of a city to collect important data. It collects data during specific time zones to understand fluctuations between time zones. It filters data by combining city areas and time zones for efficient data collection. This allows for efficient data collection by filtering based on specific areas and time zones within a city. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can have AI perform filtering based on specific areas and time zones within a city.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering urban event information during data collection. For example, the data collection unit can prioritize the collection of relevant data based on information about events held in the city. The data collection unit can also determine the priority of data collection by considering the scale and impact of events. Furthermore, the data collection unit can plan data collection based on the location and time of events. For example, the data collection unit prioritizes the collection of relevant data based on information about events held in the city. It determines the priority of data collection by considering the scale and impact of events. It plans data collection based on the location and time of events. This allows for the priority collection of highly relevant data by considering urban event information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input urban event information into AI and have the AI ​​perform the collection of highly relevant data.

[0044] The data collection unit can analyze social media trends and collect relevant data during data collection. For example, the data collection unit can analyze social media trends in real time and collect relevant data. The data collection unit can also monitor trend fluctuations and adjust the timing of data collection. Furthermore, the data collection unit can determine the type and scope of data to collect based on trends. For example, the data collection unit analyzes social media trends in real time and collects relevant data. It monitors trend fluctuations and adjusts the timing of data collection. It determines the type and scope of data to collect based on trends. This allows for the efficient collection of relevant data by analyzing social media trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media trend data into AI and have the AI ​​perform the collection of relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can allocate analysis resources according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance, a simplified analysis on data with low importance, and allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an environmental analysis algorithm to environmental data. It can also apply a traffic analysis algorithm to traffic data. Furthermore, it can apply an economic analysis algorithm to economic data. For example, the analysis unit can apply an environmental analysis algorithm to environmental data, a traffic analysis algorithm to traffic data, and an economic analysis algorithm to economic data. By applying different analysis algorithms depending on the data category, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​apply the appropriate analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. It can also analyze long-term trends based on historical data. Furthermore, the analysis unit can allocate analysis resources according to the data collection timing. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information, analyze long-term trends based on historical data, and allocate analysis resources according to the data collection timing. This allows for the provision of real-time information by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​determine the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide efficient results. Alternatively, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can allocate analysis resources according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to provide efficient results, postpone the analysis of less relevant data and prioritize the analysis of important data, and allocate analysis resources according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0049] The scenario generation unit can adjust the level of detail of a scenario based on specific urban issues during scenario generation. For example, the scenario generation unit can generate detailed scenarios for environmental problems. It can also generate detailed scenarios for traffic problems. Furthermore, it can generate detailed scenarios for economic problems. For example, the scenario generation unit generates detailed scenarios for environmental problems. It generates detailed scenarios for traffic problems. It generates detailed scenarios for economic problems. By adjusting the level of detail of a scenario based on specific urban issues, it is possible to generate highly accurate scenarios. Some or all of the above processing in the scenario generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the scenario generation unit can input specific urban issues into an AI and have the AI ​​perform the adjustment of the level of detail of the scenarios.

[0050] The scenario generation unit can apply different generation algorithms depending on the city category when generating scenarios. For example, the scenario generation unit can apply an environmental scenario generation algorithm to the environmental category. It can also apply a transportation scenario generation algorithm to the transportation category. Furthermore, it can apply an economic scenario generation algorithm to the economic category. For example, the scenario generation unit can apply an environmental scenario generation algorithm to the environmental category, a transportation scenario generation algorithm to the transportation category, and an economic scenario generation algorithm to the economic category. By applying different generation algorithms depending on the city category, appropriate scenarios can be generated. Some or all of the above processing in the scenario generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the scenario generation unit can input the city category into the AI ​​and have the AI ​​execute the application of an appropriate generation algorithm.

[0051] The scenario generation unit can determine the priority of scenarios based on the city's historical data during scenario generation. For example, the scenario generation unit can prioritize generating scenarios of high importance based on historical data. It can also prioritize generating scenarios with a large impact based on historical data. Furthermore, it can prioritize generating scenarios with high urgency based on historical data. For example, the scenario generation unit prioritizes generating scenarios of high importance based on historical data. It prioritizes generating scenarios with a large impact. It prioritizes generating scenarios with high urgency. In this way, by determining the priority of scenarios based on the city's historical data, important scenarios can be generated preferentially. Some or all of the above processing in the scenario generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the scenario generation unit can input the city's historical data into an AI and have the AI ​​perform the determination of scenario priorities.

[0052] The scenario generation unit can adjust the order of scenarios based on relevant city data during scenario generation. For example, the scenario generation unit can prioritize generating scenarios of high importance based on relevant data. It can also prioritize generating scenarios with a large impact based on relevant data. Furthermore, it can prioritize generating scenarios with high urgency based on relevant data. For example, the scenario generation unit prioritizes generating scenarios of high importance based on relevant data. It prioritizes generating scenarios with a large impact. It prioritizes generating scenarios with high urgency. This allows for efficient scenario generation by adjusting the order of scenarios based on relevant city data. Some or all of the above processing in the scenario generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the scenario generation unit can input relevant city data into an AI and have the AI ​​perform the adjustment of the scenario order.

[0053] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between scenarios. For example, the comparison unit can analyze the interrelationships between scenarios and perform a highly accurate comparison. The comparison unit can also prioritize the comparison of scenarios with a greater impact by considering the interrelationships between scenarios. Furthermore, the comparison unit can adjust the comparison criteria based on the interrelationships between scenarios. For example, the comparison unit can analyze the interrelationships between scenarios and perform a highly accurate comparison. It can prioritize the comparison of scenarios with a greater impact by considering the interrelationships between scenarios. It can adjust the comparison criteria based on the interrelationships between scenarios. This makes it possible to perform a highly accurate comparison by considering the interrelationships between scenarios. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input the interrelationships between scenarios into AI and have the AI ​​perform the improvement of the comparison accuracy.

[0054] The comparison unit can perform comparisons while considering the attribute information of the scenario submitters. For example, the comparison unit can compare scenarios while considering the submitters' expertise and experience. The comparison unit can also evaluate the reliability of scenarios based on the submitters' attribute information. Furthermore, the comparison unit can determine the priority of scenarios while considering the submitters' attribute information. For example, the comparison unit compares scenarios while considering the submitters' expertise and experience. It evaluates the reliability of scenarios based on the submitters' attribute information. It determines the priority of scenarios while considering the submitters' attribute information. This makes it possible to perform highly reliable comparisons by considering the attribute information of the scenario submitters. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input the submitters' attribute information into AI and have the AI ​​perform the comparison.

[0055] The comparison unit can perform comparisons while considering the geographical distribution of the scenarios. For example, the comparison unit can perform regional comparisons based on the geographical distribution of the scenarios. The comparison unit can also prioritize comparisons of regions with a greater impact, taking geographical distribution into consideration. Furthermore, the comparison unit can adjust the comparison criteria based on geographical distribution. For example, the comparison unit can perform regional comparisons based on the geographical distribution of the scenarios. It can prioritize comparisons of regions with a greater impact, taking geographical distribution into consideration. It can adjust the comparison criteria based on geographical distribution. This makes it possible to perform regional comparisons by considering the geographical distribution of the scenarios. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input the geographical distribution of the scenarios into AI and have the AI ​​perform the comparison.

[0056] The comparison unit can improve the accuracy of the comparison by referring to relevant literature for the scenarios during the comparison process. For example, the comparison unit can evaluate the reliability of the scenarios based on the relevant literature. The comparison unit can also adjust the comparison criteria for the scenarios by referring to the relevant literature. Furthermore, the comparison unit can predict the impact of the scenarios based on the relevant literature. For example, the comparison unit evaluates the reliability of the scenarios based on the relevant literature. It adjusts the comparison criteria for the scenarios by referring to the relevant literature. It predicts the impact of the scenarios based on the relevant literature. This makes it possible to perform a highly accurate comparison by referring to the relevant literature for the scenarios. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input the relevant literature for the scenarios into AI and have the AI ​​perform the comparison.

[0057] The optimization unit can improve the accuracy of optimization by considering the interrelationships between scenarios during optimization. For example, the optimization unit can analyze the interrelationships between scenarios and perform highly accurate optimization. The optimization unit can also prioritize the optimization of scenarios with a greater impact by considering the interrelationships between scenarios. Furthermore, the optimization unit can adjust the optimization criteria based on the interrelationships between scenarios. For example, the optimization unit can analyze the interrelationships between scenarios and perform highly accurate optimization. It can prioritize the optimization of scenarios with a greater impact by considering the interrelationships between scenarios. It can adjust the optimization criteria based on the interrelationships between scenarios. This makes highly accurate optimization possible by considering the interrelationships between scenarios. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the interrelationships between scenarios into AI and have AI perform the optimization.

[0058] The optimization unit can perform optimization while considering the attribute information of the scenario submitter. For example, the optimization unit can optimize the scenario while considering the submitter's expertise and experience. The optimization unit can also evaluate the reliability of the scenario based on the submitter's attribute information. Furthermore, the optimization unit can determine the priority of the scenario while considering the submitter's attribute information. For example, the optimization unit optimizes the scenario while considering the submitter's expertise and experience. It evaluates the reliability of the scenario based on the submitter's attribute information. It determines the priority of the scenario while considering the submitter's attribute information. This makes highly reliable optimization possible by considering the attribute information of the scenario submitter. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the submitter's attribute information into AI and have the AI ​​perform the optimization.

[0059] The optimization unit can perform optimization while considering the geographical distribution of the scenarios. For example, the optimization unit can perform regional optimization based on the geographical distribution of the scenarios. The optimization unit can also prioritize the optimization of areas with a greater impact, taking the geographical distribution into consideration. Furthermore, the optimization unit can adjust the optimization criteria based on the geographical distribution. For example, the optimization unit performs regional optimization based on the geographical distribution of the scenarios. It prioritizes the optimization of areas with a greater impact, taking the geographical distribution into consideration. It adjusts the optimization criteria based on the geographical distribution. This makes regional optimization possible by considering the geographical distribution of the scenarios. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the geographical distribution of the scenarios into AI and have the AI ​​perform the optimization.

[0060] The optimization unit can improve the accuracy of optimization by referring to relevant literature for the scenario during optimization. For example, the optimization unit can evaluate the reliability of the scenario based on the relevant literature. The optimization unit can also adjust the optimization criteria for the scenario by referring to the relevant literature. Furthermore, the optimization unit can predict the impact of the scenario based on the relevant literature. For example, the optimization unit evaluates the reliability of the scenario based on the relevant literature. It adjusts the optimization criteria for the scenario by referring to the relevant literature. It predicts the impact of the scenario based on the relevant literature. As a result, highly accurate optimization becomes possible by referring to relevant literature for the scenario. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input relevant literature for the scenario into AI and have the AI ​​perform the optimization.

[0061] The Citizen Feedback Department can select the optimal display method when displaying feedback by referring to the user's past feedback history. For example, the Citizen Feedback Department can select the optimal display method based on the user's past feedback history. The Citizen Feedback Department can also analyze past feedback history and identify areas for improvement in the display method. Furthermore, the Citizen Feedback Department can refer to past feedback history to find patterns in the display method and select the optimal method. For example, the Citizen Feedback Department selects the optimal display method based on the user's past feedback history. It analyzes past feedback history and identifies areas for improvement in the display method. It refers to past feedback history to find patterns in the display method and select the optimal method. In this way, the optimal display method can be selected by referring to the user's past feedback history. Some or all of the above processes in the Citizen Feedback Department may be performed using AI, for example, or without AI. For example, the Citizen Feedback Department can input the user's past feedback history into AI and have the AI ​​select the optimal display method.

[0062] The Citizen Feedback Unit can select the optimal display method when displaying feedback, taking into account the user's device information. For example, if the user is using a smartphone, the Citizen Feedback Unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the Citizen Feedback Unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the Citizen Feedback Unit can provide a concise and highly visible display method. This allows the Citizen Feedback Unit to select the optimal display method by considering the user's device information. Some or all of the above processing in the Citizen Feedback Unit may be performed using AI, or not. For example, the Citizen Feedback Unit can input the user's device information into AI and have AI select the optimal display method.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The urban development simulator may also include a prediction unit. Based on data obtained from the data collection unit, the prediction unit can forecast future changes in the urban environment. For example, the prediction unit can forecast the impact of climate change and assess its impact on urban infrastructure. It can also forecast demographic changes and assess future housing and transportation demand. Furthermore, it can forecast economic fluctuations and assess their impact on urban economic activity. This allows the prediction unit to provide information useful for urban development planning by forecasting future changes in the urban environment. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not. For example, the prediction unit can input collected data into an AI and have the AI ​​perform predictions of future changes.

[0065] The urban development simulator may further include an evaluation unit. The evaluation unit can evaluate the scenarios generated by the scenario generation unit and clarify the advantages and disadvantages of each scenario. For example, the evaluation unit can conduct an environmental impact assessment and evaluate the impact each scenario has on the environment. It can also conduct an economic impact assessment and evaluate the impact each scenario has on the economy. Furthermore, it can conduct a social impact assessment and evaluate the impact each scenario has on society. In this way, the evaluation unit can provide information useful for urban development planning by clarifying the advantages and disadvantages of each scenario. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the generated scenarios into an AI and have the AI ​​perform the evaluation of each scenario.

[0066] The urban development simulator may also include a notification unit. This notification unit can inform stakeholders of the progress of urban development and any significant changes. For example, it can notify stakeholders of the progress of urban development in real time. It can also send alerts to stakeholders when significant changes occur. Furthermore, it can notify stakeholders when the urban development plan is completed. This allows the notification unit to improve the efficiency of urban development by informing stakeholders of its progress and any significant changes. Some or all of the above processes in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input the progress of urban development into an AI and have the AI ​​send notifications.

[0067] The urban development simulator may further include a simulation unit. This simulation unit can simulate a virtual urban environment based on urban development scenarios. For example, the simulation unit can perform traffic simulations to evaluate the impact of urban development on traffic. It can also perform environmental simulations to evaluate the impact of urban development on the environment. Furthermore, it can perform economic simulations to evaluate the impact of urban development on the economy. In this way, the simulation unit can provide information useful for urban development planning by simulating a virtual urban environment. Some or all of the above-described processes in the simulation unit may be performed using, for example, AI, or not. For example, the simulation unit can input urban development scenarios into an AI and have the AI ​​perform the simulation.

[0068] The urban development simulator may also include a data visualization unit. This unit can visually display collected data and analysis results. For example, it can display urban geographical information on a map, allowing for a visual understanding of the city's current state. It can also display urban development scenarios in graphs and charts, facilitating comparisons between scenarios. Furthermore, it can display the impact of urban development using 3D models, making it easier to understand visually. Thus, by visually displaying collected data and analysis results, the data visualization unit can provide information useful for urban development planning. Some or all of the above-described processes in the data visualization unit may be performed using AI, for example, or without AI. For instance, the data visualization unit can input collected data into an AI and have the AI ​​perform the visual display.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects data. The data collection unit can collect urban environmental data using, for example, IoT sensors. It can also acquire satellite imagery and collect urban geographical information. Furthermore, the data collection unit can collect population and traffic data from urban databases. For example, the data collection unit collects environmental data using temperature and humidity sensors. Satellite imagery provides high-resolution images, allowing for a detailed understanding of the city's geography. Urban databases provide demographic and traffic flow data, enabling an understanding of urban dynamics. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use statistical analysis to understand data trends. It can also recognize data patterns using machine learning algorithms. Furthermore, the analysis unit can run simulations to predict the impact of urban development. For example, the analysis unit can analyze data trends using regression analysis. Machine learning algorithms learn from large amounts of data and recognize patterns. Simulations predict the impact of urban development and improve the accuracy of the plan. Step 3: The scenario generation unit generates urban development scenarios based on the data analyzed by the analysis unit. The scenario generation unit can generate multiple scenarios, for example, using a generation AI. The scenario generation unit can also adjust the level of detail of the scenarios based on specific urban challenges. Furthermore, the scenario generation unit can apply different generation algorithms depending on the urban category. For example, the generation AI generates traffic scenarios and environmental scenarios. The scenario generation unit adjusts the level of detail of the scenarios based on specific urban challenges. Depending on the urban category, different generation algorithms such as residential and commercial areas are applied. Step 4: The comparison unit compares the scenarios generated by the scenario generation unit. The comparison unit can perform cost comparisons, effect comparisons, etc. The comparison unit can also improve the accuracy of the comparison by considering the interrelationships of the scenarios. Furthermore, the comparison unit can perform comparisons by considering the attribute information of the scenario submitters. For example, the comparison unit compares scenarios based on cost efficiency and environmental impact. It prioritizes the comparison of scenarios with a greater impact by considering the interrelationships of the scenarios. It evaluates the reliability of the scenarios by considering the expertise and experience of the scenario submitters. Step 5: The optimization unit proposes the optimal solution based on the results obtained by the comparison unit. The optimization unit can, for example, propose a solution using an optimization algorithm. The optimization unit can also improve the accuracy of the optimization by considering the interrelationships of the scenarios. Furthermore, the optimization unit can perform optimization by considering the attribute information of the scenario submitters. For example, the optimization unit proposes the optimal solution based on cost efficiency and environmental impact. It prioritizes the optimization of scenarios with a large impact by considering the interrelationships of the scenarios. It evaluates the reliability of the optimization by considering the expertise and experience of the scenario submitters.

[0071] (Example of form 2) An embodiment of the present invention provides an advanced simulator for innovating urban development using AI. This simulator creates a digital twin of the urban environment in real time, enabling planners, policymakers, and citizens to visualize, test, and optimize urban development scenarios. The simulator integrates real-time data from IoT sensors, satellite imagery, and urban databases, simultaneously generating and comparing multiple urban development scenarios, predicting the long-term impact of planning decisions, and includes a portal where citizens can view proposed changes and provide feedback. AI-driven optimization proposes optimal solutions to complex urban challenges. For example, the urban development simulator targets urban planning departments, urban development agencies, private urban planners, and citizens aged 20 to 70 interested in regional development, environmental issues, and local politics in Japanese cities with populations ranging from 100,000 to 10 million. This simulator addresses issues such as delays in the urban planning process, inaccuracies in conventional simulations, limitations in citizen participation, and unique challenges faced by Japanese cities (aging infrastructure, population decline in specific areas, and resilience to natural disasters). This simulator can reduce planning time by up to 70%, improve the accuracy of impact predictions, promote citizen participation, optimize resource allocation, and be customizable to address the unique challenges of Japanese cities. The simulator leverages 5G networks and IoT infrastructure, geospatial data and user interface expertise, and a secure cloud infrastructure. It will initially target 10 major Japanese cities, expanding to 50 cities across Japan within three years, and to 200 cities worldwide by 2028. By 2025, it is projected to capture a 40% share of the Japanese urban planning software market, with annual recurring revenue of 500 million yen per major city. The global urban planning software market is projected to reach $5.3 billion by 2025. The simulator uses generative AI for scenario generation, pattern recognition, natural language processing, predictive modeling, and optimization algorithms. The market size is projected to reach $5.3 billion by 2025, with the initial serviceable market in Japan estimated at 50 billion yen per year.Factors such as rapid urbanization, technological maturity, aging infrastructure, adaptation to climate change, post-pandemic urban redesign, and government smart city initiatives make now the optimal time. The goal is to accelerate sustainable development, increase citizen participation, optimize resource allocation, create disaster-resilient cities, promote data-driven governance, and achieve global impact. As a result, urban development simulators can significantly improve the efficiency and accuracy of urban development.

[0072] The urban development simulator according to this embodiment comprises a data collection unit, an analysis unit, a scenario generation unit, a comparison unit, and an optimization unit. The data collection unit collects data. For example, the data collection unit can collect urban environmental data using IoT sensors. The data collection unit can also acquire satellite images and collect urban geographic information. Furthermore, the data collection unit can collect population data and traffic data from urban databases. For example, the data collection unit collects environmental data using temperature and humidity sensors. Satellite images provide high-resolution images to understand the urban geographic information in detail. Urban databases provide demographic and traffic flow data to understand urban dynamics. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can grasp data trends using statistical analysis. Furthermore, the analysis unit can recognize data patterns using machine learning algorithms. Furthermore, the analysis unit can perform simulations to predict the impact of urban development. For example, the analysis unit analyzes data trends using regression analysis. Machine learning algorithms learn from large amounts of data and recognize patterns. The simulation predicts the impact of urban development and improves the accuracy of the plan. The scenario generation unit generates urban development scenarios based on data analyzed by the analysis unit. The scenario generation unit can generate multiple scenarios, for example, using a generation AI. It can also adjust the level of detail of the scenarios based on specific urban challenges. Furthermore, the scenario generation unit can apply different generation algorithms depending on the urban category. For example, the generation AI can generate traffic scenarios and environmental scenarios. The scenario generation unit adjusts the level of detail of the scenarios based on specific urban challenges. Depending on the urban category, it applies different generation algorithms, such as residential or commercial areas. The comparison unit compares the scenarios generated by the scenario generation unit. For example, the comparison unit can perform cost comparisons and effectiveness comparisons. It can also improve the accuracy of the comparison by considering the interrelationships between scenarios. Furthermore, the comparison unit can perform comparisons considering the attribute information of the scenario submitters. For example, the comparison unit compares scenarios based on cost efficiency and environmental impact.The system prioritizes comparing scenarios with the greatest impact, taking into account their interrelationships. It evaluates the reliability of scenarios, considering the expertise and experience of the scenario submitters. The optimization unit proposes the optimal solution based on the results obtained by the comparison unit. The optimization unit can, for example, propose a solution using an optimization algorithm. Furthermore, the optimization unit can improve the accuracy of optimization by considering the interrelationships of scenarios. In addition, the optimization unit can perform optimization while considering the attribute information of the scenario submitters. For example, the optimization unit proposes the optimal solution based on cost efficiency and environmental impact. The system prioritizes optimizing scenarios with the greatest impact, taking into account their interrelationships. It evaluates the reliability of optimization by considering the expertise and experience of the scenario submitters. As a result, the urban development simulator according to this embodiment can efficiently generate, compare, and optimize urban development scenarios.

[0073] The data collection unit collects data. For example, the data collection unit can collect urban environmental data using IoT sensors. Specifically, temperature sensors, humidity sensors, air quality sensors, etc., are installed in various locations throughout the city, and data is collected from these sensors in real time. Temperature sensors provide detailed information on temperature fluctuations within the city, and humidity sensors monitor humidity fluctuations. Air quality sensors measure the concentrations of air pollutants such as PM2.5 and CO2, providing data for evaluating the environmental conditions of the city. The data collection unit can also acquire satellite imagery and collect geographical information about the city. Satellite imagery provides high-resolution images, allowing for detailed understanding of the city's topography, building layout, and distribution of green spaces. This makes it possible to monitor the city's geographical features and changes in real time. Furthermore, the data collection unit can also collect population data and traffic data from a city database. The city database contains demographic information on residents and traffic flow data, and this data can be used to understand the dynamics of the city. For example, the data collection unit can monitor traffic congestion conditions during specific time periods and fluctuations in population density in specific areas in real time. This allows the data collection unit to gather a wide range of data from diverse data sources, enabling it to gain a detailed understanding of the current state of the city.

[0074] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use statistical analysis to understand data trends. Specifically, it statistically analyzes collected temperature and humidity data to understand seasonal climate change and the frequency of extreme weather events. The analysis unit can also recognize data patterns using machine learning algorithms. For example, it can use traffic data to learn traffic congestion patterns at specific times of day or on specific days of the week to predict future traffic conditions. Furthermore, the analysis unit can perform simulations to predict the impact of urban development. For example, when constructing a new commercial facility in a specific area of ​​a city, it can simulate the impact to predict increased traffic volume and environmental effects. The analysis unit uses regression analysis to analyze data trends and predict future fluctuations. Machine learning algorithms learn from large amounts of data and recognize patterns to make more accurate predictions. Simulation is an important tool for predicting the impact of urban development and improving the accuracy of planning. This allows the analysis unit to quickly and accurately analyze collected data and understand the current state and future fluctuations of the city.

[0075] The scenario generation unit generates urban development scenarios based on data analyzed by the analysis unit. The scenario generation unit can generate multiple scenarios, for example, using a generation AI. The generation AI can adjust the level of detail in the scenarios based on specific urban challenges. For example, it can generate scenarios tailored to different purposes, such as scenarios aimed at mitigating traffic congestion or scenarios emphasizing environmental protection. Furthermore, the scenario generation unit can apply different generation algorithms depending on the urban category. For example, it can select an algorithm to generate the optimal scenario for different categories such as residential, commercial, and industrial areas. When generating traffic and environmental scenarios, the generation AI utilizes historical data and statistical information to generate realistic and actionable scenarios. The scenario generation unit can adjust the level of detail in the scenarios based on specific urban challenges and modify the scenario content as needed. This allows the scenario generation unit to generate diverse scenarios that address the current state and future challenges of the city, supporting urban development planning.

[0076] The comparison unit compares the scenarios generated by the scenario generation unit. For example, the comparison unit can perform cost comparisons and effectiveness comparisons. Specifically, it compares development and operational costs in each scenario to identify the most cost-effective scenario. It can also evaluate the environmental and social impacts of each scenario and select the most effective one. The comparison unit can also improve the accuracy of comparisons by considering the interrelationships between scenarios. For example, it evaluates the impact one scenario has on others and prioritizes selecting scenarios that complement each other. Furthermore, the comparison unit can perform comparisons by considering the attribute information of the scenario submitters. For example, it evaluates the expertise and experience of the scenario submitters and selects highly reliable scenarios. The comparison unit compares scenarios based on cost efficiency and environmental impact to select the most effective urban development plan. Considering the interrelationships between scenarios, it prioritizes comparing scenarios with the greatest impact and selects the optimal plan. It also evaluates the reliability of scenarios by considering the expertise and experience of the scenario submitters and selects the most reliable scenario. This allows the comparison unit to efficiently and accurately compare urban development scenarios and select the optimal plan.

[0077] The optimization unit proposes the optimal solution based on the results obtained by the comparison unit. For example, the optimization unit can propose a solution using an optimization algorithm. Specifically, it uses optimization methods such as genetic algorithms and linear programming to derive the optimal solution for urban development planning. Furthermore, the optimization unit can improve the accuracy of optimization by considering the interrelationships of scenarios. For example, if multiple scenarios influence each other, it evaluates these influences and proposes the most effective solution overall. In addition, the optimization unit can perform optimization while considering the attribute information of the scenario submitters. For example, it evaluates the expertise and experience of the scenario submitters and proposes a highly reliable solution. The optimization unit proposes the optimal solution based on cost efficiency and environmental impact, maximizing the effectiveness of urban development planning. Considering the interrelationships of scenarios, it prioritizes the optimization of scenarios with the greatest impact and proposes the most effective plan overall. It evaluates the reliability of the optimization by considering the expertise and experience of the scenario submitters and proposes the most reliable solution. In this way, the optimization unit can efficiently and accurately propose the optimal solution for urban development planning, supporting the sustainable development of cities.

[0078] The data collection unit can collect data from IoT sensors, satellite imagery, and urban databases. For example, the data collection unit can collect urban environmental data using IoT sensors. For example, it can collect environmental data using temperature sensors and humidity sensors. The data collection unit can also acquire satellite imagery and collect urban geographic information. For example, satellite imagery provides high-resolution images, allowing for a detailed understanding of urban geography. Furthermore, the data collection unit can collect population data and traffic data from urban databases. For example, urban databases provide demographic and traffic flow data, allowing for an understanding of urban dynamics. This enables data collection from diverse data sources. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from IoT sensors into AI and have the AI ​​perform data collection and analysis.

[0079] The analysis unit can analyze the collected data and predict the impact of urban development. The analysis unit can, for example, use statistical analysis to understand data trends. For example, it can use regression analysis to analyze data trends. The analysis unit can also recognize data patterns using machine learning algorithms. For example, machine learning algorithms can learn from large amounts of data and recognize patterns. Furthermore, the analysis unit can run simulations to predict the impact of urban development. For example, simulations can predict the impact of urban development and improve the accuracy of plans. This improves the accuracy of plans by predicting the impact of urban development. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform data analysis and prediction.

[0080] The scenario generation unit can generate multiple urban development scenarios. The scenario generation unit generates multiple scenarios, for example, using a generation AI. For example, the generation AI can generate traffic scenarios and environmental scenarios. Furthermore, the scenario generation unit can adjust the level of detail of the scenarios based on specific urban challenges. For example, the scenario generation unit can generate detailed scenarios for environmental problems. In addition, the scenario generation unit can apply different generation algorithms depending on the urban category. For example, the scenario generation unit can apply different generation algorithms for residential areas and commercial areas. This allows for the exploration of various urban development possibilities by generating multiple scenarios. Some or all of the above-described processes in the scenario generation unit may be performed using a generation AI, or without one. For example, the scenario generation unit can have the generation AI generate scenarios based on specific urban challenges.

[0081] The comparison unit can compare multiple generated scenarios. For example, it can perform cost comparisons or effectiveness comparisons. For instance, it can compare scenarios based on cost efficiency or environmental impact. Furthermore, the comparison unit can improve the accuracy of the comparison by considering the interrelationships between scenarios. For example, it can analyze the interrelationships between scenarios to perform a highly accurate comparison. Additionally, the comparison unit can perform comparisons considering the attribute information of the scenario submitters. For example, it can evaluate the reliability of a scenario by considering the submitter's expertise and experience. This allows for the selection of the optimal scenario by comparing multiple scenarios. Some or all of the above processing in the comparison unit may be performed using AI, or not. For example, the comparison unit can input the generated scenarios into an AI and have the AI ​​perform the scenario comparison.

[0082] The optimization unit can propose the optimal solution based on the comparison results. For example, the optimization unit can propose a solution using an optimization algorithm. For example, the optimization unit can propose the optimal solution based on cost efficiency and environmental impact. The optimization unit can also improve the accuracy of optimization by considering the interrelationships between scenarios. For example, the optimization unit can analyze the interrelationships between scenarios and perform highly accurate optimization. Furthermore, the optimization unit can perform optimization while considering the attribute information of the scenario submitters. For example, the optimization unit can evaluate the reliability of the optimization by considering the submitter's expertise and experience. This improves the efficiency of urban development by proposing the optimal solution. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the comparison results into AI and have the AI ​​execute the proposal of the optimal solution.

[0083] The Citizen Feedback Department can provide a portal for citizens to view proposed changes and provide feedback. For example, citizens can view proposed changes and provide opinions through an online portal. For example, the Citizen Feedback Department can collect citizens' opinions through surveys. Furthermore, the Citizen Feedback Department can collect citizen feedback in real time and reflect it in urban development scenarios. For example, citizens can view proposed changes and provide opinions through an online portal. Surveys are a means of collecting citizens' opinions and reflecting them in urban development scenarios. Real-time feedback collection allows for the rapid reflection of citizens' opinions. This enables citizens to view proposed changes and provide feedback. Some or all of the above processes in the Citizen Feedback Department may be performed using AI, or not. For example, the Citizen Feedback Department can input citizens' opinions collected through the online portal into AI and have the AI ​​analyze the feedback.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. For example, the data collection unit estimates the user's emotions and reduces the frequency of data collection if the user is stressed, increases the frequency of data collection if the user is relaxed, and shortens the timing of data collection if the user is in a hurry. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of data collection.

[0085] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. Furthermore, the data collection unit can identify areas for improvement in the collection method based on past data collection history and optimize it. In addition, the data collection unit can analyze past data collection history, find patterns in collection methods, and select the optimal method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. It can identify areas for improvement in the collection method and optimize it. It can find patterns in collection methods and select the optimal method. Thus, by analyzing past data collection history, the optimal collection method can be selected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0086] The data collection unit can filter data based on specific areas or time zones within a city. For example, the unit can prioritize data collection in specific areas of a city to collect important data. It can also collect data during specific time zones to understand fluctuations between time zones. Furthermore, the unit can combine city areas and time zones for efficient data collection. For instance, the unit prioritizes data collection in specific areas of a city to collect important data. It collects data during specific time zones to understand fluctuations between time zones. It filters data by combining city areas and time zones for efficient data collection. This allows for efficient data collection by filtering based on specific areas and time zones within a city. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can have AI perform filtering based on specific areas and time zones within a city.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. Conversely, if the user is relaxed, the data collection unit can prioritize the collection of detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of highly important data. For example, the data collection unit estimates the user's emotions and postpones the collection of less important data if the user is stressed. If the user is relaxed, it prioritizes the collection of detailed data. If the user is in a hurry, it prioritizes the collection of highly important data. This enables efficient data collection by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the data.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering urban event information during data collection. For example, the data collection unit can prioritize the collection of relevant data based on information about events held in the city. The data collection unit can also determine the priority of data collection by considering the scale and impact of events. Furthermore, the data collection unit can plan data collection based on the location and time of events. For example, the data collection unit prioritizes the collection of relevant data based on information about events held in the city. It determines the priority of data collection by considering the scale and impact of events. It plans data collection based on the location and time of events. This allows for the priority collection of highly relevant data by considering urban event information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input urban event information into AI and have the AI ​​perform the collection of highly relevant data.

[0089] The data collection unit can analyze social media trends and collect relevant data during data collection. For example, the data collection unit can analyze social media trends in real time and collect relevant data. The data collection unit can also monitor trend fluctuations and adjust the timing of data collection. Furthermore, the data collection unit can determine the type and scope of data to collect based on trends. For example, the data collection unit analyzes social media trends in real time and collects relevant data. It monitors trend fluctuations and adjusts the timing of data collection. It determines the type and scope of data to collect based on trends. This allows for the efficient collection of relevant data by analyzing social media trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media trend data into AI and have the AI ​​perform the collection of relevant data.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, the analysis unit estimates the user's emotions and provides simple and easy-to-understand analysis results if the user is nervous. If the user is relaxed, it provides detailed analysis results. If the user is in a hurry, it provides concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the way the analysis is presented.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can allocate analysis resources according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance, a simplified analysis on data with low importance, and allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an environmental analysis algorithm to environmental data. It can also apply a traffic analysis algorithm to traffic data. Furthermore, it can apply an economic analysis algorithm to economic data. For example, the analysis unit can apply an environmental analysis algorithm to environmental data, a traffic analysis algorithm to traffic data, and an economic analysis algorithm to economic data. By applying different analysis algorithms depending on the data category, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​apply the appropriate analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. For example, the analysis unit estimates the user's emotions and provides a short, concise analysis if the user is in a hurry. If the user is relaxed, it provides a detailed analysis. If the user is excited, it provides a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0094] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. It can also analyze long-term trends based on historical data. Furthermore, the analysis unit can allocate analysis resources according to the data collection timing. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information, analyze long-term trends based on historical data, and allocate analysis resources according to the data collection timing. This allows for the provision of real-time information by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​determine the analysis priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide efficient results. Alternatively, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can allocate analysis resources according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to provide efficient results, postpone the analysis of less relevant data and prioritize the analysis of important data, and allocate analysis resources according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0096] The scenario generation unit can estimate the user's emotions and adjust the scenario generation method based on the estimated user emotions. For example, if the user is relaxed, the scenario generation unit can generate a detailed scenario. If the user is in a hurry, the scenario generation unit can generate a concise scenario. Furthermore, if the user is excited, the scenario generation unit can generate a visually stimulating scenario. For example, the scenario generation unit estimates the user's emotions and generates a detailed scenario if the user is relaxed, a concise scenario if the user is in a hurry, and a visually stimulating scenario if the user is excited. By adjusting the scenario generation method according to the user's emotions, it is possible to provide the user with an appropriate scenario. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the scenario generation unit may be performed using AI, for example, or without AI. For example, the scenario generation unit can input user emotion data into an AI and have the AI ​​adjust the scenario generation method.

[0097] The scenario generation unit can adjust the level of detail of a scenario based on specific urban issues during scenario generation. For example, the scenario generation unit can generate detailed scenarios for environmental problems. It can also generate detailed scenarios for traffic problems. Furthermore, it can generate detailed scenarios for economic problems. For example, the scenario generation unit generates detailed scenarios for environmental problems. It generates detailed scenarios for traffic problems. It generates detailed scenarios for economic problems. By adjusting the level of detail of a scenario based on specific urban issues, it is possible to generate highly accurate scenarios. Some or all of the above processing in the scenario generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the scenario generation unit can input specific urban issues into an AI and have the AI ​​perform the adjustment of the level of detail of the scenarios.

[0098] The scenario generation unit can apply different generation algorithms depending on the city category when generating scenarios. For example, the scenario generation unit can apply an environmental scenario generation algorithm to the environmental category. It can also apply a transportation scenario generation algorithm to the transportation category. Furthermore, it can apply an economic scenario generation algorithm to the economic category. For example, the scenario generation unit can apply an environmental scenario generation algorithm to the environmental category, a transportation scenario generation algorithm to the transportation category, and an economic scenario generation algorithm to the economic category. By applying different generation algorithms depending on the city category, appropriate scenarios can be generated. Some or all of the above processing in the scenario generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the scenario generation unit can input the city category into the AI ​​and have the AI ​​execute the application of an appropriate generation algorithm.

[0099] The scenario generation unit can estimate the user's emotions and adjust the length of the scenario based on the estimated emotions. For example, if the user is in a hurry, the scenario generation unit can generate a short, concise scenario. If the user is relaxed, the scenario generation unit can generate a detailed scenario. Furthermore, if the user is excited, the scenario generation unit can generate a visually stimulating scenario. For example, the scenario generation unit estimates the user's emotions and generates a short, concise scenario if the user is in a hurry. If the user is relaxed, it generates a detailed scenario. If the user is excited, it generates a visually stimulating scenario. By adjusting the length of the scenario according to the user's emotions, an appropriate scenario can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the scenario generation unit may be performed using a generative AI, for example, or without a generative AI. For example, the scenario generation unit can input user emotion data into the AI ​​and have the AI ​​adjust the length of the scenario.

[0100] The scenario generation unit can determine the priority of scenarios based on the city's historical data during scenario generation. For example, the scenario generation unit can prioritize generating scenarios of high importance based on historical data. It can also prioritize generating scenarios with a large impact based on historical data. Furthermore, it can prioritize generating scenarios with high urgency based on historical data. For example, the scenario generation unit prioritizes generating scenarios of high importance based on historical data. It prioritizes generating scenarios with a large impact. It prioritizes generating scenarios with high urgency. In this way, by determining the priority of scenarios based on the city's historical data, important scenarios can be generated preferentially. Some or all of the above processing in the scenario generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the scenario generation unit can input the city's historical data into an AI and have the AI ​​perform the determination of scenario priorities.

[0101] The scenario generation unit can adjust the order of scenarios based on relevant city data during scenario generation. For example, the scenario generation unit can prioritize generating scenarios of high importance based on relevant data. It can also prioritize generating scenarios with a large impact based on relevant data. Furthermore, it can prioritize generating scenarios with high urgency based on relevant data. For example, the scenario generation unit prioritizes generating scenarios of high importance based on relevant data. It prioritizes generating scenarios with a large impact. It prioritizes generating scenarios with high urgency. This allows for efficient scenario generation by adjusting the order of scenarios based on relevant city data. Some or all of the above processing in the scenario generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the scenario generation unit can input relevant city data into an AI and have the AI ​​perform the adjustment of the scenario order.

[0102] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated emotions. For example, if the user is relaxed, the comparison unit can provide detailed comparison criteria. If the user is in a hurry, the comparison unit can provide concise comparison criteria. Furthermore, if the user is excited, the comparison unit can provide visually stimulating comparison criteria. For example, the comparison unit estimates the user's emotions and provides detailed comparison criteria when the user is relaxed, concise comparison criteria when the user is in a hurry, and visually stimulating comparison criteria when the user is excited. By adjusting the comparison criteria according to the user's emotions, it is possible to provide the user with comparison results that are appropriate for them. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit can input user emotion data into AI and have the AI ​​perform the adjustment of the comparison criteria.

[0103] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between scenarios. For example, the comparison unit can analyze the interrelationships between scenarios and perform a highly accurate comparison. The comparison unit can also prioritize the comparison of scenarios with a greater impact by considering the interrelationships between scenarios. Furthermore, the comparison unit can adjust the comparison criteria based on the interrelationships between scenarios. For example, the comparison unit can analyze the interrelationships between scenarios and perform a highly accurate comparison. It can prioritize the comparison of scenarios with a greater impact by considering the interrelationships between scenarios. It can adjust the comparison criteria based on the interrelationships between scenarios. This makes it possible to perform a highly accurate comparison by considering the interrelationships between scenarios. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input the interrelationships between scenarios into AI and have the AI ​​perform the improvement of the comparison accuracy.

[0104] The comparison unit can perform comparisons while considering the attribute information of the scenario submitters. For example, the comparison unit can compare scenarios while considering the submitters' expertise and experience. The comparison unit can also evaluate the reliability of scenarios based on the submitters' attribute information. Furthermore, the comparison unit can determine the priority of scenarios while considering the submitters' attribute information. For example, the comparison unit compares scenarios while considering the submitters' expertise and experience. It evaluates the reliability of scenarios based on the submitters' attribute information. It determines the priority of scenarios while considering the submitters' attribute information. This makes it possible to perform highly reliable comparisons by considering the attribute information of the scenario submitters. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input the submitters' attribute information into AI and have the AI ​​perform the comparison.

[0105] The comparison unit can estimate the user's emotions and adjust the order in which the comparison results are displayed based on the estimated emotions. For example, if the user is relaxed, the comparison unit can prioritize displaying detailed comparison results. If the user is in a hurry, the comparison unit can prioritize displaying concise comparison results. Furthermore, if the user is excited, the comparison unit can prioritize displaying visually stimulating comparison results. For example, the comparison unit estimates the user's emotions and prioritizes displaying detailed comparison results if the user is relaxed, concise comparison results if the user is in a hurry, and visually stimulating comparison results if the user is excited. This allows the system to provide the user with appropriate comparison results by adjusting the order in which the comparison results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input user emotion data into the AI ​​and have the AI ​​adjust the display order of the comparison results.

[0106] The comparison unit can perform comparisons while considering the geographical distribution of the scenarios. For example, the comparison unit can perform regional comparisons based on the geographical distribution of the scenarios. The comparison unit can also prioritize comparisons of regions with a greater impact, taking geographical distribution into consideration. Furthermore, the comparison unit can adjust the comparison criteria based on geographical distribution. For example, the comparison unit can perform regional comparisons based on the geographical distribution of the scenarios. It can prioritize comparisons of regions with a greater impact, taking geographical distribution into consideration. It can adjust the comparison criteria based on geographical distribution. This makes it possible to perform regional comparisons by considering the geographical distribution of the scenarios. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input the geographical distribution of the scenarios into AI and have the AI ​​perform the comparison.

[0107] The comparison unit can improve the accuracy of the comparison by referring to relevant literature for the scenarios during the comparison process. For example, the comparison unit can evaluate the reliability of the scenarios based on the relevant literature. The comparison unit can also adjust the comparison criteria for the scenarios by referring to the relevant literature. Furthermore, the comparison unit can predict the impact of the scenarios based on the relevant literature. For example, the comparison unit evaluates the reliability of the scenarios based on the relevant literature. It adjusts the comparison criteria for the scenarios by referring to the relevant literature. It predicts the impact of the scenarios based on the relevant literature. This makes it possible to perform a highly accurate comparison by referring to the relevant literature for the scenarios. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input the relevant literature for the scenarios into AI and have the AI ​​perform the comparison.

[0108] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is relaxed, the optimization unit can provide a detailed optimization method. If the user is in a hurry, the optimization unit can provide a concise optimization method. Furthermore, if the user is excited, the optimization unit can provide a visually stimulating optimization method. For example, the optimization unit estimates the user's emotions and provides a detailed optimization method if the user is relaxed, a concise optimization method if the user is in a hurry, and a visually stimulating optimization method if the user is excited. By adjusting the optimization method according to the user's emotions, the optimization unit can provide an appropriate optimization result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the optimization method.

[0109] The optimization unit can improve the accuracy of optimization by considering the interrelationships between scenarios during optimization. For example, the optimization unit can analyze the interrelationships between scenarios and perform highly accurate optimization. The optimization unit can also prioritize the optimization of scenarios with a greater impact by considering the interrelationships between scenarios. Furthermore, the optimization unit can adjust the optimization criteria based on the interrelationships between scenarios. For example, the optimization unit can analyze the interrelationships between scenarios and perform highly accurate optimization. It can prioritize the optimization of scenarios with a greater impact by considering the interrelationships between scenarios. It can adjust the optimization criteria based on the interrelationships between scenarios. This makes highly accurate optimization possible by considering the interrelationships between scenarios. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the interrelationships between scenarios into AI and have AI perform the optimization.

[0110] The optimization unit can perform optimization while considering the attribute information of the scenario submitter. For example, the optimization unit can optimize the scenario while considering the submitter's expertise and experience. The optimization unit can also evaluate the reliability of the scenario based on the submitter's attribute information. Furthermore, the optimization unit can determine the priority of the scenario while considering the submitter's attribute information. For example, the optimization unit optimizes the scenario while considering the submitter's expertise and experience. It evaluates the reliability of the scenario based on the submitter's attribute information. It determines the priority of the scenario while considering the submitter's attribute information. This makes highly reliable optimization possible by considering the attribute information of the scenario submitter. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the submitter's attribute information into AI and have the AI ​​perform the optimization.

[0111] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is relaxed, the optimization unit can prioritize detailed optimization. If the user is in a hurry, the optimization unit can also prioritize concise optimization. Furthermore, if the user is excited, the optimization unit can also prioritize visually stimulating optimization. For example, the optimization unit estimates the user's emotions and prioritizes detailed optimization if the user is relaxed, concise optimization if the user is in a hurry, and visually stimulating optimization if the user is excited. By determining optimization priorities according to the user's emotions, the system can provide the user with appropriate optimization results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into the AI ​​and have the AI ​​determine the optimization priorities.

[0112] The optimization unit can perform optimization while considering the geographical distribution of the scenarios. For example, the optimization unit can perform regional optimization based on the geographical distribution of the scenarios. The optimization unit can also prioritize the optimization of areas with a greater impact, taking the geographical distribution into consideration. Furthermore, the optimization unit can adjust the optimization criteria based on the geographical distribution. For example, the optimization unit performs regional optimization based on the geographical distribution of the scenarios. It prioritizes the optimization of areas with a greater impact, taking the geographical distribution into consideration. It adjusts the optimization criteria based on the geographical distribution. This makes regional optimization possible by considering the geographical distribution of the scenarios. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the geographical distribution of the scenarios into AI and have the AI ​​perform the optimization.

[0113] The optimization unit can improve the accuracy of optimization by referring to relevant literature for the scenario during optimization. For example, the optimization unit can evaluate the reliability of the scenario based on the relevant literature. The optimization unit can also adjust the optimization criteria for the scenario by referring to the relevant literature. Furthermore, the optimization unit can predict the impact of the scenario based on the relevant literature. For example, the optimization unit evaluates the reliability of the scenario based on the relevant literature. It adjusts the optimization criteria for the scenario by referring to the relevant literature. It predicts the impact of the scenario based on the relevant literature. As a result, highly accurate optimization becomes possible by referring to relevant literature for the scenario. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input relevant literature for the scenario into AI and have the AI ​​perform the optimization.

[0114] The citizen feedback unit can estimate the user's emotions and adjust how feedback is displayed based on the estimated emotions. For example, if the user is relaxed, the citizen feedback unit can display detailed feedback. If the user is in a hurry, it can display concise feedback. Furthermore, if the user is excited, it can display visually stimulating feedback. For example, the citizen feedback unit estimates the user's emotions and displays detailed feedback if the user is relaxed, concise feedback if the user is in a hurry, and visually stimulating feedback if the user is excited. This allows the system to provide appropriate feedback to the user by adjusting how feedback is displayed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the citizen feedback unit may be performed using AI, or not using AI. For example, the citizen feedback unit can input user emotion data into an AI and have the AI ​​adjust how feedback is displayed.

[0115] The Citizen Feedback Department can select the optimal display method when displaying feedback by referring to the user's past feedback history. For example, the Citizen Feedback Department can select the optimal display method based on the user's past feedback history. The Citizen Feedback Department can also analyze past feedback history and identify areas for improvement in the display method. Furthermore, the Citizen Feedback Department can refer to past feedback history to find patterns in the display method and select the optimal method. For example, the Citizen Feedback Department selects the optimal display method based on the user's past feedback history. It analyzes past feedback history and identifies areas for improvement in the display method. It refers to past feedback history to find patterns in the display method and select the optimal method. In this way, the optimal display method can be selected by referring to the user's past feedback history. Some or all of the above processes in the Citizen Feedback Department may be performed using AI, for example, or without AI. For example, the Citizen Feedback Department can input the user's past feedback history into AI and have the AI ​​select the optimal display method.

[0116] The citizen feedback unit can estimate the user's emotions and adjust the feedback operation procedure based on the estimated user emotions. For example, if the user is relaxed, the citizen feedback unit can provide detailed operation procedures. If the user is in a hurry, it can also provide concise operation procedures. Furthermore, if the user is excited, it can provide visually stimulating operation procedures. For example, the citizen feedback unit estimates the user's emotions and provides detailed operation procedures if the user is relaxed, concise operation procedures if the user is in a hurry, and visually stimulating operation procedures if the user is excited. In this way, by adjusting the feedback operation procedure according to the user's emotions, it is possible to provide the user with an appropriate operation procedure. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the citizen feedback unit may be performed using AI, for example, or without using AI. For example, the Citizen Feedback Department can input user emotion data into the AI ​​and have the AI ​​adjust the operating procedures.

[0117] The Citizen Feedback Unit can select the optimal display method when displaying feedback, taking into account the user's device information. For example, if the user is using a smartphone, the Citizen Feedback Unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the Citizen Feedback Unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the Citizen Feedback Unit can provide a concise and highly visible display method. This allows the Citizen Feedback Unit to select the optimal display method by considering the user's device information. Some or all of the above processing in the Citizen Feedback Unit may be performed using AI, or not. For example, the Citizen Feedback Unit can input the user's device information into AI and have AI select the optimal display method.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The urban development simulator may also include a prediction unit. Based on data obtained from the data collection unit, the prediction unit can forecast future changes in the urban environment. For example, the prediction unit can forecast the impact of climate change and assess its impact on urban infrastructure. It can also forecast demographic changes and assess future housing and transportation demand. Furthermore, it can forecast economic fluctuations and assess their impact on urban economic activity. This allows the prediction unit to provide information useful for urban development planning by forecasting future changes in the urban environment. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not. For example, the prediction unit can input collected data into an AI and have the AI ​​perform predictions of future changes.

[0120] The urban development simulator may further include an evaluation unit. The evaluation unit can evaluate the scenarios generated by the scenario generation unit and clarify the advantages and disadvantages of each scenario. For example, the evaluation unit can conduct an environmental impact assessment and evaluate the impact each scenario has on the environment. It can also conduct an economic impact assessment and evaluate the impact each scenario has on the economy. Furthermore, it can conduct a social impact assessment and evaluate the impact each scenario has on society. In this way, the evaluation unit can provide information useful for urban development planning by clarifying the advantages and disadvantages of each scenario. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the generated scenarios into an AI and have the AI ​​perform the evaluation of each scenario.

[0121] The urban development simulator may also include a notification unit. This notification unit can inform stakeholders of the progress of urban development and any significant changes. For example, it can notify stakeholders of the progress of urban development in real time. It can also send alerts to stakeholders when significant changes occur. Furthermore, it can notify stakeholders when the urban development plan is completed. This allows the notification unit to improve the efficiency of urban development by informing stakeholders of its progress and any significant changes. Some or all of the above processes in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input the progress of urban development into an AI and have the AI ​​send notifications.

[0122] The urban development simulator may further include a simulation unit. This simulation unit can simulate a virtual urban environment based on urban development scenarios. For example, the simulation unit can perform traffic simulations to evaluate the impact of urban development on traffic. It can also perform environmental simulations to evaluate the impact of urban development on the environment. Furthermore, it can perform economic simulations to evaluate the impact of urban development on the economy. In this way, the simulation unit can provide information useful for urban development planning by simulating a virtual urban environment. Some or all of the above-described processes in the simulation unit may be performed using, for example, AI, or not. For example, the simulation unit can input urban development scenarios into an AI and have the AI ​​perform the simulation.

[0123] The urban development simulator may also include a data visualization unit. This unit can visually display collected data and analysis results. For example, it can display urban geographical information on a map, allowing for a visual understanding of the city's current state. It can also display urban development scenarios in graphs and charts, facilitating comparisons between scenarios. Furthermore, it can display the impact of urban development using 3D models, making it easier to understand visually. Thus, by visually displaying collected data and analysis results, the data visualization unit can provide information useful for urban development planning. Some or all of the above-described processes in the data visualization unit may be performed using AI, for example, or without AI. For instance, the data visualization unit can input collected data into an AI and have the AI ​​perform the visual display.

[0124] The urban development simulator may also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the simulator's operation based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can simplify the operation to reduce the user's burden. It can also provide more detailed operation instructions if the user is relaxed. Furthermore, if the user is excited, the emotion estimation unit can provide visually stimulating operation instructions. In this way, the emotion estimation unit can provide a user-friendly simulator by adjusting the simulator's operation according to the user's emotions. Some or all of the above processing in the emotion estimation unit may be performed using, for example, AI, or not. For example, the emotion estimation unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the operation instructions.

[0125] The city development simulator may also include an emotional feedback unit. This unit can estimate the user's emotions and provide feedback based on those estimates. For example, if the user is stressed, the emotional feedback unit can display encouraging messages to alleviate their feelings. It can also provide detailed feedback if the user is relaxed. Furthermore, if the user is excited, the emotional feedback unit can provide visually stimulating feedback. In this way, the emotional feedback unit can provide appropriate feedback to the user by responding to their emotions. Some or all of the above processing in the emotional feedback unit may be performed using AI, for example, or without AI. For example, the emotional feedback unit can input the user's emotional data into an AI and have the AI ​​provide the feedback.

[0126] The city development simulator may also include an emotion analysis unit. This unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the emotion analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is excited, it can provide visually stimulating analysis results. This allows the emotion analysis unit to provide analysis results that are easy for the user to understand by adjusting the display method according to the user's emotions. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the display method of the analysis results.

[0127] The city development simulator may also include an emotion notification unit. This unit can estimate the user's emotions and adjust the content of notifications based on those emotions. For example, if the user is stressed, the emotion notification unit can display only important notifications to reduce the user's burden. It can also provide detailed notifications if the user is relaxed. Furthermore, if the user is excited, the emotion notification unit can provide visually stimulating notifications. In this way, the emotion notification unit can provide appropriate notifications to the user by adjusting the content of notifications according to the user's emotions. Some or all of the above processing in the emotion notification unit may be performed using AI, for example, or without AI. For example, the emotion notification unit can input user emotion data into an AI and have the AI ​​adjust the content of the notifications.

[0128] The city development simulator may also include an emotion optimization unit. This unit can estimate the user's emotions and adjust the optimization method based on those emotions. For example, if the user is stressed, the emotion optimization unit can provide a simplified optimization method to reduce the user's burden. It can also provide a more detailed optimization method if the user is relaxed. Furthermore, if the user is excited, the emotion optimization unit can provide a visually stimulating optimization method. In this way, the emotion optimization unit can provide an appropriate optimization result for the user by adjusting the optimization method according to the user's emotions. Some or all of the above processing in the emotion optimization unit may be performed using, for example, AI, or without AI. For example, the emotion optimization unit can input user emotion data into an AI and have the AI ​​adjust the optimization method.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The data collection unit collects data. The data collection unit can collect urban environmental data using, for example, IoT sensors. It can also acquire satellite imagery and collect urban geographical information. Furthermore, the data collection unit can collect population and traffic data from urban databases. For example, the data collection unit collects environmental data using temperature and humidity sensors. Satellite imagery provides high-resolution images, allowing for a detailed understanding of the city's geography. Urban databases provide demographic and traffic flow data, enabling an understanding of urban dynamics. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use statistical analysis to understand data trends. It can also recognize data patterns using machine learning algorithms. Furthermore, the analysis unit can run simulations to predict the impact of urban development. For example, the analysis unit can analyze data trends using regression analysis. Machine learning algorithms learn from large amounts of data and recognize patterns. Simulations predict the impact of urban development and improve the accuracy of the plan. Step 3: The scenario generation unit generates urban development scenarios based on the data analyzed by the analysis unit. The scenario generation unit can generate multiple scenarios, for example, using a generation AI. The scenario generation unit can also adjust the level of detail of the scenarios based on specific urban challenges. Furthermore, the scenario generation unit can apply different generation algorithms depending on the urban category. For example, the generation AI generates traffic scenarios and environmental scenarios. The scenario generation unit adjusts the level of detail of the scenarios based on specific urban challenges. Depending on the urban category, different generation algorithms such as residential and commercial areas are applied. Step 4: The comparison unit compares the scenarios generated by the scenario generation unit. The comparison unit can perform cost comparisons, effect comparisons, etc. The comparison unit can also improve the accuracy of the comparison by considering the interrelationships of the scenarios. Furthermore, the comparison unit can perform comparisons by considering the attribute information of the scenario submitters. For example, the comparison unit compares scenarios based on cost efficiency and environmental impact. It prioritizes the comparison of scenarios with a greater impact by considering the interrelationships of the scenarios. It evaluates the reliability of the scenarios by considering the expertise and experience of the scenario submitters. Step 5: The optimization unit proposes the optimal solution based on the results obtained by the comparison unit. The optimization unit can, for example, propose a solution using an optimization algorithm. The optimization unit can also improve the accuracy of the optimization by considering the interrelationships of the scenarios. Furthermore, the optimization unit can perform optimization by considering the attribute information of the scenario submitters. For example, the optimization unit proposes the optimal solution based on cost efficiency and environmental impact. It prioritizes the optimization of scenarios with a large impact by considering the interrelationships of the scenarios. It evaluates the reliability of the optimization by considering the expertise and experience of the scenario submitters.

[0131] 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.

[0132] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0133] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, scenario generation unit, comparison unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect urban environmental data using IoT sensors and cameras 42 of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The scenario generation unit generates urban development scenarios using the specific processing unit 290 of the data processing unit 12. The comparison unit compares the scenarios generated by the specific processing unit 290 of the data processing unit 12. The optimization unit proposes the optimal solution using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0138] 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0140] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] 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.

[0142] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, scenario generation unit, comparison unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect urban environmental data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The scenario generation unit generates urban development scenarios using the specific processing unit 290 of the data processing unit 12. The comparison unit compares the scenarios generated by the specific processing unit 290 of the data processing unit 12. The optimization unit proposes the optimal solution using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0154] 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.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0156] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] 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.

[0158] 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.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the data collection unit, analysis unit, scenario generation unit, comparison unit, and optimization unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the data collection unit can collect urban environmental data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The scenario generation unit generates urban development scenarios using the specific processing unit 290 of the data processing unit 12. The comparison unit compares the scenarios generated by the specific processing unit 290 of the data processing unit 12. The optimization unit proposes the optimal solution using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0170] 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.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0172] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] 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.

[0174] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0175] 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.

[0176] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0177] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0178] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0179] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0183] Each of the multiple elements described above, including the collection unit, analysis unit, scenario generation unit, comparison unit, and optimization unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit can collect urban environmental data using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The scenario generation unit generates urban development scenarios using the specific processing unit 290 of the data processing unit 12. The comparison unit compares the scenarios generated by the specific processing unit 290 of the data processing unit 12. The optimization unit proposes the optimal solution using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0184] 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.

[0185] Figure 9 shows the 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.

[0186] 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.

[0187] 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.

[0188] 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, and motorcycles, 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 based, for example, 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.

[0189] 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."

[0190] 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.

[0191] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0200] 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 other things 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.

[0201] 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.

[0202] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A scenario generation unit generates urban development scenarios based on data analyzed by the aforementioned analysis unit, A comparison unit that compares the scenarios generated by the scenario generation unit, The system includes an optimization unit that proposes an optimal solution based on the results obtained by the comparison unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data from IoT sensors, satellite imagery, and city databases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data and predict the impact of urban development. The system described in Appendix 1, characterized by the features described herein. (Note 4) The scenario generation unit, Generate multiple urban development scenarios The system described in Appendix 1, characterized by the features described herein. (Note 5) The comparison unit is, Compare the multiple scenarios that have been generated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, We propose the optimal solution based on the comparison results. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a Citizen Feedback Department that provides a portal for citizens to view proposed changes and provide feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on specific areas of the city and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account urban event information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, analyze social media trends and gather relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The scenario generation unit, It estimates the user's emotions and adjusts the scenario generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The scenario generation unit, When generating scenarios, adjust the level of detail of the scenarios based on specific challenges in the city. The system described in Appendix 1, characterized by the features described herein. (Note 22) The scenario generation unit, When generating scenarios, different generation algorithms are applied depending on the city category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The scenario generation unit, It estimates the user's emotions and adjusts the scenario length based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The scenario generation unit, When generating scenarios, prioritize scenarios based on the city's historical data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The scenario generation unit, When generating scenarios, adjust the order of scenarios based on relevant city data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The comparison unit is, It estimates the user's emotions and adjusts the comparison criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The comparison unit is, When making comparisons, consider the interrelationships between scenarios to improve the accuracy of the comparison. The system described in Appendix 1, characterized by the features described herein. (Note 28) The comparison unit is, When making comparisons, the attribute information of the scenario submitters will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The comparison unit is, It estimates the user's sentiment and adjusts the order in which comparison results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The comparison unit is, When making comparisons, the geographical distribution of the scenarios should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The comparison unit is, When making comparisons, refer to relevant literature for each scenario to improve the accuracy of the comparison. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, During optimization, consider the interrelationships between scenarios to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, During optimization, the attribute information of the scenario submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The optimization unit, During optimization, the geographical distribution of the scenarios is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 37) The optimization unit, During optimization, refer to relevant literature for the scenario to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned citizen feedback department, It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned citizen feedback department, When displaying feedback, the system will refer to the user's past feedback history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned citizen feedback department, It estimates the user's emotions and adjusts the feedback process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned citizen feedback department, When displaying feedback, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A scenario generation unit generates urban development scenarios based on data analyzed by the aforementioned analysis unit, A comparison unit that compares the scenarios generated by the scenario generation unit, The system includes an optimization unit that proposes an optimal solution based on the results obtained by the comparison unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data from IoT sensors, satellite imagery, and city databases. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected data and predict the impact of urban development. The system according to feature 1.

4. The scenario generation unit, Generate multiple urban development scenarios The system according to feature 1.

5. The comparison unit is, Compare the multiple scenarios that have been generated. The system according to feature 1.

6. The optimization unit, We propose the optimal solution based on the comparison results. The system according to feature 1.

7. It includes a Citizen Feedback Department that provides a portal for citizens to view proposed changes and provide feedback. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filtering is performed based on specific areas of the city and time periods. The system according to feature 1.

Images