system

Abstract

Provide a system. 【Solution means】 Means for collecting information on the region, Means for analyzing the collected information, Means for referring to a reference case database to identify similar cases, Generating algorithm means for generating a redevelopment plan based on regional characteristics, Means for simulating the economic effects of the generated plan, Means for evaluating sustainability and optimizing environmental design, Means for visualizing the redevelopment plan using augmented reality technology based on regional characteristics, Scenario analysis means for predicting long-term future impacts, A system including.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0006] "Regional data" includes information related to the target area, such as population, infrastructure, transportation, economy, and environment.

[0007] "Methods of analysis" refers to the process of applying statistical methods and machine learning algorithms to collected data to extract meaningful information.

[0008] The "Success Story Database" is a database containing examples of redevelopment projects from around the world, and is used to refer to similar cases.

[0009] A "redevelopment plan" refers to a plan document that outlines the design and strategies for revitalizing a particular area.

[0010] A "generative algorithm" is a mathematical method for predicting specific results from input data and automatically creating optimal suggestions or designs.

[0011] A "simulation" is a method for analyzing the impact of real-world economic conditions and environmental factors by simulating them.

[0012] "Sustainability" refers to a state in which redevelopment is environmentally, economically, and socially sustainable, and the benefits continue into the future.

[0013] "Environmental design" refers to a design approach in architecture and urban planning that minimizes the impact on the environment.

[0014] "Scenario analysis" is a method for comparing and examining results based on different hypotheses or future scenarios.

[0015] "Feedback" refers to opinions and evaluations of proposals and plans, and is used to help improve them. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

[0021] In the following embodiments, the 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.

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a system that utilizes generative AI to generate and evaluate redevelopment plans tailored to regional characteristics, and in order to implement this, the server, terminal, and user each play a specific role.

[0038] The server functions as the central processing unit of this system. The server collects various data about the target area. Specifically, it obtains local population data, traffic patterns, geographic information, infrastructure status, and environmental data from open databases and real-time sensors. Based on this data, the server applies analytical algorithms to understand the current situation in the area and identify challenges.

[0039] Based on the analysis results, the server accesses a database of successful case studies from around the world to identify similar redevelopment projects. Considering the factors extracted from these success stories, the server uses generative AI to create a redevelopment plan tailored to the specific area. This plan includes concrete suggestions for revitalizing the area, such as the placement of commercial facilities and the expansion of public transportation.

[0040] The created redevelopment plan is presented to the user via a terminal. The terminal displays the plan in a visually easy-to-understand manner and supports the user in evaluating the plan and reviewing the proposed content. The user can send feedback on the proposal to the server via the terminal.

[0041] Based on the collected feedback, the server uses generative AI to further improve the plan. Furthermore, it simulates the economic effects of the plan and evaluates its impact on the local economy. To ensure sustainability, the server evaluates it from an environmental design perspective and proposes an optimized design.

[0042] Users can ultimately select the plan that best matches the characteristics of their area based on the multiple plans presented. This minimizes uncertainties during the planning stage, resulting in a more feasible and sustainable redevelopment plan.

[0043] This invention makes it possible to effectively implement redevelopment tailored to regional characteristics and bring new value to local communities. For example, in sparsely populated suburban areas, AI can propose the optimal placement of commercial facilities and provide plans to attract new residents and visitors, thereby promoting regional revitalization.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects data for the target area. It obtains demographic data, traffic volume data, environmental data, etc. from open databases, and collects traffic patterns and air quality data from real-time sensors. This collects basic information to understand the current situation in the area.

[0047] Step 2:

[0048] The server analyzes the collected data. It applies machine learning algorithms to perform data analysis to identify local and potential issues. For example, it extracts trends such as population decline and traffic congestion times.

[0049] Step 3:

[0050] The server accesses a global database of successful case studies. Based on regional characteristics, it searches for similar redevelopment projects and identifies success factors. Natural language processing techniques are used to generate summaries of relevant case studies.

[0051] Step 4:

[0052] The server generates redevelopment plans using AI. Based on the analysis results and successful case studies, it creates specific plans and proposes the optimal placement of commercial facilities, public facilities, and residential areas.

[0053] Step 5:

[0054] The device displays the generated plan to the user. Visualization tools are used to clearly present the plan's contents and help the user review the details. The user can evaluate the plan and provide feedback.

[0055] Step 6:

[0056] The server receives feedback from users and modifies the plan based on that information. The generative AI model, which incorporates the feedback, improves the accuracy of the plan.

[0057] Step 7:

[0058] The server simulates the economic effects of the revised plan. Using an economic model, it generates data predicting the potential for job creation, increased tax revenue, and regional economic revitalization.

[0059] Step 8:

[0060] The server optimizes environmental design to assess sustainability. It evaluates resource consumption and energy efficiency and proposes the optimal environmental design.

[0061] Step 9:

[0062] The server predicts long-term future impacts based on multiple scenarios. Using scenario analysis, it evaluates the future impacts of regional redevelopment and proposes the optimal strategy.

[0063] Step 10:

[0064] Users determine the regional redevelopment strategy based on the final plan. They select a sustainable plan that matches the characteristics of the region, based on the presented data and scenario information.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] In modern regional development, there is a problem where uniform development plans that disregard local characteristics are being implemented, resulting in insufficient sustainability and economic revitalization of the region. Furthermore, past development plans often fail to efficiently utilize unique local data and do not take future impacts into consideration. As a result, it becomes difficult to respond to unexpected environmental changes and to meet the needs of residents.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for collecting local information, means for analyzing the collected information, and means for referring to records of successful cases and identifying similar cases. This enables the generation of redevelopment plans closely tailored to local characteristics, facilitating sustainable development and economic revitalization. Furthermore, by revising the plan based on user feedback, development that can flexibly respond to the needs of residents is realized.

[0070] "Means for collecting local information" refers to methods or devices for efficiently collecting diverse information related to a given region, such as population, transportation, geography, infrastructure, and environment.

[0071] "Means for analyzing collected information" refers to methods or devices for conducting statistical, spatial, or time-series analysis based on collected data to clarify the current situation and challenges of a region.

[0072] "Means of identifying similar cases by referring to records of successful cases" refers to a method or apparatus for searching a database of past successful projects to find past cases that are similar to the current situation.

[0073] "An algorithmic means for generating development plans" refers to a computational method or device for automatically creating an optimal redevelopment plan that takes into account the characteristics and challenges of the region.

[0074] "Means for simulating the economic effects of a plan" refers to methods or devices for predicting and evaluating the impact of a proposed development plan on the regional economy.

[0075] "Means for evaluating sustainability and optimizing environmental design" refers to methods or devices for evaluating the environmental impact of development plans and proposing optimal designs from a sustainable perspective.

[0076] A "user interface means" is a visual or manipulable means for a user to interact with a system, allowing them to confirm and select plans.

[0077] "Means for summarizing using natural language processing" refers to a method or apparatus for summarizing collected text information using natural language processing techniques and extracting key points.

[0078] "Means of receiving user feedback" refers to methods or devices for collecting and analyzing feedback and opinions that users have given to the system.

[0079] This invention will now be described in terms of embodiments for carrying it out. The purpose of this system is to efficiently generate and evaluate redevelopment plans tailored to regional characteristics. The system consists of a server, terminals, and users, each playing a different role.

[0080] The server functions as the central processing unit of this system. Using open databases and real-time sensors to collect local information, the server acquires population data, traffic patterns, geographical information, infrastructure status, and environmental data. It applies analytical algorithms to the collected data to accurately understand the current state of the region and identify challenges. Next, it refers to success stories to find similar cases and uses generative AI models to automatically generate redevelopment plans suitable for the region. The server enables efficient data processing by executing these processes on a scalable cloud platform.

[0081] To evaluate the economic impact of the generated plan, the server uses simulation software to predict its effect on the local economy. It also conducts sustainability assessments to optimize the environmental design and proposes an optimal design incorporating the results. The generated plan is then restructured using visualization software in a human-readable format.

[0082] As a concrete example, when considering a redevelopment plan for a sparsely populated suburban area, a prompt such as "Generate a plan to optimize the placement of commercial facilities in the sparsely populated area" is entered into the AI ​​model. Based on this prompt, the server can generate an optimized facility placement plan.

[0083] The terminal's role is to present the redevelopment plan provided by the server to the user. The terminal provides an intuitive user interface, supporting the user in reviewing and evaluating each element of the redevelopment plan. User feedback is sent to the server via the terminal and used to improve the plan.

[0084] In this way, this system makes the most of regional characteristics and enables effective redevelopment that balances sustainability and economic revitalization.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server collects local information. Specifically, it obtains population data, traffic patterns, geographical information, infrastructure status, and environmental data from open databases and real-time sensors using API communication. In this process, the collected data is converted into a structured format and stored in the database. Query conditions are provided as input, and local characteristic data is obtained as output.

[0088] Step 2:

[0089] The server analyzes the collected data to understand the current situation and challenges of the region. Specifically, it performs data cleaning to remove missing and outlier values. Next, it uses a statistical analysis module to analyze demographics and traffic patterns. In this step, regional characteristic data is used as input, and a report on the current situation of the region is generated as output.

[0090] Step 3:

[0091] The server identifies similar projects by referring to success stories. Specifically, it accesses a database of success stories, analyzes the text using natural language processing, and extracts similarities. At this stage, the input is the aforementioned status report, and the output is the identification of similar cases.

[0092] Step 4:

[0093] The server generates redevelopment plans using a generative AI model. Specifically, it takes prompts such as "Generate a plan to optimize the placement of commercial facilities in sparsely populated areas," and the AI ​​creates plans for the placement of commercial facilities and the expansion of public facilities. The input is a prompt, and the output is a detailed redevelopment plan.

[0094] Step 5:

[0095] The terminal presents the generated redevelopment plan to the user. Specifically, it uses visualization tools to display the plan as an interactive map and graph, allowing the user to click on each element to view detailed information. The input is the redevelopment plan, and the output is the visualization data available to the user.

[0096] Step 6:

[0097] Users evaluate the presented plan and provide feedback. Specifically, they input their opinions and suggestions regarding specific elements of the plan in text format into their terminal and send them to the server. The input is the user's feedback on the plan, and the output is the collection of this feedback information on the server.

[0098] Step 7:

[0099] The server reuses the generated AI model based on the collected feedback to improve the plan. Specifically, it considers user opinions, updates the generated plan, and re-runs economic effect simulations and sustainability evaluations. The input is user feedback and the initial plan, and the output is an improved redevelopment plan.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] In modern redevelopment planning, creating effective plans that take into account local characteristics is challenging. In particular, elements such as the placement of commercial facilities and the expansion of public transportation require sustainable implementation with the participation of local residents. Therefore, it is necessary to construct plans in a more concrete and visually understandable way, and to effectively incorporate feedback from residents.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for collecting information about a region, means for analyzing the collected information, and means for referring to a database of reference cases to identify similar cases. This makes it possible to visualize the generated redevelopment plan based on regional characteristics using augmented reality technology, allowing residents to easily understand it and provide feedback.

[0105] "Regional information" refers to various types of data related to a specific region, such as geographical information, demographics, traffic patterns, infrastructure status, and environmental conditions.

[0106] "Means of analyzing collected information" refers to methods and technologies for analyzing collected regional data to conduct current situation assessments and identify issues necessary for redevelopment.

[0107] A "reference case database" refers to a collection of information that accumulates past redevelopment projects and successful urban planning examples, allowing users to search and refer to similar cases.

[0108] A "generative algorithm" refers to a set of computational procedures used to process data based on a specific purpose and automatically generate new plans or proposals.

[0109] "Means for simulating the economic effects of generated plans" refers to models and technologies for predicting and evaluating the impact of redevelopment plans on the local economy.

[0110] "Means of evaluating sustainability and optimizing environmental design" refers to technologies that design and adjust for long-term sustainable development while considering environmental impact.

[0111] "Means of visualization using augmented reality technology" refers to technologies that overlay digital plans onto the real world, making them easier for users to understand visually.

[0112] "Scenario analysis for predicting long-term future impacts" refers to a method of quantitatively or qualitatively evaluating the future impact of a redevelopment plan based on multiple future hypotheses.

[0113] This invention is a system for generating redevelopment plans suited to local characteristics and presenting them to residents. The specific forms of its implementation are described below.

[0114] First, the server acquires and stores information about the region from open databases and real-time sensors. This includes geographical information, demographic data, traffic patterns, infrastructure status, and environmental conditions. The server uses analytical algorithms to analyze this data and identify the current state of the region and its challenges.

[0115] Based on the analysis results, the server refers to a database of reference cases to identify similar redevelopment projects. Then, it uses a generative AI model to generate specific redevelopment plans tailored to the characteristics of the region. The plan generation process includes considering factors from past successful projects and simulating the economic effects of the generated plans to verify their impact on the local economy.

[0116] The generated plans are evaluated for sustainability and optimized from an environmental design perspective. Furthermore, augmented reality technology is used to allow users to visually review the plans on their devices. Specifically, visualization is possible by overlaying the plan content onto real-world scenery via devices such as smartphones.

[0117] Users can review the presented plan via their device and provide feedback. The server collects this feedback and uses the generation AI again to improve the plan. This feedback loop results in a more feasible plan.

[0118] For example, in a sparsely populated area, the AI ​​can propose the optimal placement of commercial facilities and, based on that, provide a plan for regional revitalization, such as attracting new residents and visitors. An example of a prompt for the generating AI model is: "Based on regional data, consider the impact of commercial facilities and transportation infrastructure on the regional economy, and identify and propose the optimal factors for generating a new redevelopment plan."

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The server acquires regional information from open databases and real-time sensors. Input information includes geographical data, demographic data, and traffic patterns. This data is stored in a database and prepared for analysis. The output is an analyzable regional dataset.

[0122] Step 2:

[0123] The server analyzes the accumulated regional datasets using analytical algorithms. Based on input data such as population dynamics and traffic patterns, it assesses the current state of the region and identifies specific challenges. This process involves data classification and cluster analysis, generating a list of regional characteristics and problems as output.

[0124] Step 3:

[0125] Based on these analysis results, the server refers to a database of reference cases to identify similar redevelopment projects. The input is the regional characteristics and problems obtained in step 2, and based on this, it searches for past successful cases. The output is a list of relevant cases and their details.

[0126] Step 4:

[0127] The server uses a generative AI model to generate redevelopment plans tailored to regional characteristics. The input data consists of regional characteristics and factors from similar cases obtained in steps 2 and 3. This results in the output of specific plans, including the placement of commercial facilities and public transportation.

[0128] Step 5:

[0129] The server simulates the economic effects of the generated redevelopment plan. The input is the plan obtained in step 4, and an economic model is applied to predict its impact on the regional economy. The output is a report of the economic effects as a result of the simulation.

[0130] Step 6:

[0131] The server evaluates the sustainability of the generated plan and optimizes the environmental design. Based on the redevelopment plan as input, it conducts an environmental impact assessment. The output is an optimized redevelopment plan that includes sustainable design elements.

[0132] Step 7:

[0133] The device visually presents the final generated redevelopment plan to the user using augmented reality technology. The input is the optimized plan from step 6, and the output is an AR display that the user can visually confirm.

[0134] Step 8:

[0135] Users review the redevelopment plan presented through their terminal and provide feedback. The input consists of user opinions and suggestions, while the output is data sent to the server as feedback.

[0136] Step 9:

[0137] The server analyzes the feedback received from the user and uses the generating AI again to improve the redevelopment plan. The input is the feedback obtained in step 8, and the output is the revised redevelopment plan.

[0138] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0139] This invention is a system that combines generative AI and an emotion engine to provide an optimal redevelopment plan based on regional characteristics. In its embodiment, the server, terminal, and user play the main roles and work together to process each step.

[0140] The server performs the central processing of this system. It collects vast amounts of data about the region and analyzes it using machine learning algorithms. This identifies the current situation and potential challenges in the region. Based on this analysis, it generates plans suitable for areas requiring redevelopment. The generating AI refers to a database of past success stories and presents the optimal strategy based on similar cases.

[0141] The generated plan is delivered to the user via a device. The device uses visualization tools to clearly display the plan's details and effects. During this process, an emotion engine analyzes the user's emotions based on facial recognition and voice, determining their satisfaction level and areas of dissatisfaction. This emotion data is sent to a server and used to improve the plan.

[0142] Users evaluate redevelopment plans via their devices and provide feedback, while emotional information analyzed by the emotion engine is incorporated into plan adjustments. This results in plans tailored to the individual needs of each user. For example, if a user expresses positive emotions towards a plan, the detailed design proceeds based on that plan; conversely, if negative emotions are expressed, the plan is reconsidered.

[0143] Furthermore, the server utilizes an emotion engine to incorporate users' emotional responses into scenario analysis, enabling more precise and effective long-term regional development strategies. The results of economic impact and environmental assessments obtained from simulations, along with user emotional information, are integrated to present the final strategy.

[0144] These elements enable the creation of sustainable redevelopment plans optimized for local characteristics and residents' needs, bringing new value and vitality to the community. For example, in urban redevelopment, an emotional engine can analyze residents' satisfaction in real time and reflect this in the placement of public spaces and the selection of facilities, thereby creating a more comfortable and inviting community environment.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The server collects regional data from various sources. Specifically, it obtains demographic data, traffic data, environmental monitoring data, and other information from open databases on the internet, and collects sensor information in real time. This data provides the basis for analyzing the current state of the region.

[0148] Step 2:

[0149] The server analyzes the collected data. Using machine learning algorithms and data analysis tools, it identifies local issues and pinpoints the needs of residents and areas requiring improvement. It also refers to a database of past success stories to identify success factors in similar regions.

[0150] Step 3:

[0151] The server uses AI generation to create redevelopment plans. Based on regional characteristics and successful case studies, it automatically generates optimal facility placement and infrastructure improvement plans, and compares the features of each plan.

[0152] Step 4:

[0153] The terminal presents the generated redevelopment plan to the user. The user interface visually displays the plan's contents in an easy-to-understand manner, allowing the user to review the details.

[0154] Step 5:

[0155] The emotion engine analyzes the user's emotions. While the user is viewing the presented plan, it uses the camera and voice input to evaluate the user's emotional state in real time and identify areas of satisfaction and dissatisfaction.

[0156] Step 6:

[0157] Users provide feedback on the redevelopment plan. They input comments and ratings through their devices, and this feedback, along with sentiment information obtained by the sentiment engine, is sent to the server.

[0158] Step 7:

[0159] The server modifies the plan based on user feedback and sentiment information. It restarts the generation AI and adjusts the plan to incorporate the improvements pointed out in the feedback.

[0160] Step 8:

[0161] The server performs simulations of the economic effects. Using economic models, it predicts the impact of the revised plan on the local economy and assesses its sustainability.

[0162] Step 9:

[0163] The server conducts scenario analysis to predict long-term future impacts. It considers multiple scenarios, including user response data obtained from the emotion engine, to formulate the optimal strategy.

[0164] Step 10:

[0165] Users select the final plan and determine the regional redevelopment strategy. Based on detailed data and strategic options presented by the server, they can choose the most suitable plan.

[0166] (Example 2)

[0167] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0168] In modern society, regional redevelopment plans are required to accurately reflect local characteristics and the needs of residents. However, traditional methods make it difficult to select highly relevant data from a vast amount of information and create plans that take residents' feelings into consideration. As a result, there is a challenge in proposing sustainable redevelopment plans that are tailored to local characteristics and the needs of residents.

[0169] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0170] In this invention, the server includes means for collecting local information, means for analyzing the collected information, and means for generating an AI model. This makes it possible to generate an optimal redevelopment plan based on local characteristics and to propose a plan that reflects the user's feelings.

[0171] "Means of collecting local information" refers to methods of gathering geographical, demographic, and economic information about a specific region.

[0172] "Means of analyzing collected information" refers to methods of using machine learning algorithms to analyze collected data and identify the current situation and potential challenges in the region.

[0173] "Means of identifying similar cases by referring to information on successful cases" refers to methods for identifying similar successful cases from a database of past redevelopment projects.

[0174] "Generative AI modeling" refers to artificial intelligence techniques used to automatically generate new redevelopment plans by learning from past cases.

[0175] "Means for simulating economic outcomes" refers to methods for calculating the expected economic impacts if a redevelopment plan is implemented.

[0176] "Means for assessing sustainability and optimizing environmental plans" refers to methods for analyzing the environmental impact of planned redevelopment and optimizing it in a sustainable manner.

[0177] A "scenario analysis method for predicting long-term future impacts" is a simulation method for predicting how a redevelopment plan will affect a region over time.

[0178] "An emotional engine that analyzes user emotional information and reflects it in redevelopment plans" refers to a method of analyzing user emotions and incorporating the results as feedback into redevelopment plans.

[0179] This invention is a system for generating optimal plans that reflect regional characteristics and the needs of residents in regional redevelopment. Specific embodiments are described below.

[0180] Server Role

[0181] The server handles the central processing of this system. First, it collects local information by using open databases and geographic information systems to obtain geographic, demographic, and economic data. This collected information is analyzed using machine learning libraries such as Python's Scikit-learn and TENSORFLOW®. Based on the analysis results, it refers to successful case studies and identifies similar cases. This prepares the generative AI model to generate the optimal redevelopment plan. The server also simulates the economic impact of the generated plan and performs calculations to assess its sustainability.

[0182] Terminal role

[0183] The terminal is responsible for visualizing and presenting the generated redevelopment plan to the user. A web application is used for visualization, allowing users to intuitively understand the plan through 3D models and infographics. During this process, an emotion engine built into the terminal analyzes the user's facial expressions and voice to collect emotional information. This information is sent to a server and used to improve the plan.

[0184] User roles

[0185] Users evaluate the redevelopment plan through their devices and provide feedback based on emotional information analyzed by an emotion engine. This user feedback is used to revise and improve the plan, resulting in a redevelopment plan that better suits individual needs.

[0186] As a concrete example, when a user expresses interest in urban redevelopment and proposes a layout for public spaces, the server references past successful examples and generates an optimal layout plan. The terminal visualizes this plan, allowing the user to review each element and provide feedback. This enables effective development that leverages the unique characteristics of the region.

[0187] Example of a prompt

[0188] "Please generate a sustainable redevelopment plan for Shibuya Ward in Tokyo. Include an analysis of local resident satisfaction and provide specific proposals for the placement of public spaces."

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The user accesses the system and enters information about the target area. This information includes the area's geographical characteristics, the purpose of redevelopment, and priority areas for improvement. The terminal sends this information to the server, which then receives the necessary initial data.

[0192] Step 2:

[0193] The server automatically collects relevant data from external databases and geographic information systems based on the received regional information. Using the input regional parameters as keys, it searches for geographical conditions, demographics, and economic indicators to construct a dataset. The resulting dataset is then generated.

[0194] Step 3:

[0195] The server uses machine learning algorithms to analyze the collected dataset. This step utilizes Python's Scikit-learn and TensorFlow libraries to identify regional challenges and potential development opportunities. The analysis results output a list of regional challenges and development opportunities.

[0196] Step 4:

[0197] The server references successful case studies and selects cases similar to the analysis results. This identifies strategies and elements that have a high success rate in redevelopment. In this step, past project data is filtered and relevant data is extracted.

[0198] Step 5:

[0199] A generative AI model is used to combine identified challenges with success stories to generate an optimal redevelopment plan. Inputs include feedback data from success stories and on-site data. The AI ​​model integrates these to output a predictive plan.

[0200] Step 6:

[0201] The terminal visualizes the generated redevelopment plan and presents it to the user as a 3D model or infographic. The output plan is accessible through the user interface, allowing the user to interactively review each part of the plan.

[0202] Step 7:

[0203] The device uses sensors to detect the user's facial expressions and voice, and its built-in emotion engine analyzes the user's emotions. The input includes emotion data acquired in real time. The analysis outputs an emotional evaluation of the user's response to the plan.

[0204] Step 8:

[0205] The server receives the user's sentiment evaluation and feedback, and uses the generative AI model again to improve the plan. The improved plan reflects the user's favorable feedback and corrects negative feedback. This results in the output of the final redevelopment plan, which is then presented to the user.

[0206] (Application Example 2)

[0207] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0208] Modern regional redevelopment projects require plans that fully reflect local characteristics and the needs of residents. However, traditional methods have made it difficult to collect and analyze detailed local information, and emotional feedback from residents has often not been quickly integrated. As a result, development plans often fail to meet residents' expectations, hindering the sustainable development of the region.

[0209] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0210] In this invention, the server includes means for collecting local information, means for identifying similar cases by referring to information on successful cases, and means for collecting emotional feedback from users using an emotion analysis engine. This enables the generation of redevelopment plans based on local characteristics and the reflection of resident feedback in real time.

[0211] "Means of collecting local information" refers to methods for collecting geographical, social, and economic data related to a specific region.

[0212] "Means of analysis" refers to methods of processing collected data using computer algorithms to clarify the current situation and problems of a region.

[0213] "Methods for identifying similar cases by referring to success stories" refers to methods for exploring past successful development cases and finding similar cases that can be applied to the current situation.

[0214] "Generative AI means" refers to a function that uses machine learning and artificial intelligence technologies to automatically generate redevelopment plans based on regional characteristics.

[0215] "Means for simulating economic impacts" refers to methods for predicting the economic outcomes of a planned redevelopment project.

[0216] "Methods for evaluating sustainability and optimizing environmental design" refers to methods for evaluating the environmental impact of redevelopment plans and optimizing environmental design to achieve a sustainable society.

[0217] "Scenario analysis methods for predicting long-term future impacts" are methods for comprehensively predicting the impact of redevelopment plans on a region in the future.

[0218] "A means of visually providing regional development plans through AR display" refers to a method of using augmented reality technology to visually present plans to residents and aid their understanding.

[0219] "Methods for collecting emotional feedback from users using an emotion analysis engine" refers to methods for analyzing emotions from users' facial expressions and voices and collecting that feedback.

[0220] This invention is a system that generates regional redevelopment plans for smart cities and incorporates resident feedback in real time. Its main components include a server, terminals, and users.

[0221] The server handles the central processing of this system. The server runs programs on high-performance computers to collect and analyze vast amounts of information about the region. These programs use machine learning algorithms and artificial intelligence models to identify areas requiring redevelopment from the local data. This enables data-driven, scientific, and logical decision-making. Based on the analysis results, a generative AI system references a database of past success stories to generate an optimal redevelopment plan tailored to the specific characteristics of the region.

[0222] The terminal provides an interface for users to review the plan and provide feedback. This terminal is envisioned to be a mobile information device such as a smartphone or tablet. Users can visually review the redevelopment plan transmitted from the server on the terminal. Augmented reality (AR) functionality is used to overlay the plan onto the actual landscape, deepening residents' understanding. Emotional feedback is collected from the user's facial expressions and voice using an emotion analysis engine and transmitted to the server.

[0223] Users are the ultimate beneficiaries of this system, evaluating the generated plans and providing feedback. Feedback based on user sentiment data is re-analyzed by the server and used to optimize the plans. This ensures that the plans are flexible and responsive to the needs and feelings of the residents.

[0224] As a concrete example, in urban redevelopment, users can view augmented reality (AR) simulations of new park designs via their smartphones, and their facial expressions can be seen, allowing for real-time evaluation of their level of satisfaction and their positive feelings towards the environment. Based on this information, plans can be revised and new ideas incorporated, resulting in community development that achieves high levels of resident satisfaction.

[0225] Examples of prompt statements for manipulating a generative AI model include:

[0226] "Regarding the park development plan for this area, please propose the optimal layout and methods for promoting community activities, drawing on past successful examples."

[0227] This prompt allows the generating AI to output specific and actionable ideas, thereby improving the quality of the plan.

[0228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0229] Step 1:

[0230] The server collects information relevant to a region. It takes geographic data, demographic data, economic data, etc., as input and stores them in a database. Based on this information, it constructs a dataset to evaluate the current situation and challenges of the region.

[0231] Step 2:

[0232] The server analyzes the collected data. It uses the dataset constructed in Step 1 as input. Machine learning algorithms and statistical analysis techniques are applied to identify regional characteristics and potential challenges. The output consists of numerical data and graphs illustrating regional characteristics.

[0233] Step 3:

[0234] The server references successful case studies to identify similar cases. It uses analyzed regional characteristics data as input. It searches the database for similar past cases and provides reference information for formulating optimal redevelopment plans. The output is a list of selected successful cases.

[0235] Step 4:

[0236] The server generates a redevelopment plan using a generative AI method. Analysis results and a list of successful cases are input to the generative AI model along with prompts. Based on this information, the AI ​​model outputs an optimal development plan tailored to the specific characteristics of the region.

[0237] Step 5:

[0238] The terminal visualizes the generated redevelopment plan. It receives development plan data from a server as input. Using augmented reality (AR) technology, it overlays the plan onto the real-world landscape for visual display. The output is a 3D model on the terminal screen.

[0239] Step 6:

[0240] Users evaluate the plan and provide feedback through their device. The input is a visualized plan displayed on the device. An emotion analysis engine, using facial expressions and voice, analyzes the user's reactions and sends emotion feedback data to the server. The output is emotion feedback information indicating satisfaction levels and areas for improvement.

[0241] Step 7:

[0242] The server optimizes the redevelopment plan based on user feedback. It uses the sentiment feedback data collected in step 6 as input. The feedback is incorporated into the existing plan data, the plan is modified as needed, and the final redevelopment plan is generated. The output is the optimized redevelopment plan.

[0243] 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.

[0244] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search)<url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0245] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0250] 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.

[0251] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0252] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0253] 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.

[0254] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0255] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0256] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0257] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0258] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0259] This invention is a system that utilizes generative AI to generate and evaluate redevelopment plans tailored to regional characteristics, and in order to implement this, the server, terminal, and user each play a specific role.

[0260] The server functions as the central processing unit of this system. The server collects various data about the target area. Specifically, it obtains local population data, traffic patterns, geographic information, infrastructure status, and environmental data from open databases and real-time sensors. Based on this data, the server applies analytical algorithms to understand the current situation in the area and identify challenges.

[0261] Based on the analysis results, the server accesses a database of successful case studies from around the world to identify similar redevelopment projects. Considering the factors extracted from these success stories, the server uses generative AI to create a redevelopment plan tailored to the specific area. This plan includes concrete suggestions for revitalizing the area, such as the placement of commercial facilities and the expansion of public transportation.

[0262] The created redevelopment plan is presented to the user via a terminal. The terminal displays the plan in a visually easy-to-understand manner and supports the user in evaluating the plan and reviewing the proposed content. The user can send feedback on the proposal to the server via the terminal.

[0263] Based on the collected feedback, the server uses generative AI to further improve the plan. Furthermore, it simulates the economic effects of the plan and evaluates its impact on the local economy. To ensure sustainability, the server evaluates it from an environmental design perspective and proposes an optimized design.

[0264] Users can ultimately select the plan that best matches the characteristics of their area based on the multiple plans presented. This minimizes uncertainties during the planning stage, resulting in a more feasible and sustainable redevelopment plan.

[0265] This invention makes it possible to effectively implement redevelopment tailored to regional characteristics and bring new value to local communities. For example, in sparsely populated suburban areas, AI can propose the optimal placement of commercial facilities and provide plans to attract new residents and visitors, thereby promoting regional revitalization.

[0266] The following describes the processing flow.

[0267] Step 1:

[0268] The server collects data for the target area. It obtains demographic data, traffic volume data, environmental data, etc. from open databases, and collects traffic patterns and air quality data from real-time sensors. This collects basic information to understand the current situation in the area.

[0269] Step 2:

[0270] The server analyzes the collected data. It applies machine learning algorithms to perform data analysis to identify local and potential issues. For example, it extracts trends such as population decline and traffic congestion times.

[0271] Step 3:

[0272] The server accesses a global database of successful case studies. Based on regional characteristics, it searches for similar redevelopment projects and identifies success factors. Natural language processing techniques are used to generate summaries of relevant case studies.

[0273] Step 4:

[0274] The server generates redevelopment plans using AI. Based on the analysis results and successful case studies, it creates specific plans and proposes the optimal placement of commercial facilities, public facilities, and residential areas.

[0275] Step 5:

[0276] The device displays the generated plan to the user. Visualization tools are used to clearly present the plan's contents and help the user review the details. The user can evaluate the plan and provide feedback.

[0277] Step 6:

[0278] The server receives feedback from users and modifies the plan based on that information. The generative AI model, which incorporates the feedback, improves the accuracy of the plan.

[0279] Step 7:

[0280] The server simulates the economic effects of the revised plan. Using an economic model, data is generated to predict the potential for job creation, tax revenue increase, and regional economic activation.

[0281] Step 8:

[0282] The server optimizes the environmental design to evaluate sustainability. It evaluates resource consumption and energy efficiency and proposes an optimal environmental design.

[0283] Step 9:

[0284] The server predicts long-term future impacts based on multiple scenarios. Using scenario analysis, it evaluates the future impacts of regional redevelopment and presents an optimal strategy.

[0285] Step 10:

[0286] The user determines the regional redevelopment strategy based on the final plan. Based on the presented data and scenario information, the user selects a sustainable plan that suits the regional characteristics.

[0287] (Example 1)

[0288] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In modern regional development, there is a problem that a uniform development plan ignoring regional characteristics is in progress, and the sustainability and economic activation of the region are insufficient. Also, in previous development plans, there has often been no efficient utilization of the unique data of the region and no planning considering future impacts. As a result, it is difficult to respond to unexpected environmental changes and to meet the needs of residents.

[0290] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0291] In this invention, the server includes means for collecting local information, means for analyzing the collected information, and means for referring to records of successful cases and identifying similar cases. This enables the generation of redevelopment plans closely tailored to local characteristics, facilitating sustainable development and economic revitalization. Furthermore, by revising the plan based on user feedback, development that can flexibly respond to the needs of residents is realized.

[0292] "Means for collecting local information" refers to methods or devices for efficiently collecting diverse information related to a given region, such as population, transportation, geography, infrastructure, and environment.

[0293] "Means for analyzing collected information" refers to methods or devices for conducting statistical, spatial, or time-series analysis based on collected data to clarify the current situation and challenges of a region.

[0294] "Means of identifying similar cases by referring to records of successful cases" refers to a method or apparatus for searching a database of past successful projects to find past cases that are similar to the current situation.

[0295] "An algorithmic means for generating development plans" refers to a computational method or device for automatically creating an optimal redevelopment plan that takes into account the characteristics and challenges of the region.

[0296] "Means for simulating the economic effects of a plan" refers to methods or devices for predicting and evaluating the impact of a proposed development plan on the regional economy.

[0297] "Means for evaluating sustainability and optimizing environmental design" refers to methods or devices for evaluating the environmental impact of development plans and proposing optimal designs from a sustainable perspective.

[0298] A "user interface means" is a visual or manipulable means for a user to interact with a system, allowing them to confirm and select plans.

[0299] "Means for summarizing using natural language processing" refers to a method or apparatus for summarizing collected text information using natural language processing techniques and extracting key points.

[0300] "Means of receiving user feedback" refers to methods or devices for collecting and analyzing feedback and opinions that users have given to the system.

[0301] This invention will now be described in terms of embodiments for carrying it out. The purpose of this system is to efficiently generate and evaluate redevelopment plans tailored to regional characteristics. The system consists of a server, terminals, and users, each playing a different role.

[0302] The server functions as the central processing unit of this system. Using open databases and real-time sensors to collect local information, the server acquires population data, traffic patterns, geographical information, infrastructure status, and environmental data. It applies analytical algorithms to the collected data to accurately understand the current state of the region and identify challenges. Next, it refers to success stories to find similar cases and uses generative AI models to automatically generate redevelopment plans suitable for the region. The server enables efficient data processing by executing these processes on a scalable cloud platform.

[0303] To evaluate the economic impact of the generated plan, the server uses simulation software to predict its effect on the local economy. It also conducts sustainability assessments to optimize the environmental design and proposes an optimal design incorporating the results. The generated plan is then restructured using visualization software in a human-readable format.

[0304] As a specific example, in the scenario of considering a redevelopment plan for a depopulated area in the suburbs, a prompt sentence such as "Please generate a plan for optimizing the placement of commercial facilities in the depopulated area" is input into the generative AI model. Based on this prompt, the server can generate an optimized facility placement plan.

[0305] The terminal has the role of presenting the redevelopment plan provided by the server to the user. The terminal provides an intuitive user interface and supports the user in checking and evaluating detailed information about each element of the redevelopment plan. Feedback from the user is sent to the server through the terminal and utilized for improving the plan.

[0306] In this way, this system can effectively implement redevelopment that maximally utilizes regional characteristics and achieves both sustainability and economic activation.

[0307] The flow of the specific process in Example 1 will be described using FIG. 11.

[0308] Step 1:

[0309] The server collects information about the region. Specifically, population data, traffic patterns, geographical information, infrastructure status, and environmental data are obtained from open databases and real-time sensors using API communication. In this process, the collected data is converted into a structured format and stored in a database. Query conditions are given as input, and regional characteristic data is obtained as output.

[0310] Step 2:

[0311] The server analyzes the collected data to understand the current situation and issues of the region. Specifically, data cleaning is performed to remove missing values and outliers. Next, statistical analysis modules analyze population dynamics and traffic patterns. In this step, regional characteristic data is used as input, and a report on the current situation of the region is generated as output. [[ID=�0]]

[0312] Step 3:

[0313] The server identifies similar projects by referring to success stories. Specifically, it accesses a database of success stories, analyzes the text using natural language processing, and extracts similarities. At this stage, the input is the aforementioned status report, and the output is the identification of similar cases.

[0314] Step 4:

[0315] The server generates redevelopment plans using a generative AI model. Specifically, it takes prompts such as "Generate a plan to optimize the placement of commercial facilities in sparsely populated areas," and the AI ​​creates plans for the placement of commercial facilities and the expansion of public facilities. The input is a prompt, and the output is a detailed redevelopment plan.

[0316] Step 5:

[0317] The terminal presents the generated redevelopment plan to the user. Specifically, it uses visualization tools to display the plan as an interactive map and graph, allowing the user to click on each element to view detailed information. The input is the redevelopment plan, and the output is the visualization data available to the user.

[0318] Step 6:

[0319] Users evaluate the presented plan and provide feedback. Specifically, they input their opinions and suggestions regarding specific elements of the plan in text format into their terminal and send them to the server. The input is the user's feedback on the plan, and the output is the collection of this feedback information on the server.

[0320] Step 7:

[0321] The server reuses the generated AI model based on the collected feedback to improve the plan. Specifically, it considers user opinions, updates the generated plan, and re-runs economic effect simulations and sustainability evaluations. The input is user feedback and the initial plan, and the output is an improved redevelopment plan.

[0322] (Application Example 1)

[0323] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0324] In modern redevelopment planning, creating effective plans that take into account local characteristics is challenging. In particular, elements such as the placement of commercial facilities and the expansion of public transportation require sustainable implementation with the participation of local residents. Therefore, it is necessary to construct plans in a more concrete and visually understandable way, and to effectively incorporate feedback from residents.

[0325] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0326] In this invention, the server includes means for collecting information about a region, means for analyzing the collected information, and means for referring to a database of reference cases to identify similar cases. This makes it possible to visualize the generated redevelopment plan based on regional characteristics using augmented reality technology, allowing residents to easily understand it and provide feedback.

[0327] "Regional information" refers to various types of data related to a specific region, such as geographical information, demographics, traffic patterns, infrastructure status, and environmental conditions.

[0328] "Means of analyzing collected information" refers to methods and technologies for analyzing collected regional data to conduct current situation assessments and identify issues necessary for redevelopment.

[0329] A "reference case database" refers to a collection of information that accumulates past redevelopment projects and successful urban planning examples, allowing users to search and refer to similar cases.

[0330] A "generative algorithm" refers to a set of computational procedures used to process data based on a specific purpose and automatically generate new plans or proposals.

[0331] "Means for simulating the economic effects of generated plans" refers to models and technologies for predicting and evaluating the impact of redevelopment plans on the local economy.

[0332] "Means of evaluating sustainability and optimizing environmental design" refers to technologies that design and adjust for long-term sustainable development while considering environmental impact.

[0333] "Means of visualization using augmented reality technology" refers to technologies that overlay digital plans onto the real world, making them easier for users to understand visually.

[0334] "Scenario analysis for predicting long-term future impacts" refers to a method of quantitatively or qualitatively evaluating the future impact of a redevelopment plan based on multiple future hypotheses.

[0335] This invention is a system for generating redevelopment plans suited to local characteristics and presenting them to residents. The specific forms of its implementation are described below.

[0336] First, the server acquires and stores information about the region from open databases and real-time sensors. This includes geographical information, demographic data, traffic patterns, infrastructure status, and environmental conditions. The server uses analytical algorithms to analyze this data and identify the current state of the region and its challenges.

[0337] Based on the analysis results, the server refers to a database of reference cases to identify similar redevelopment projects. Then, it uses a generative AI model to generate specific redevelopment plans tailored to the characteristics of the region. The plan generation process includes considering factors from past successful projects and simulating the economic effects of the generated plans to verify their impact on the local economy.

[0338] The generated plans are evaluated for sustainability and optimized from an environmental design perspective. Furthermore, augmented reality technology is used to allow users to visually review the plans on their devices. Specifically, visualization is possible by overlaying the plan content onto real-world scenery via devices such as smartphones.

[0339] Users can review the presented plan via their device and provide feedback. The server collects this feedback and uses the generation AI again to improve the plan. This feedback loop results in a more feasible plan.

[0340] For example, in a sparsely populated area, the AI ​​can propose the optimal placement of commercial facilities and, based on that, provide a plan for regional revitalization, such as attracting new residents and visitors. An example of a prompt for the generating AI model is: "Based on regional data, consider the impact of commercial facilities and transportation infrastructure on the regional economy, and identify and propose the optimal factors for generating a new redevelopment plan."

[0341] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0342] Step 1:

[0343] The server acquires regional information from open databases and real-time sensors. Input information includes geographical data, demographic data, and traffic patterns. This data is stored in a database and prepared for analysis. The output is an analyzable regional dataset.

[0344] Step 2:

[0345] The server analyzes the accumulated regional datasets using analytical algorithms. Based on input data such as population dynamics and traffic patterns, it assesses the current state of the region and identifies specific challenges. This process involves data classification and cluster analysis, generating a list of regional characteristics and problems as output.

[0346] Step 3:

[0347] Based on these analysis results, the server refers to a database of reference cases to identify similar redevelopment projects. The input is the regional characteristics and problems obtained in step 2, and based on this, it searches for past successful cases. The output is a list of relevant cases and their details.

[0348] Step 4:

[0349] The server uses a generative AI model to generate redevelopment plans tailored to regional characteristics. The input data consists of regional characteristics and factors from similar cases obtained in steps 2 and 3. This results in the output of specific plans, including the placement of commercial facilities and public transportation.

[0350] Step 5:

[0351] The server simulates the economic effects of the generated redevelopment plan. The input is the plan obtained in step 4, and an economic model is applied to predict its impact on the regional economy. The output is a report of the economic effects as a result of the simulation.

[0352] Step 6:

[0353] The server evaluates the sustainability of the generated plan and optimizes the environmental design. Based on the redevelopment plan as input, it conducts an environmental impact assessment. The output is an optimized redevelopment plan that includes sustainable design elements.

[0354] Step 7:

[0355] The device visually presents the final generated redevelopment plan to the user using augmented reality technology. The input is the optimized plan from step 6, and the output is an AR display that the user can visually confirm.

[0356] Step 8:

[0357] Users review the redevelopment plan presented through their terminal and provide feedback. The input consists of user opinions and suggestions, while the output is data sent to the server as feedback.

[0358] Step 9:

[0359] The server analyzes the feedback received from the user and uses the generating AI again to improve the redevelopment plan. The input is the feedback obtained in step 8, and the output is the revised redevelopment plan.

[0360] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0361] This invention is a system that combines generative AI and an emotion engine to provide an optimal redevelopment plan based on regional characteristics. In its embodiment, the server, terminal, and user play the main roles and work together to process each step.

[0362] The server performs the central processing of this system. It collects vast amounts of data about the region and analyzes it using machine learning algorithms. This identifies the current situation and potential challenges in the region. Based on this analysis, it generates plans suitable for areas requiring redevelopment. The generating AI refers to a database of past success stories and presents the optimal strategy based on similar cases.

[0363] The generated plan is delivered to the user via a device. The device uses visualization tools to clearly display the plan's details and effects. During this process, an emotion engine analyzes the user's emotions based on facial recognition and voice, determining their satisfaction level and areas of dissatisfaction. This emotion data is sent to a server and used to improve the plan.

[0364] Users evaluate redevelopment plans via their devices and provide feedback, while emotional information analyzed by the emotion engine is incorporated into plan adjustments. This results in plans tailored to the individual needs of each user. For example, if a user expresses positive emotions towards a plan, the detailed design proceeds based on that plan; conversely, if negative emotions are expressed, the plan is reconsidered.

[0365] Furthermore, the server utilizes an emotion engine to incorporate users' emotional responses into scenario analysis, enabling more precise and effective long-term regional development strategies. The results of economic impact and environmental assessments obtained from simulations, along with user emotional information, are integrated to present the final strategy.

[0366] These elements enable the creation of sustainable redevelopment plans optimized for local characteristics and residents' needs, bringing new value and vitality to the community. For example, in urban redevelopment, an emotional engine can analyze residents' satisfaction in real time and reflect this in the placement of public spaces and the selection of facilities, thereby creating a more comfortable and inviting community environment.

[0367] The following describes the processing flow.

[0368] Step 1:

[0369] The server collects regional data from various sources. Specifically, it obtains demographic data, traffic data, environmental monitoring data, and other information from open databases on the internet, and collects sensor information in real time. This data provides the basis for analyzing the current state of the region.

[0370] Step 2:

[0371] The server analyzes the collected data. Using machine learning algorithms and data analysis tools, it identifies local issues and pinpoints the needs of residents and areas requiring improvement. It also refers to a database of past success stories to identify success factors in similar regions.

[0372] Step 3:

[0373] The server uses AI generation to create redevelopment plans. Based on regional characteristics and successful case studies, it automatically generates optimal facility placement and infrastructure improvement plans, and compares the features of each plan.

[0374] Step 4:

[0375] The terminal presents the generated redevelopment plan to the user. The user interface visually displays the plan's contents in an easy-to-understand manner, allowing the user to review the details.

[0376] Step 5:

[0377] The emotion engine analyzes the user's emotions. While the user is viewing the presented plan, it uses the camera and voice input to evaluate the user's emotional state in real time and identify areas of satisfaction and dissatisfaction.

[0378] Step 6:

[0379] Users provide feedback on the redevelopment plan. They input comments and ratings through their devices, and this feedback, along with sentiment information obtained by the sentiment engine, is sent to the server.

[0380] Step 7:

[0381] The server modifies the plan based on user feedback and sentiment information. It restarts the generation AI and adjusts the plan to incorporate the improvements pointed out in the feedback.

[0382] Step 8:

[0383] The server performs simulations of the economic effects. Using economic models, it predicts the impact of the revised plan on the local economy and assesses its sustainability.

[0384] Step 9:

[0385] The server conducts scenario analysis to predict long-term future impacts. It considers multiple scenarios, including user response data obtained from the emotion engine, to formulate the optimal strategy.

[0386] Step 10:

[0387] Users select the final plan and determine the regional redevelopment strategy. Based on detailed data and strategic options presented by the server, they can choose the most suitable plan.

[0388] (Example 2)

[0389] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0390] In modern society, regional redevelopment plans are required to accurately reflect local characteristics and the needs of residents. However, traditional methods make it difficult to select highly relevant data from a vast amount of information and create plans that take residents' feelings into consideration. As a result, there is a challenge in proposing sustainable redevelopment plans that are tailored to local characteristics and the needs of residents.

[0391] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0392] In this invention, the server includes means for collecting local information, means for analyzing the collected information, and means for generating an AI model. This makes it possible to generate an optimal redevelopment plan based on local characteristics and to propose a plan that reflects the user's feelings.

[0393] "Means of collecting local information" refers to methods of gathering geographical, demographic, and economic information about a specific region.

[0394] "Means of analyzing collected information" refers to methods of using machine learning algorithms to analyze collected data and identify the current situation and potential challenges in the region.

[0395] "Means of identifying similar cases by referring to information on successful cases" refers to methods for identifying similar successful cases from a database of past redevelopment projects.

[0396] "Generative AI modeling" refers to artificial intelligence techniques used to automatically generate new redevelopment plans by learning from past cases.

[0397] "Means for simulating economic outcomes" refers to methods for calculating the expected economic impacts if a redevelopment plan is implemented.

[0398] "Means for assessing sustainability and optimizing environmental plans" refers to methods for analyzing the environmental impact of planned redevelopment and optimizing it in a sustainable manner.

[0399] A "scenario analysis method for predicting long-term future impacts" is a simulation method for predicting how a redevelopment plan will affect a region over time.

[0400] "An emotional engine that analyzes user emotional information and reflects it in redevelopment plans" refers to a method of analyzing user emotions and incorporating the results as feedback into redevelopment plans.

[0401] This invention is a system for generating optimal plans that reflect regional characteristics and the needs of residents in regional redevelopment. Specific embodiments are described below.

[0402] Server Role

[0403] The server handles the central processing of this system. First, it collects local information by using open databases and geographic information systems to obtain geographic, demographic, and economic data. This collected information is analyzed using machine learning libraries such as Python's Scikit-learn and TensorFlow. Based on the analysis results, it refers to successful case studies and identifies similar cases. This prepares the generative AI model to generate the optimal redevelopment plan. The server also simulates the economic impact of the generated plan and performs calculations to assess its sustainability.

[0404] Terminal role

[0405] The terminal is responsible for visualizing and presenting the generated redevelopment plan to the user. A web application is used for visualization, allowing users to intuitively understand the plan through 3D models and infographics. During this process, an emotion engine built into the terminal analyzes the user's facial expressions and voice to collect emotional information. This information is sent to a server and used to improve the plan.

[0406] User roles

[0407] Users evaluate the redevelopment plan through their devices and provide feedback based on emotional information analyzed by an emotion engine. This user feedback is used to revise and improve the plan, resulting in a redevelopment plan that better suits individual needs.

[0408] As a concrete example, when a user expresses interest in urban redevelopment and proposes a layout for public spaces, the server references past successful examples and generates an optimal layout plan. The terminal visualizes this plan, allowing the user to review each element and provide feedback. This enables effective development that leverages the unique characteristics of the region.

[0409] Example of a prompt

[0410] "Please generate a sustainable redevelopment plan for Shibuya Ward in Tokyo. Include an analysis of local resident satisfaction and provide specific proposals for the placement of public spaces."

[0411] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0412] Step 1:

[0413] The user accesses the system and enters information about the target area. This information includes the area's geographical characteristics, the purpose of redevelopment, and priority areas for improvement. The terminal sends this information to the server, which then receives the necessary initial data.

[0414] Step 2:

[0415] The server automatically collects relevant data from external databases and geographic information systems based on the received regional information. Using the input regional parameters as keys, it searches for geographical conditions, demographics, and economic indicators to construct a dataset. The resulting dataset is then generated.

[0416] Step 3:

[0417] The server uses machine learning algorithms to analyze the collected dataset. This step utilizes Python's Scikit-learn and TensorFlow libraries to identify regional challenges and potential development opportunities. The analysis results output a list of regional challenges and development opportunities.

[0418] Step 4:

[0419] The server references successful case studies and selects cases similar to the analysis results. This identifies strategies and elements that have a high success rate in redevelopment. In this step, past project data is filtered and relevant data is extracted.

[0420] Step 5:

[0421] A generative AI model is used to combine identified challenges with success stories to generate an optimal redevelopment plan. Inputs include feedback data from success stories and on-site data. The AI ​​model integrates these to output a predictive plan.

[0422] Step 6:

[0423] The terminal visualizes the generated redevelopment plan and presents it to the user as a 3D model or infographic. The output plan is accessible through the user interface, allowing the user to interactively review each part of the plan.

[0424] Step 7:

[0425] The device uses sensors to detect the user's facial expressions and voice, and its built-in emotion engine analyzes the user's emotions. The input includes emotion data acquired in real time. The analysis outputs an emotional evaluation of the user's response to the plan.

[0426] Step 8:

[0427] The server receives the user's sentiment evaluation and feedback, and uses the generative AI model again to improve the plan. The improved plan reflects the user's favorable feedback and corrects negative feedback. This results in the output of the final redevelopment plan, which is then presented to the user.

[0428] (Application Example 2)

[0429] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0430] Modern regional redevelopment projects require plans that fully reflect local characteristics and the needs of residents. However, traditional methods have made it difficult to collect and analyze detailed local information, and emotional feedback from residents has often not been quickly integrated. As a result, development plans often fail to meet residents' expectations, hindering the sustainable development of the region.

[0431] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0432] In this invention, the server includes means for collecting local information, means for identifying similar cases by referring to information on successful cases, and means for collecting emotional feedback from users using an emotion analysis engine. This enables the generation of redevelopment plans based on local characteristics and the reflection of resident feedback in real time.

[0433] "Means of collecting local information" refers to methods for collecting geographical, social, and economic data related to a specific region.

[0434] "Means of analysis" refers to methods of processing collected data using computer algorithms to clarify the current situation and problems of a region.

[0435] "Methods for identifying similar cases by referring to success stories" refers to methods for exploring past successful development cases and finding similar cases that can be applied to the current situation.

[0436] "Generative AI means" refers to a function that uses machine learning and artificial intelligence technologies to automatically generate redevelopment plans based on regional characteristics.

[0437] "Means for simulating economic impacts" refers to methods for predicting the economic outcomes of a planned redevelopment project.

[0438] "Methods for evaluating sustainability and optimizing environmental design" refers to methods for evaluating the environmental impact of redevelopment plans and optimizing environmental design to achieve a sustainable society.

[0439] "Scenario analysis methods for predicting long-term future impacts" are methods for comprehensively predicting the impact of redevelopment plans on a region in the future.

[0440] "A means of visually providing regional development plans through AR display" refers to a method of using augmented reality technology to visually present plans to residents and aid their understanding.

[0441] "Methods for collecting emotional feedback from users using an emotion analysis engine" refers to methods for analyzing emotions from users' facial expressions and voices and collecting that feedback.

[0442] This invention is a system that generates regional redevelopment plans for smart cities and incorporates resident feedback in real time. Its main components include a server, terminals, and users.

[0443] The server handles the central processing of this system. The server runs programs on high-performance computers to collect and analyze vast amounts of information about the region. These programs use machine learning algorithms and artificial intelligence models to identify areas requiring redevelopment from the local data. This enables data-driven, scientific, and logical decision-making. Based on the analysis results, a generative AI system references a database of past success stories to generate an optimal redevelopment plan tailored to the specific characteristics of the region.

[0444] The terminal provides an interface for users to review the plan and provide feedback. This terminal is envisioned to be a mobile information device such as a smartphone or tablet. Users can visually review the redevelopment plan transmitted from the server on the terminal. Augmented reality (AR) functionality is used to overlay the plan onto the actual landscape, deepening residents' understanding. Emotional feedback is collected from the user's facial expressions and voice using an emotion analysis engine and transmitted to the server.

[0445] Users are the ultimate beneficiaries of this system, evaluating the generated plans and providing feedback. Feedback based on user sentiment data is re-analyzed by the server and used to optimize the plans. This ensures that the plans are flexible and responsive to the needs and feelings of the residents.

[0446] As a concrete example, in urban redevelopment, users can view augmented reality (AR) simulations of new park designs via their smartphones, and their facial expressions can be seen, allowing for real-time evaluation of their level of satisfaction and their positive feelings towards the environment. Based on this information, plans can be revised and new ideas incorporated, resulting in community development that achieves high levels of resident satisfaction.

[0447] Examples of prompt statements for manipulating a generative AI model include:

[0448] "Regarding the park development plan for this area, please propose the optimal layout and methods for promoting community activities, drawing on past successful examples."

[0449] This prompt allows the generating AI to output specific and actionable ideas, thereby improving the quality of the plan.

[0450] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0451] Step 1:

[0452] The server collects information relevant to a region. It takes geographic data, demographic data, economic data, etc., as input and stores them in a database. Based on this information, it constructs a dataset to evaluate the current situation and challenges of the region.

[0453] Step 2:

[0454] The server analyzes the collected data. It uses the dataset constructed in Step 1 as input. Machine learning algorithms and statistical analysis techniques are applied to identify regional characteristics and potential challenges. The output consists of numerical data and graphs illustrating regional characteristics.

[0455] Step 3:

[0456] The server references successful case studies to identify similar cases. It uses analyzed regional characteristics data as input. It searches the database for similar past cases and provides reference information for formulating optimal redevelopment plans. The output is a list of selected successful cases.

[0457] Step 4:

[0458] The server generates a redevelopment plan using a generative AI method. Analysis results and a list of successful cases are input to the generative AI model along with prompts. Based on this information, the AI ​​model outputs an optimal development plan tailored to the specific characteristics of the region.

[0459] Step 5:

[0460] The terminal visualizes the generated redevelopment plan. It receives development plan data from a server as input. Using augmented reality (AR) technology, it overlays the plan onto the real-world landscape for visual display. The output is a 3D model on the terminal screen.

[0461] Step 6:

[0462] Users evaluate the plan and provide feedback through their device. The input is a visualized plan displayed on the device. An emotion analysis engine, using facial expressions and voice, analyzes the user's reactions and sends emotion feedback data to the server. The output is emotion feedback information indicating satisfaction levels and areas for improvement.

[0463] Step 7:

[0464] The server optimizes the redevelopment plan based on user feedback. It uses the sentiment feedback data collected in step 6 as input. The feedback is incorporated into the existing plan data, the plan is modified as needed, and the final redevelopment plan is generated. The output is the optimized redevelopment plan.

[0465] 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.

[0466] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0467] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0468] [Third Embodiment]

[0469] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0470] 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.

[0471] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0472] 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.

[0473] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0474] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0475] 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.

[0476] 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.

[0477] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0478] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0479] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0480] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0481] This invention is a system that utilizes generative AI to generate and evaluate redevelopment plans tailored to regional characteristics, and in order to implement this, the server, terminal, and user each play a specific role.

[0482] The server functions as the central processing unit of this system. The server collects various data about the target area. Specifically, it obtains local population data, traffic patterns, geographic information, infrastructure status, and environmental data from open databases and real-time sensors. Based on this data, the server applies analytical algorithms to understand the current situation in the area and identify challenges.

[0483] Based on the analysis results, the server accesses a database of successful case studies from around the world to identify similar redevelopment projects. Considering the factors extracted from these success stories, the server uses generative AI to create a redevelopment plan tailored to the specific area. This plan includes concrete suggestions for revitalizing the area, such as the placement of commercial facilities and the expansion of public transportation.

[0484] The created redevelopment plan is presented to the user via a terminal. The terminal displays the plan in a visually easy-to-understand manner and supports the user in evaluating the plan and reviewing the proposed content. The user can send feedback on the proposal to the server via the terminal.

[0485] Based on the collected feedback, the server uses generative AI to further improve the plan. Furthermore, it simulates the economic effects of the plan and evaluates its impact on the local economy. To ensure sustainability, the server evaluates it from an environmental design perspective and proposes an optimized design.

[0486] Users can ultimately select the plan that best matches the characteristics of their area based on the multiple plans presented. This minimizes uncertainties during the planning stage, resulting in a more feasible and sustainable redevelopment plan.

[0487] This invention makes it possible to effectively implement redevelopment tailored to regional characteristics and bring new value to local communities. For example, in sparsely populated suburban areas, AI can propose the optimal placement of commercial facilities and provide plans to attract new residents and visitors, thereby promoting regional revitalization.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The server collects data for the target area. It obtains demographic data, traffic volume data, environmental data, etc. from open databases, and collects traffic patterns and air quality data from real-time sensors. This collects basic information to understand the current situation in the area.

[0491] Step 2:

[0492] The server analyzes the collected data. It applies machine learning algorithms to perform data analysis to identify local and potential issues. For example, it extracts trends such as population decline and traffic congestion times.

[0493] Step 3:

[0494] The server accesses a global database of successful case studies. Based on regional characteristics, it searches for similar redevelopment projects and identifies success factors. Natural language processing techniques are used to generate summaries of relevant case studies.

[0495] Step 4:

[0496] The server generates redevelopment plans using AI. Based on the analysis results and successful case studies, it creates specific plans and proposes the optimal placement of commercial facilities, public facilities, and residential areas.

[0497] Step 5:

[0498] The device displays the generated plan to the user. Visualization tools are used to clearly present the plan's contents and help the user review the details. The user can evaluate the plan and provide feedback.

[0499] Step 6:

[0500] The server receives feedback from users and modifies the plan based on that information. The generative AI model, which incorporates the feedback, improves the accuracy of the plan.

[0501] Step 7:

[0502] The server simulates the economic effects of the revised plan. Using an economic model, it generates data predicting the potential for job creation, increased tax revenue, and regional economic revitalization.

[0503] Step 8:

[0504] The server optimizes environmental design to assess sustainability. It evaluates resource consumption and energy efficiency and proposes the optimal environmental design.

[0505] Step 9:

[0506] The server predicts long-term future impacts based on multiple scenarios. Using scenario analysis, it evaluates the future impacts of regional redevelopment and proposes the optimal strategy.

[0507] Step 10:

[0508] Users determine the regional redevelopment strategy based on the final plan. They select a sustainable plan that matches the characteristics of the region, based on the presented data and scenario information.

[0509] (Example 1)

[0510] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] In modern regional development, there is a problem where uniform development plans that disregard local characteristics are being implemented, resulting in insufficient sustainability and economic revitalization of the region. Furthermore, past development plans often fail to efficiently utilize unique local data and do not take future impacts into consideration. As a result, it becomes difficult to respond to unexpected environmental changes and to meet the needs of residents.

[0512] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0513] In this invention, the server includes means for collecting local information, means for analyzing the collected information, and means for referring to records of successful cases and identifying similar cases. This enables the generation of redevelopment plans closely tailored to local characteristics, facilitating sustainable development and economic revitalization. Furthermore, by revising the plan based on user feedback, development that can flexibly respond to the needs of residents is realized.

[0514] "Means for collecting local information" refers to methods or devices for efficiently collecting diverse information related to a given region, such as population, transportation, geography, infrastructure, and environment.

[0515] "Means for analyzing collected information" refers to methods or devices for conducting statistical, spatial, or time-series analysis based on collected data to clarify the current situation and challenges of a region.

[0516] "Means of identifying similar cases by referring to records of successful cases" refers to a method or apparatus for searching a database of past successful projects to find past cases that are similar to the current situation.

[0517] "An algorithmic means for generating development plans" refers to a computational method or device for automatically creating an optimal redevelopment plan that takes into account the characteristics and challenges of the region.

[0518] "Means for simulating the economic effects of a plan" refers to methods or devices for predicting and evaluating the impact of a proposed development plan on the regional economy.

[0519] "Means for evaluating sustainability and optimizing environmental design" refers to methods or devices for evaluating the environmental impact of development plans and proposing optimal designs from a sustainable perspective.

[0520] A "user interface means" is a visual or manipulable means for a user to interact with a system, allowing them to confirm and select plans.

[0521] "Means for summarizing using natural language processing" refers to a method or apparatus for summarizing collected text information using natural language processing techniques and extracting key points.

[0522] "Means of receiving user feedback" refers to methods or devices for collecting and analyzing feedback and opinions that users have given to the system.

[0523] This invention will now be described in terms of embodiments for carrying it out. The purpose of this system is to efficiently generate and evaluate redevelopment plans tailored to regional characteristics. The system consists of a server, terminals, and users, each playing a different role.

[0524] The server functions as the central processing unit of this system. Using open databases and real-time sensors to collect local information, the server acquires population data, traffic patterns, geographical information, infrastructure status, and environmental data. It applies analytical algorithms to the collected data to accurately understand the current state of the region and identify challenges. Next, it refers to success stories to find similar cases and uses generative AI models to automatically generate redevelopment plans suitable for the region. The server enables efficient data processing by executing these processes on a scalable cloud platform.

[0525] To evaluate the economic impact of the generated plan, the server uses simulation software to predict its effect on the local economy. It also conducts sustainability assessments to optimize the environmental design and proposes an optimal design incorporating the results. The generated plan is then restructured using visualization software in a human-readable format.

[0526] As a concrete example, when considering a redevelopment plan for a sparsely populated suburban area, a prompt such as "Generate a plan to optimize the placement of commercial facilities in the sparsely populated area" is entered into the AI ​​model. Based on this prompt, the server can generate an optimized facility placement plan.

[0527] The terminal's role is to present the redevelopment plan provided by the server to the user. The terminal provides an intuitive user interface, supporting the user in reviewing and evaluating each element of the redevelopment plan. User feedback is sent to the server via the terminal and used to improve the plan.

[0528] In this way, this system makes the most of regional characteristics and enables effective redevelopment that balances sustainability and economic revitalization.

[0529] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0530] Step 1:

[0531] The server collects local information. Specifically, it obtains population data, traffic patterns, geographical information, infrastructure status, and environmental data from open databases and real-time sensors using API communication. In this process, the collected data is converted into a structured format and stored in the database. Query conditions are provided as input, and local characteristic data is obtained as output.

[0532] Step 2:

[0533] The server analyzes the collected data to understand the current situation and challenges of the region. Specifically, it performs data cleaning to remove missing and outlier values. Next, it uses a statistical analysis module to analyze demographics and traffic patterns. In this step, regional characteristic data is used as input, and a report on the current situation of the region is generated as output.

[0534] Step 3:

[0535] The server identifies similar projects by referring to success stories. Specifically, it accesses a database of success stories, analyzes the text using natural language processing, and extracts similarities. At this stage, the input is the aforementioned status report, and the output is the identification of similar cases.

[0536] Step 4:

[0537] The server generates redevelopment plans using a generative AI model. Specifically, it takes prompts such as "Generate a plan to optimize the placement of commercial facilities in sparsely populated areas," and the AI ​​creates plans for the placement of commercial facilities and the expansion of public facilities. The input is a prompt, and the output is a detailed redevelopment plan.

[0538] Step 5:

[0539] The terminal presents the generated redevelopment plan to the user. Specifically, it uses visualization tools to display the plan as an interactive map and graph, allowing the user to click on each element to view detailed information. The input is the redevelopment plan, and the output is the visualization data available to the user.

[0540] Step 6:

[0541] Users evaluate the presented plan and provide feedback. Specifically, they input their opinions and suggestions regarding specific elements of the plan in text format into their terminal and send them to the server. The input is the user's feedback on the plan, and the output is the collection of this feedback information on the server.

[0542] Step 7:

[0543] The server reuses the generated AI model based on the collected feedback to improve the plan. Specifically, it considers user opinions, updates the generated plan, and re-runs economic effect simulations and sustainability evaluations. The input is user feedback and the initial plan, and the output is an improved redevelopment plan.

[0544] (Application Example 1)

[0545] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] In modern redevelopment planning, creating effective plans that take into account local characteristics is challenging. In particular, elements such as the placement of commercial facilities and the expansion of public transportation require sustainable implementation with the participation of local residents. Therefore, it is necessary to construct plans in a more concrete and visually understandable way, and to effectively incorporate feedback from residents.

[0547] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0548] In this invention, the server includes means for collecting information about a region, means for analyzing the collected information, and means for referring to a database of reference cases to identify similar cases. This makes it possible to visualize the generated redevelopment plan based on regional characteristics using augmented reality technology, allowing residents to easily understand it and provide feedback.

[0549] "Regional information" refers to various types of data related to a specific region, such as geographical information, demographics, traffic patterns, infrastructure status, and environmental conditions.

[0550] "Means of analyzing collected information" refers to methods and technologies for analyzing collected regional data to conduct current situation assessments and identify issues necessary for redevelopment.

[0551] A "reference case database" refers to a collection of information that accumulates past redevelopment projects and successful urban planning examples, allowing users to search and refer to similar cases.

[0552] A "generative algorithm" refers to a set of computational procedures used to process data based on a specific purpose and automatically generate new plans or proposals.

[0553] "Means for simulating the economic effects of generated plans" refers to models and technologies for predicting and evaluating the impact of redevelopment plans on the local economy.

[0554] "Means of evaluating sustainability and optimizing environmental design" refers to technologies that design and adjust for long-term sustainable development while considering environmental impact.

[0555] "Means of visualization using augmented reality technology" refers to technologies that overlay digital plans onto the real world, making them easier for users to understand visually.

[0556] "Scenario analysis for predicting long-term future impacts" refers to a method of quantitatively or qualitatively evaluating the future impact of a redevelopment plan based on multiple future hypotheses.

[0557] This invention is a system for generating redevelopment plans suited to local characteristics and presenting them to residents. The specific forms of its implementation are described below.

[0558] First, the server acquires and stores information about the region from open databases and real-time sensors. This includes geographical information, demographic data, traffic patterns, infrastructure status, and environmental conditions. The server uses analytical algorithms to analyze this data and identify the current state of the region and its challenges.

[0559] Based on the analysis results, the server refers to a database of reference cases to identify similar redevelopment projects. Then, it uses a generative AI model to generate specific redevelopment plans tailored to the characteristics of the region. The plan generation process includes considering factors from past successful projects and simulating the economic effects of the generated plans to verify their impact on the local economy.

[0560] The generated plans are evaluated for sustainability and optimized from an environmental design perspective. Furthermore, augmented reality technology is used to allow users to visually review the plans on their devices. Specifically, visualization is possible by overlaying the plan content onto real-world scenery via devices such as smartphones.

[0561] Users can review the presented plan via their device and provide feedback. The server collects this feedback and uses the generation AI again to improve the plan. This feedback loop results in a more feasible plan.

[0562] For example, in a sparsely populated area, the AI ​​can propose the optimal placement of commercial facilities and, based on that, provide a plan for regional revitalization, such as attracting new residents and visitors. An example of a prompt for the generating AI model is: "Based on regional data, consider the impact of commercial facilities and transportation infrastructure on the regional economy, and identify and propose the optimal factors for generating a new redevelopment plan."

[0563] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0564] Step 1:

[0565] The server acquires regional information from open databases and real-time sensors. Input information includes geographical data, demographic data, and traffic patterns. This data is stored in a database and prepared for analysis. The output is an analyzable regional dataset.

[0566] Step 2:

[0567] The server analyzes the accumulated regional datasets using analytical algorithms. Based on input data such as population dynamics and traffic patterns, it assesses the current state of the region and identifies specific challenges. This process involves data classification and cluster analysis, generating a list of regional characteristics and problems as output.

[0568] Step 3:

[0569] Based on these analysis results, the server refers to a database of reference cases to identify similar redevelopment projects. The input is the regional characteristics and problems obtained in step 2, and based on this, it searches for past successful cases. The output is a list of relevant cases and their details.

[0570] Step 4:

[0571] The server uses a generative AI model to generate redevelopment plans tailored to regional characteristics. The input data consists of regional characteristics and factors from similar cases obtained in steps 2 and 3. This results in the output of specific plans, including the placement of commercial facilities and public transportation.

[0572] Step 5:

[0573] The server simulates the economic effects of the generated redevelopment plan. The input is the plan obtained in step 4, and an economic model is applied to predict its impact on the regional economy. The output is a report of the economic effects as a result of the simulation.

[0574] Step 6:

[0575] The server evaluates the sustainability of the generated plan and optimizes the environmental design. Based on the redevelopment plan as input, it conducts an environmental impact assessment. The output is an optimized redevelopment plan that includes sustainable design elements.

[0576] Step 7:

[0577] The device visually presents the final generated redevelopment plan to the user using augmented reality technology. The input is the optimized plan from step 6, and the output is an AR display that the user can visually confirm.

[0578] Step 8:

[0579] Users review the redevelopment plan presented through their terminal and provide feedback. The input consists of user opinions and suggestions, while the output is data sent to the server as feedback.

[0580] Step 9:

[0581] The server analyzes the feedback received from the user and uses the generating AI again to improve the redevelopment plan. The input is the feedback obtained in step 8, and the output is the revised redevelopment plan.

[0582] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0583] This invention is a system that combines generative AI and an emotion engine to provide an optimal redevelopment plan based on regional characteristics. In its embodiment, the server, terminal, and user play the main roles and work together to process each step.

[0584] The server performs the central processing of this system. It collects vast amounts of data about the region and analyzes it using machine learning algorithms. This identifies the current situation and potential challenges in the region. Based on this analysis, it generates plans suitable for areas requiring redevelopment. The generating AI refers to a database of past success stories and presents the optimal strategy based on similar cases.

[0585] The generated plan is delivered to the user via a device. The device uses visualization tools to clearly display the plan's details and effects. During this process, an emotion engine analyzes the user's emotions based on facial recognition and voice, determining their satisfaction level and areas of dissatisfaction. This emotion data is sent to a server and used to improve the plan.

[0586] Users evaluate redevelopment plans via their devices and provide feedback, while emotional information analyzed by the emotion engine is incorporated into plan adjustments. This results in plans tailored to the individual needs of each user. For example, if a user expresses positive emotions towards a plan, the detailed design proceeds based on that plan; conversely, if negative emotions are expressed, the plan is reconsidered.

[0587] Furthermore, the server utilizes an emotion engine to incorporate users' emotional responses into scenario analysis, enabling more precise and effective long-term regional development strategies. The results of economic impact and environmental assessments obtained from simulations, along with user emotional information, are integrated to present the final strategy.

[0588] These elements enable the creation of sustainable redevelopment plans optimized for local characteristics and residents' needs, bringing new value and vitality to the community. For example, in urban redevelopment, an emotional engine can analyze residents' satisfaction in real time and reflect this in the placement of public spaces and the selection of facilities, thereby creating a more comfortable and inviting community environment.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The server collects regional data from various sources. Specifically, it obtains demographic data, traffic data, environmental monitoring data, and other information from open databases on the internet, and collects sensor information in real time. This data provides the basis for analyzing the current state of the region.

[0592] Step 2:

[0593] The server analyzes the collected data. Using machine learning algorithms and data analysis tools, it identifies local issues and pinpoints the needs of residents and areas requiring improvement. It also refers to a database of past success stories to identify success factors in similar regions.

[0594] Step 3:

[0595] The server uses AI generation to create redevelopment plans. Based on regional characteristics and successful case studies, it automatically generates optimal facility placement and infrastructure improvement plans, and compares the features of each plan.

[0596] Step 4:

[0597] The terminal presents the generated redevelopment plan to the user. The user interface visually displays the plan's contents in an easy-to-understand manner, allowing the user to review the details.

[0598] Step 5:

[0599] The emotion engine analyzes the user's emotions. While the user is viewing the presented plan, it uses the camera and voice input to evaluate the user's emotional state in real time and identify areas of satisfaction and dissatisfaction.

[0600] Step 6:

[0601] Users provide feedback on the redevelopment plan. They input comments and ratings through their devices, and this feedback, along with sentiment information obtained by the sentiment engine, is sent to the server.

[0602] Step 7:

[0603] The server modifies the plan based on user feedback and sentiment information. It restarts the generation AI and adjusts the plan to incorporate the improvements pointed out in the feedback.

[0604] Step 8:

[0605] The server performs simulations of the economic effects. Using economic models, it predicts the impact of the revised plan on the local economy and assesses its sustainability.

[0606] Step 9:

[0607] The server conducts scenario analysis to predict long-term future impacts. It considers multiple scenarios, including user response data obtained from the emotion engine, to formulate the optimal strategy.

[0608] Step 10:

[0609] Users select the final plan and determine the regional redevelopment strategy. Based on detailed data and strategic options presented by the server, they can choose the most suitable plan.

[0610] (Example 2)

[0611] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0612] In modern society, regional redevelopment plans are required to accurately reflect local characteristics and the needs of residents. However, traditional methods make it difficult to select highly relevant data from a vast amount of information and create plans that take residents' feelings into consideration. As a result, there is a challenge in proposing sustainable redevelopment plans that are tailored to local characteristics and the needs of residents.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0614] In this invention, the server includes means for collecting local information, means for analyzing the collected information, and means for generating an AI model. This makes it possible to generate an optimal redevelopment plan based on local characteristics and to propose a plan that reflects the user's feelings.

[0615] "Means of collecting local information" refers to methods of gathering geographical, demographic, and economic information about a specific region.

[0616] "Means of analyzing collected information" refers to methods of using machine learning algorithms to analyze collected data and identify the current situation and potential challenges in the region.

[0617] "Means of identifying similar cases by referring to information on successful cases" refers to methods for identifying similar successful cases from a database of past redevelopment projects.

[0618] "Generative AI modeling" refers to artificial intelligence techniques used to automatically generate new redevelopment plans by learning from past cases.

[0619] "Means for simulating economic outcomes" refers to methods for calculating the expected economic impacts if a redevelopment plan is implemented.

[0620] "Means for assessing sustainability and optimizing environmental plans" refers to methods for analyzing the environmental impact of planned redevelopment and optimizing it in a sustainable manner.

[0621] A "scenario analysis method for predicting long-term future impacts" is a simulation method for predicting how a redevelopment plan will affect a region over time.

[0622] "An emotional engine that analyzes user emotional information and reflects it in redevelopment plans" refers to a method of analyzing user emotions and incorporating the results as feedback into redevelopment plans.

[0623] This invention is a system for generating optimal plans that reflect regional characteristics and the needs of residents in regional redevelopment. Specific embodiments are described below.

[0624] Server Role

[0625] The server handles the central processing of this system. First, it collects local information by using open databases and geographic information systems to obtain geographic, demographic, and economic data. This collected information is analyzed using machine learning libraries such as Python's Scikit-learn and TensorFlow. Based on the analysis results, it refers to successful case studies and identifies similar cases. This prepares the generative AI model to generate the optimal redevelopment plan. The server also simulates the economic impact of the generated plan and performs calculations to assess its sustainability.

[0626] Terminal role

[0627] The terminal is responsible for visualizing and presenting the generated redevelopment plan to the user. A web application is used for visualization, allowing users to intuitively understand the plan through 3D models and infographics. During this process, an emotion engine built into the terminal analyzes the user's facial expressions and voice to collect emotional information. This information is sent to a server and used to improve the plan.

[0628] User roles

[0629] Users evaluate the redevelopment plan through their devices and provide feedback based on emotional information analyzed by an emotion engine. This user feedback is used to revise and improve the plan, resulting in a redevelopment plan that better suits individual needs.

[0630] As a concrete example, when a user expresses interest in urban redevelopment and proposes a layout for public spaces, the server references past successful examples and generates an optimal layout plan. The terminal visualizes this plan, allowing the user to review each element and provide feedback. This enables effective development that leverages the unique characteristics of the region.

[0631] Example of a prompt

[0632] "Please generate a sustainable redevelopment plan for Shibuya Ward in Tokyo. Include an analysis of local resident satisfaction and provide specific proposals for the placement of public spaces."

[0633] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0634] Step 1:

[0635] The user accesses the system and enters information about the target area. This information includes the area's geographical characteristics, the purpose of redevelopment, and priority areas for improvement. The terminal sends this information to the server, which then receives the necessary initial data.

[0636] Step 2:

[0637] The server automatically collects relevant data from external databases and geographic information systems based on the received regional information. Using the input regional parameters as keys, it searches for geographical conditions, demographics, and economic indicators to construct a dataset. The resulting dataset is then generated.

[0638] Step 3:

[0639] The server uses machine learning algorithms to analyze the collected dataset. This step utilizes Python's Scikit-learn and TensorFlow libraries to identify regional challenges and potential development opportunities. The analysis results output a list of regional challenges and development opportunities.

[0640] Step 4:

[0641] The server references successful case studies and selects cases similar to the analysis results. This identifies strategies and elements that have a high success rate in redevelopment. In this step, past project data is filtered and relevant data is extracted.

[0642] Step 5:

[0643] A generative AI model is used to combine identified challenges with success stories to generate an optimal redevelopment plan. Inputs include feedback data from success stories and on-site data. The AI ​​model integrates these to output a predictive plan.

[0644] Step 6:

[0645] The terminal visualizes the generated redevelopment plan and presents it to the user as a 3D model or infographic. The output plan is accessible through the user interface, allowing the user to interactively review each part of the plan.

[0646] Step 7:

[0647] The device uses sensors to detect the user's facial expressions and voice, and its built-in emotion engine analyzes the user's emotions. The input includes emotion data acquired in real time. The analysis outputs an emotional evaluation of the user's response to the plan.

[0648] Step 8:

[0649] The server receives the user's sentiment evaluation and feedback, and uses the generative AI model again to improve the plan. The improved plan reflects the user's favorable feedback and corrects negative feedback. This results in the output of the final redevelopment plan, which is then presented to the user.

[0650] (Application Example 2)

[0651] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0652] Modern regional redevelopment projects require plans that fully reflect local characteristics and the needs of residents. However, traditional methods have made it difficult to collect and analyze detailed local information, and emotional feedback from residents has often not been quickly integrated. As a result, development plans often fail to meet residents' expectations, hindering the sustainable development of the region.

[0653] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0654] In this invention, the server includes means for collecting local information, means for identifying similar cases by referring to information on successful cases, and means for collecting emotional feedback from users using an emotion analysis engine. This enables the generation of redevelopment plans based on local characteristics and the reflection of resident feedback in real time.

[0655] "Means of collecting local information" refers to methods for collecting geographical, social, and economic data related to a specific region.

[0656] "Means of analysis" refers to methods of processing collected data using computer algorithms to clarify the current situation and problems of a region.

[0657] "Methods for identifying similar cases by referring to success stories" refers to methods for exploring past successful development cases and finding similar cases that can be applied to the current situation.

[0658] "Generative AI means" refers to a function that uses machine learning and artificial intelligence technologies to automatically generate redevelopment plans based on regional characteristics.

[0659] "Means for simulating economic impacts" refers to methods for predicting the economic outcomes of a planned redevelopment project.

[0660] "Methods for evaluating sustainability and optimizing environmental design" refers to methods for evaluating the environmental impact of redevelopment plans and optimizing environmental design to achieve a sustainable society.

[0661] "Scenario analysis methods for predicting long-term future impacts" are methods for comprehensively predicting the impact of redevelopment plans on a region in the future.

[0662] "A means of visually providing regional development plans through AR display" refers to a method of using augmented reality technology to visually present plans to residents and aid their understanding.

[0663] "Methods for collecting emotional feedback from users using an emotion analysis engine" refers to methods for analyzing emotions from users' facial expressions and voices and collecting that feedback.

[0664] This invention is a system that generates regional redevelopment plans for smart cities and incorporates resident feedback in real time. Its main components include a server, terminals, and users.

[0665] The server handles the central processing of this system. The server runs programs on high-performance computers to collect and analyze vast amounts of information about the region. These programs use machine learning algorithms and artificial intelligence models to identify areas requiring redevelopment from the local data. This enables data-driven, scientific, and logical decision-making. Based on the analysis results, a generative AI system references a database of past success stories to generate an optimal redevelopment plan tailored to the specific characteristics of the region.

[0666] The terminal provides an interface for users to review the plan and provide feedback. This terminal is envisioned to be a mobile information device such as a smartphone or tablet. Users can visually review the redevelopment plan transmitted from the server on the terminal. Augmented reality (AR) functionality is used to overlay the plan onto the actual landscape, deepening residents' understanding. Emotional feedback is collected from the user's facial expressions and voice using an emotion analysis engine and transmitted to the server.

[0667] Users are the ultimate beneficiaries of this system, evaluating the generated plans and providing feedback. Feedback based on user sentiment data is re-analyzed by the server and used to optimize the plans. This ensures that the plans are flexible and responsive to the needs and feelings of the residents.

[0668] As a concrete example, in urban redevelopment, users can view augmented reality (AR) simulations of new park designs via their smartphones, and their facial expressions can be seen, allowing for real-time evaluation of their level of satisfaction and their positive feelings towards the environment. Based on this information, plans can be revised and new ideas incorporated, resulting in community development that achieves high levels of resident satisfaction.

[0669] Examples of prompt statements for manipulating a generative AI model include:

[0670] "Regarding the park development plan for this area, please propose the optimal layout and methods for promoting community activities, drawing on past successful examples."

[0671] This prompt allows the generating AI to output specific and actionable ideas, thereby improving the quality of the plan.

[0672] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0673] Step 1:

[0674] The server collects information relevant to a region. It takes geographic data, demographic data, economic data, etc., as input and stores them in a database. Based on this information, it constructs a dataset to evaluate the current situation and challenges of the region.

[0675] Step 2:

[0676] The server analyzes the collected data. It uses the dataset constructed in Step 1 as input. Machine learning algorithms and statistical analysis techniques are applied to identify regional characteristics and potential challenges. The output consists of numerical data and graphs illustrating regional characteristics.

[0677] Step 3:

[0678] The server references successful case studies to identify similar cases. It uses analyzed regional characteristics data as input. It searches the database for similar past cases and provides reference information for formulating optimal redevelopment plans. The output is a list of selected successful cases.

[0679] Step 4:

[0680] The server generates a redevelopment plan using a generative AI method. Analysis results and a list of successful cases are input to the generative AI model along with prompts. Based on this information, the AI ​​model outputs an optimal development plan tailored to the specific characteristics of the region.

[0681] Step 5:

[0682] The terminal visualizes the generated redevelopment plan. It receives development plan data from a server as input. Using augmented reality (AR) technology, it overlays the plan onto the real-world landscape for visual display. The output is a 3D model on the terminal screen.

[0683] Step 6:

[0684] Users evaluate the plan and provide feedback through their device. The input is a visualized plan displayed on the device. An emotion analysis engine, using facial expressions and voice, analyzes the user's reactions and sends emotion feedback data to the server. The output is emotion feedback information indicating satisfaction levels and areas for improvement.

[0685] Step 7:

[0686] The server optimizes the redevelopment plan based on user feedback. It uses the sentiment feedback data collected in step 6 as input. The feedback is incorporated into the existing plan data, the plan is modified as needed, and the final redevelopment plan is generated. The output is the optimized redevelopment plan.

[0687] 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.

[0688] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0689] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0690] [Fourth Embodiment]

[0691] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0692] 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.

[0693] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0694] 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.

[0695] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0696] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0697] 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.

[0698] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0699] 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.

[0700] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0701] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0702] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0703] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0704] This invention is a system that utilizes generative AI to generate and evaluate redevelopment plans tailored to regional characteristics, and in order to implement this, the server, terminal, and user each play a specific role.

[0705] The server functions as the central processing unit of this system. The server collects various data about the target area. Specifically, it obtains local population data, traffic patterns, geographic information, infrastructure status, and environmental data from open databases and real-time sensors. Based on this data, the server applies analytical algorithms to understand the current situation in the area and identify challenges.

[0706] Based on the analysis results, the server accesses a database of successful case studies from around the world to identify similar redevelopment projects. Considering the factors extracted from these success stories, the server uses generative AI to create a redevelopment plan tailored to the specific area. This plan includes concrete suggestions for revitalizing the area, such as the placement of commercial facilities and the expansion of public transportation.

[0707] The created redevelopment plan is presented to the user via a terminal. The terminal displays the plan in a visually easy-to-understand manner and supports the user in evaluating the plan and reviewing the proposed content. The user can send feedback on the proposal to the server via the terminal.

[0708] Based on the collected feedback, the server uses generative AI to further improve the plan. Furthermore, it simulates the economic effects of the plan and evaluates its impact on the local economy. To ensure sustainability, the server evaluates it from an environmental design perspective and proposes an optimized design.

[0709] Users can ultimately select the plan that best matches the characteristics of their area based on the multiple plans presented. This minimizes uncertainties during the planning stage, resulting in a more feasible and sustainable redevelopment plan.

[0710] This invention makes it possible to effectively implement redevelopment tailored to regional characteristics and bring new value to local communities. For example, in sparsely populated suburban areas, AI can propose the optimal placement of commercial facilities and provide plans to attract new residents and visitors, thereby promoting regional revitalization.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The server collects data for the target area. It obtains demographic data, traffic volume data, environmental data, etc. from open databases, and collects traffic patterns and air quality data from real-time sensors. This collects basic information to understand the current situation in the area.

[0714] Step 2:

[0715] The server analyzes the collected data. It applies machine learning algorithms to perform data analysis to identify local and potential issues. For example, it extracts trends such as population decline and traffic congestion times.

[0716] Step 3:

[0717] The server accesses a global database of successful case studies. Based on regional characteristics, it searches for similar redevelopment projects and identifies success factors. Natural language processing techniques are used to generate summaries of relevant case studies.

[0718] Step 4:

[0719] The server generates redevelopment plans using AI. Based on the analysis results and successful case studies, it creates specific plans and proposes the optimal placement of commercial facilities, public facilities, and residential areas.

[0720] Step 5:

[0721] The device displays the generated plan to the user. Visualization tools are used to clearly present the plan's contents and help the user review the details. The user can evaluate the plan and provide feedback.

[0722] Step 6:

[0723] The server receives feedback from users and modifies the plan based on that information. The generative AI model, which incorporates the feedback, improves the accuracy of the plan.

[0724] Step 7:

[0725] The server simulates the economic effects of the revised plan. Using an economic model, it generates data predicting the potential for job creation, increased tax revenue, and regional economic revitalization.

[0726] Step 8:

[0727] The server optimizes environmental design to assess sustainability. It evaluates resource consumption and energy efficiency and proposes the optimal environmental design.

[0728] Step 9:

[0729] The server predicts long-term future impacts based on multiple scenarios. Using scenario analysis, it evaluates the future impacts of regional redevelopment and proposes the optimal strategy.

[0730] Step 10:

[0731] Users determine the regional redevelopment strategy based on the final plan. They select a sustainable plan that matches the characteristics of the region, based on the presented data and scenario information.

[0732] (Example 1)

[0733] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] In modern regional development, there is a problem where uniform development plans that disregard local characteristics are being implemented, resulting in insufficient sustainability and economic revitalization of the region. Furthermore, past development plans often fail to efficiently utilize unique local data and do not take future impacts into consideration. As a result, it becomes difficult to respond to unexpected environmental changes and to meet the needs of residents.

[0735] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0736] In this invention, the server includes means for collecting local information, means for analyzing the collected information, and means for referring to records of successful cases and identifying similar cases. This enables the generation of redevelopment plans closely tailored to local characteristics, facilitating sustainable development and economic revitalization. Furthermore, by revising the plan based on user feedback, development that can flexibly respond to the needs of residents is realized.

[0737] "Means for collecting local information" refers to methods or devices for efficiently collecting diverse information related to a given region, such as population, transportation, geography, infrastructure, and environment.

[0738] "Means for analyzing collected information" refers to methods or devices for conducting statistical, spatial, or time-series analysis based on collected data to clarify the current situation and challenges of a region.

[0739] "Means of identifying similar cases by referring to records of successful cases" refers to a method or apparatus for searching a database of past successful projects to find past cases that are similar to the current situation.

[0740] "An algorithmic means for generating development plans" refers to a computational method or device for automatically creating an optimal redevelopment plan that takes into account the characteristics and challenges of the region.

[0741] "Means for simulating the economic effects of a plan" refers to methods or devices for predicting and evaluating the impact of a proposed development plan on the regional economy.

[0742] "Means for evaluating sustainability and optimizing environmental design" refers to methods or devices for evaluating the environmental impact of development plans and proposing optimal designs from a sustainable perspective.

[0743] A "user interface means" is a visual or manipulable means for a user to interact with a system, allowing them to confirm and select plans.

[0744] "Means for summarizing using natural language processing" refers to a method or apparatus for summarizing collected text information using natural language processing techniques and extracting key points.

[0745] "Means of receiving user feedback" refers to methods or devices for collecting and analyzing feedback and opinions that users have given to the system.

[0746] This invention will now be described in terms of embodiments for carrying it out. The purpose of this system is to efficiently generate and evaluate redevelopment plans tailored to regional characteristics. The system consists of a server, terminals, and users, each playing a different role.

[0747] The server functions as the central processing unit of this system. Using open databases and real-time sensors to collect local information, the server acquires population data, traffic patterns, geographical information, infrastructure status, and environmental data. It applies analytical algorithms to the collected data to accurately understand the current state of the region and identify challenges. Next, it refers to success stories to find similar cases and uses generative AI models to automatically generate redevelopment plans suitable for the region. The server enables efficient data processing by executing these processes on a scalable cloud platform.

[0748] To evaluate the economic impact of the generated plan, the server uses simulation software to predict its effect on the local economy. It also conducts sustainability assessments to optimize the environmental design and proposes an optimal design incorporating the results. The generated plan is then restructured using visualization software in a human-readable format.

[0749] As a concrete example, when considering a redevelopment plan for a sparsely populated suburban area, a prompt such as "Generate a plan to optimize the placement of commercial facilities in the sparsely populated area" is entered into the AI ​​model. Based on this prompt, the server can generate an optimized facility placement plan.

[0750] The terminal's role is to present the redevelopment plan provided by the server to the user. The terminal provides an intuitive user interface, supporting the user in reviewing and evaluating each element of the redevelopment plan. User feedback is sent to the server via the terminal and used to improve the plan.

[0751] In this way, this system makes the most of regional characteristics and enables effective redevelopment that balances sustainability and economic revitalization.

[0752] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0753] Step 1:

[0754] The server collects local information. Specifically, it obtains population data, traffic patterns, geographical information, infrastructure status, and environmental data from open databases and real-time sensors using API communication. In this process, the collected data is converted into a structured format and stored in the database. Query conditions are provided as input, and local characteristic data is obtained as output.

[0755] Step 2:

[0756] The server analyzes the collected data to understand the current situation and challenges of the region. Specifically, it performs data cleaning to remove missing and outlier values. Next, it uses a statistical analysis module to analyze demographics and traffic patterns. In this step, regional characteristic data is used as input, and a report on the current situation of the region is generated as output.

[0757] Step 3:

[0758] The server identifies similar projects by referring to success stories. Specifically, it accesses a database of success stories, analyzes the text using natural language processing, and extracts similarities. At this stage, the input is the aforementioned status report, and the output is the identification of similar cases.

[0759] Step 4:

[0760] The server generates redevelopment plans using a generative AI model. Specifically, it takes prompts such as "Generate a plan to optimize the placement of commercial facilities in sparsely populated areas," and the AI ​​creates plans for the placement of commercial facilities and the expansion of public facilities. The input is a prompt, and the output is a detailed redevelopment plan.

[0761] Step 5:

[0762] The terminal presents the generated redevelopment plan to the user. Specifically, it uses visualization tools to display the plan as an interactive map and graph, allowing the user to click on each element to view detailed information. The input is the redevelopment plan, and the output is the visualization data available to the user.

[0763] Step 6:

[0764] Users evaluate the presented plan and provide feedback. Specifically, they input their opinions and suggestions regarding specific elements of the plan in text format into their terminal and send them to the server. The input is the user's feedback on the plan, and the output is the collection of this feedback information on the server.

[0765] Step 7:

[0766] The server reuses the generated AI model based on the collected feedback to improve the plan. Specifically, it considers user opinions, updates the generated plan, and re-runs economic effect simulations and sustainability evaluations. The input is user feedback and the initial plan, and the output is an improved redevelopment plan.

[0767] (Application Example 1)

[0768] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0769] In modern redevelopment planning, creating effective plans that take into account local characteristics is challenging. In particular, elements such as the placement of commercial facilities and the expansion of public transportation require sustainable implementation with the participation of local residents. Therefore, it is necessary to construct plans in a more concrete and visually understandable way, and to effectively incorporate feedback from residents.

[0770] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0771] In this invention, the server includes means for collecting information about a region, means for analyzing the collected information, and means for referring to a database of reference cases to identify similar cases. This makes it possible to visualize the generated redevelopment plan based on regional characteristics using augmented reality technology, allowing residents to easily understand it and provide feedback.

[0772] "Regional information" refers to various types of data related to a specific region, such as geographical information, demographics, traffic patterns, infrastructure status, and environmental conditions.

[0773] "Means of analyzing collected information" refers to methods and technologies for analyzing collected regional data to conduct current situation assessments and identify issues necessary for redevelopment.

[0774] A "reference case database" refers to a collection of information that accumulates past redevelopment projects and successful urban planning examples, allowing users to search and refer to similar cases.

[0775] A "generative algorithm" refers to a set of computational procedures used to process data based on a specific purpose and automatically generate new plans or proposals.

[0776] "Means for simulating the economic effects of generated plans" refers to models and technologies for predicting and evaluating the impact of redevelopment plans on the local economy.

[0777] "Means of evaluating sustainability and optimizing environmental design" refers to technologies that design and adjust for long-term sustainable development while considering environmental impact.

[0778] "Means of visualization using augmented reality technology" refers to technologies that overlay digital plans onto the real world, making them easier for users to understand visually.

[0779] "Scenario analysis for predicting long-term future impacts" refers to a method of quantitatively or qualitatively evaluating the future impact of a redevelopment plan based on multiple future hypotheses.

[0780] This invention is a system for generating redevelopment plans suited to local characteristics and presenting them to residents. The specific forms of its implementation are described below.

[0781] First, the server acquires and stores information about the region from open databases and real-time sensors. This includes geographical information, demographic data, traffic patterns, infrastructure status, and environmental conditions. The server uses analytical algorithms to analyze this data and identify the current state of the region and its challenges.

[0782] Based on the analysis results, the server refers to a database of reference cases to identify similar redevelopment projects. Then, it uses a generative AI model to generate specific redevelopment plans tailored to the characteristics of the region. The plan generation process includes considering factors from past successful projects and simulating the economic effects of the generated plans to verify their impact on the local economy.

[0783] The generated plans are evaluated for sustainability and optimized from an environmental design perspective. Furthermore, augmented reality technology is used to allow users to visually review the plans on their devices. Specifically, visualization is possible by overlaying the plan content onto real-world scenery via devices such as smartphones.

[0784] Users can review the presented plan via their device and provide feedback. The server collects this feedback and uses the generation AI again to improve the plan. This feedback loop results in a more feasible plan.

[0785] For example, in a sparsely populated area, the AI ​​can propose the optimal placement of commercial facilities and, based on that, provide a plan for regional revitalization, such as attracting new residents and visitors. An example of a prompt for the generating AI model is: "Based on regional data, consider the impact of commercial facilities and transportation infrastructure on the regional economy, and identify and propose the optimal factors for generating a new redevelopment plan."

[0786] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0787] Step 1:

[0788] The server acquires regional information from open databases and real-time sensors. Input information includes geographical data, demographic data, and traffic patterns. This data is stored in a database and prepared for analysis. The output is an analyzable regional dataset.

[0789] Step 2:

[0790] The server analyzes the accumulated regional datasets using analytical algorithms. Based on input data such as population dynamics and traffic patterns, it assesses the current state of the region and identifies specific challenges. This process involves data classification and cluster analysis, generating a list of regional characteristics and problems as output.

[0791] Step 3:

[0792] Based on these analysis results, the server refers to a database of reference cases to identify similar redevelopment projects. The input is the regional characteristics and problems obtained in step 2, and based on this, it searches for past successful cases. The output is a list of relevant cases and their details.

[0793] Step 4:

[0794] The server uses a generative AI model to generate redevelopment plans tailored to regional characteristics. The input data consists of regional characteristics and factors from similar cases obtained in steps 2 and 3. This results in the output of specific plans, including the placement of commercial facilities and public transportation.

[0795] Step 5:

[0796] The server simulates the economic effects of the generated redevelopment plan. The input is the plan obtained in step 4, and an economic model is applied to predict its impact on the regional economy. The output is a report of the economic effects as a result of the simulation.

[0797] Step 6:

[0798] The server evaluates the sustainability of the generated plan and optimizes the environmental design. Based on the redevelopment plan as input, it conducts an environmental impact assessment. The output is an optimized redevelopment plan that includes sustainable design elements.

[0799] Step 7:

[0800] The device visually presents the final generated redevelopment plan to the user using augmented reality technology. The input is the optimized plan from step 6, and the output is an AR display that the user can visually confirm.

[0801] Step 8:

[0802] Users review the redevelopment plan presented through their terminal and provide feedback. The input consists of user opinions and suggestions, while the output is data sent to the server as feedback.

[0803] Step 9:

[0804] The server analyzes the feedback received from the user and uses the generating AI again to improve the redevelopment plan. The input is the feedback obtained in step 8, and the output is the revised redevelopment plan.

[0805] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0806] This invention is a system that combines generative AI and an emotion engine to provide an optimal redevelopment plan based on regional characteristics. In its embodiment, the server, terminal, and user play the main roles and work together to process each step.

[0807] The server performs the central processing of this system. It collects vast amounts of data about the region and analyzes it using machine learning algorithms. This identifies the current situation and potential challenges in the region. Based on this analysis, it generates plans suitable for areas requiring redevelopment. The generating AI refers to a database of past success stories and presents the optimal strategy based on similar cases.

[0808] The generated plan is delivered to the user via a device. The device uses visualization tools to clearly display the plan's details and effects. During this process, an emotion engine analyzes the user's emotions based on facial recognition and voice, determining their satisfaction level and areas of dissatisfaction. This emotion data is sent to a server and used to improve the plan.

[0809] Users evaluate redevelopment plans via their devices and provide feedback, while emotional information analyzed by the emotion engine is incorporated into plan adjustments. This results in plans tailored to the individual needs of each user. For example, if a user expresses positive emotions towards a plan, the detailed design proceeds based on that plan; conversely, if negative emotions are expressed, the plan is reconsidered.

[0810] Furthermore, the server utilizes an emotion engine to incorporate users' emotional responses into scenario analysis, enabling more precise and effective long-term regional development strategies. The results of economic impact and environmental assessments obtained from simulations, along with user emotional information, are integrated to present the final strategy.

[0811] These elements enable the creation of sustainable redevelopment plans optimized for local characteristics and residents' needs, bringing new value and vitality to the community. For example, in urban redevelopment, an emotional engine can analyze residents' satisfaction in real time and reflect this in the placement of public spaces and the selection of facilities, thereby creating a more comfortable and inviting community environment.

[0812] The following describes the processing flow.

[0813] Step 1:

[0814] The server collects regional data from various sources. Specifically, it obtains demographic data, traffic data, environmental monitoring data, and other information from open databases on the internet, and collects sensor information in real time. This data provides the basis for analyzing the current state of the region.

[0815] Step 2:

[0816] The server analyzes the collected data. Using machine learning algorithms and data analysis tools, it identifies local issues and pinpoints the needs of residents and areas requiring improvement. It also refers to a database of past success stories to identify success factors in similar regions.

[0817] Step 3:

[0818] The server uses AI generation to create redevelopment plans. Based on regional characteristics and successful case studies, it automatically generates optimal facility placement and infrastructure improvement plans, and compares the features of each plan.

[0819] Step 4:

[0820] The terminal presents the generated redevelopment plan to the user. The user interface visually displays the plan's contents in an easy-to-understand manner, allowing the user to review the details.

[0821] Step 5:

[0822] The emotion engine analyzes the user's emotions. While the user is viewing the presented plan, it uses the camera and voice input to evaluate the user's emotional state in real time and identify areas of satisfaction and dissatisfaction.

[0823] Step 6:

[0824] Users provide feedback on the redevelopment plan. They input comments and ratings through their devices, and this feedback, along with sentiment information obtained by the sentiment engine, is sent to the server.

[0825] Step 7:

[0826] The server modifies the plan based on user feedback and sentiment information. It restarts the generation AI and adjusts the plan to incorporate the improvements pointed out in the feedback.

[0827] Step 8:

[0828] The server performs simulations of the economic effects. Using economic models, it predicts the impact of the revised plan on the local economy and assesses its sustainability.

[0829] Step 9:

[0830] The server conducts scenario analysis to predict long-term future impacts. It considers multiple scenarios, including user response data obtained from the emotion engine, to formulate the optimal strategy.

[0831] Step 10:

[0832] Users select the final plan and determine the regional redevelopment strategy. Based on detailed data and strategic options presented by the server, they can choose the most suitable plan.

[0833] (Example 2)

[0834] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0835] In modern society, regional redevelopment plans are required to accurately reflect local characteristics and the needs of residents. However, traditional methods make it difficult to select highly relevant data from a vast amount of information and create plans that take residents' feelings into consideration. As a result, there is a challenge in proposing sustainable redevelopment plans that are tailored to local characteristics and the needs of residents.

[0836] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0837] In this invention, the server includes means for collecting local information, means for analyzing the collected information, and means for generating an AI model. This makes it possible to generate an optimal redevelopment plan based on local characteristics and to propose a plan that reflects the user's feelings.

[0838] "Means of collecting local information" refers to methods of gathering geographical, demographic, and economic information about a specific region.

[0839] "Means of analyzing collected information" refers to methods of using machine learning algorithms to analyze collected data and identify the current situation and potential challenges in the region.

[0840] "Means of identifying similar cases by referring to information on successful cases" refers to methods for identifying similar successful cases from a database of past redevelopment projects.

[0841] "Generative AI modeling" refers to artificial intelligence techniques used to automatically generate new redevelopment plans by learning from past cases.

[0842] "Means for simulating economic outcomes" refers to methods for calculating the expected economic impacts if a redevelopment plan is implemented.

[0843] "Means for assessing sustainability and optimizing environmental plans" refers to methods for analyzing the environmental impact of planned redevelopment and optimizing it in a sustainable manner.

[0844] A "scenario analysis method for predicting long-term future impacts" is a simulation method for predicting how a redevelopment plan will affect a region over time.

[0845] "An emotional engine that analyzes user emotional information and reflects it in redevelopment plans" refers to a method of analyzing user emotions and incorporating the results as feedback into redevelopment plans.

[0846] This invention is a system for generating optimal plans that reflect regional characteristics and the needs of residents in regional redevelopment. Specific embodiments are described below.

[0847] Server Role

[0848] The server handles the central processing of this system. First, it collects local information by using open databases and geographic information systems to obtain geographic, demographic, and economic data. This collected information is analyzed using machine learning libraries such as Python's Scikit-learn and TensorFlow. Based on the analysis results, it refers to successful case studies and identifies similar cases. This prepares the generative AI model to generate the optimal redevelopment plan. The server also simulates the economic impact of the generated plan and performs calculations to assess its sustainability.

[0849] Terminal role

[0850] The terminal is responsible for visualizing and presenting the generated redevelopment plan to the user. A web application is used for visualization, allowing users to intuitively understand the plan through 3D models and infographics. During this process, an emotion engine built into the terminal analyzes the user's facial expressions and voice to collect emotional information. This information is sent to a server and used to improve the plan.

[0851] User roles

[0852] Users evaluate the redevelopment plan through their devices and provide feedback based on emotional information analyzed by an emotion engine. This user feedback is used to revise and improve the plan, resulting in a redevelopment plan that better suits individual needs.

[0853] As a concrete example, when a user expresses interest in urban redevelopment and proposes a layout for public spaces, the server references past successful examples and generates an optimal layout plan. The terminal visualizes this plan, allowing the user to review each element and provide feedback. This enables effective development that leverages the unique characteristics of the region.

[0854] Example of a prompt

[0855] "Please generate a sustainable redevelopment plan for Shibuya Ward in Tokyo. Include an analysis of local resident satisfaction and provide specific proposals for the placement of public spaces."

[0856] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0857] Step 1:

[0858] The user accesses the system and enters information about the target area. This information includes the area's geographical characteristics, the purpose of redevelopment, and priority areas for improvement. The terminal sends this information to the server, which then receives the necessary initial data.

[0859] Step 2:

[0860] The server automatically collects relevant data from external databases and geographic information systems based on the received regional information. Using the input regional parameters as keys, it searches for geographical conditions, demographics, and economic indicators to construct a dataset. The resulting dataset is then generated.

[0861] Step 3:

[0862] The server uses machine learning algorithms to analyze the collected dataset. This step utilizes Python's Scikit-learn and TensorFlow libraries to identify regional challenges and potential development opportunities. The analysis results output a list of regional challenges and development opportunities.

[0863] Step 4:

[0864] The server references successful case studies and selects cases similar to the analysis results. This identifies strategies and elements that have a high success rate in redevelopment. In this step, past project data is filtered and relevant data is extracted.

[0865] Step 5:

[0866] A generative AI model is used to combine identified challenges with success stories to generate an optimal redevelopment plan. Inputs include feedback data from success stories and on-site data. The AI ​​model integrates these to output a predictive plan.

[0867] Step 6:

[0868] The terminal visualizes the generated redevelopment plan and presents it to the user as a 3D model or infographic. The output plan is accessible through the user interface, allowing the user to interactively review each part of the plan.

[0869] Step 7:

[0870] The device uses sensors to detect the user's facial expressions and voice, and its built-in emotion engine analyzes the user's emotions. The input includes emotion data acquired in real time. The analysis outputs an emotional evaluation of the user's response to the plan.

[0871] Step 8:

[0872] The server receives the user's sentiment evaluation and feedback, and uses the generative AI model again to improve the plan. The improved plan reflects the user's favorable feedback and corrects negative feedback. This results in the output of the final redevelopment plan, which is then presented to the user.

[0873] (Application Example 2)

[0874] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0875] Modern regional redevelopment projects require plans that fully reflect local characteristics and the needs of residents. However, traditional methods have made it difficult to collect and analyze detailed local information, and emotional feedback from residents has often not been quickly integrated. As a result, development plans often fail to meet residents' expectations, hindering the sustainable development of the region.

[0876] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0877] In this invention, the server includes means for collecting local information, means for identifying similar cases by referring to information on successful cases, and means for collecting emotional feedback from users using an emotion analysis engine. This enables the generation of redevelopment plans based on local characteristics and the reflection of resident feedback in real time.

[0878] "Means of collecting local information" refers to methods for collecting geographical, social, and economic data related to a specific region.

[0879] "Means of analysis" refers to methods of processing collected data using computer algorithms to clarify the current situation and problems of a region.

[0880] "Methods for identifying similar cases by referring to success stories" refers to methods for exploring past successful development cases and finding similar cases that can be applied to the current situation.

[0881] "Generative AI means" refers to a function that uses machine learning and artificial intelligence technologies to automatically generate redevelopment plans based on regional characteristics.

[0882] "Means for simulating economic impacts" refers to methods for predicting the economic outcomes of a planned redevelopment project.

[0883] "Methods for evaluating sustainability and optimizing environmental design" refers to methods for evaluating the environmental impact of redevelopment plans and optimizing environmental design to achieve a sustainable society.

[0884] "Scenario analysis methods for predicting long-term future impacts" are methods for comprehensively predicting the impact of redevelopment plans on a region in the future.

[0885] "A means of visually providing regional development plans through AR display" refers to a method of using augmented reality technology to visually present plans to residents and aid their understanding.

[0886] "Methods for collecting emotional feedback from users using an emotion analysis engine" refers to methods for analyzing emotions from users' facial expressions and voices and collecting that feedback.

[0887] This invention is a system that generates regional redevelopment plans for smart cities and incorporates resident feedback in real time. Its main components include a server, terminals, and users.

[0888] The server handles the central processing of this system. The server runs programs on high-performance computers to collect and analyze vast amounts of information about the region. These programs use machine learning algorithms and artificial intelligence models to identify areas requiring redevelopment from the local data. This enables data-driven, scientific, and logical decision-making. Based on the analysis results, a generative AI system references a database of past success stories to generate an optimal redevelopment plan tailored to the specific characteristics of the region.

[0889] The terminal provides an interface for users to review the plan and provide feedback. This terminal is envisioned to be a mobile information device such as a smartphone or tablet. Users can visually review the redevelopment plan transmitted from the server on the terminal. Augmented reality (AR) functionality is used to overlay the plan onto the actual landscape, deepening residents' understanding. Emotional feedback is collected from the user's facial expressions and voice using an emotion analysis engine and transmitted to the server.

[0890] Users are the ultimate beneficiaries of this system, evaluating the generated plans and providing feedback. Feedback based on user sentiment data is re-analyzed by the server and used to optimize the plans. This ensures that the plans are flexible and responsive to the needs and feelings of the residents.

[0891] As a concrete example, in urban redevelopment, users can view augmented reality (AR) simulations of new park designs via their smartphones, and their facial expressions can be seen, allowing for real-time evaluation of their level of satisfaction and their positive feelings towards the environment. Based on this information, plans can be revised and new ideas incorporated, resulting in community development that achieves high levels of resident satisfaction.

[0892] Examples of prompt statements for manipulating a generative AI model include:

[0893] "Regarding the park development plan for this area, please propose the optimal layout and methods for promoting community activities, drawing on past successful examples."

[0894] This prompt allows the generating AI to output specific and actionable ideas, thereby improving the quality of the plan.

[0895] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0896] Step 1:

[0897] The server collects information relevant to a region. It takes geographic data, demographic data, economic data, etc., as input and stores them in a database. Based on this information, it constructs a dataset to evaluate the current situation and challenges of the region.

[0898] Step 2:

[0899] The server analyzes the collected data. It uses the dataset constructed in Step 1 as input. Machine learning algorithms and statistical analysis techniques are applied to identify regional characteristics and potential challenges. The output consists of numerical data and graphs illustrating regional characteristics.

[0900] Step 3:

[0901] The server references successful case studies to identify similar cases. It uses analyzed regional characteristics data as input. It searches the database for similar past cases and provides reference information for formulating optimal redevelopment plans. The output is a list of selected successful cases.

[0902] Step 4:

[0903] The server generates a redevelopment plan using a generative AI method. Analysis results and a list of successful cases are input to the generative AI model along with prompts. Based on this information, the AI ​​model outputs an optimal development plan tailored to the specific characteristics of the region.

[0904] Step 5:

[0905] The terminal visualizes the generated redevelopment plan. It receives development plan data from a server as input. Using augmented reality (AR) technology, it overlays the plan onto the real-world landscape for visual display. The output is a 3D model on the terminal screen.

[0906] Step 6:

[0907] Users evaluate the plan and provide feedback through their device. The input is a visualized plan displayed on the device. An emotion analysis engine, using facial expressions and voice, analyzes the user's reactions and sends emotion feedback data to the server. The output is emotion feedback information indicating satisfaction levels and areas for improvement.

[0908] Step 7:

[0909] The server optimizes the redevelopment plan based on user feedback. It uses the sentiment feedback data collected in step 6 as input. The feedback is incorporated into the existing plan data, the plan is modified as needed, and the final redevelopment plan is generated. The output is the optimized redevelopment plan.

[0910] 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.

[0911] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0912] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0913] 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.

[0914] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0915] 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.

[0916] 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.

[0917] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0918] 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."

[0919] 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.

[0920] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0921] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0930] 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.

[0931] The following is further disclosed regarding the embodiments described above.

[0932] (Claim 1)

[0933] Means of collecting local data,

[0934] Methods for analyzing the collected data,

[0935] A means of identifying similar cases by referring to a database of successful cases,

[0936] A generation algorithm means for generating redevelopment plans based on regional characteristics,

[0937] A means of simulating the economic effects of the generated plan,

[0938] A means of evaluating sustainability and optimizing environmental design,

[0939] Scenario analysis methods for predicting long-term future impacts,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] A method for summarizing case studies obtained from a success story database using natural language processing,

[0943] A means of visualizing the generated redevelopment plan and providing it to the user,

[0944] The system according to claim 1, further comprising:

[0945] (Claim 3)

[0946] A means of receiving user feedback and incorporating that feedback into revisions to the redevelopment plan,

[0947] A means of presenting the optimal strategy based on the results of long-term scenario analysis,

[0948] The system according to claim 1, further comprising:

[0949] "Example 1"

[0950] (Claim 1)

[0951] Means of collecting local information,

[0952] Means for analyzing the collected information,

[0953] A means of identifying similar cases by referring to records of successful cases,

[0954] An algorithmic means for generating development plans based on regional characteristics,

[0955] A means of simulating the economic effects of the generated plan,

[0956] A means of evaluating sustainability and optimizing environmental design,

[0957] A user interface means for selecting the optimal plan from multiple plan options,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] A method for summarizing case studies obtained from success story records using natural language processing,

[0961] A means of visualizing the generated development plan and providing it to the user,

[0962] The system according to claim 1, further comprising:

[0963] (Claim 3)

[0964] A means of receiving feedback from users and reflecting that feedback in revising the development plan,

[0965] A means of proposing optimal measures based on the results of detailed future forecast analysis,

[0966] The system according to claim 1, further comprising:

[0967] "Application Example 1"

[0968] (Claim 1)

[0969] Means of collecting information about the region,

[0970] The means of analyzing the collected information,

[0971] A means of identifying similar cases by referring to a database of reference cases,

[0972] A generation algorithm means for generating redevelopment plans based on regional characteristics,

[0973] A means of simulating the economic effects of the generated plan,

[0974] A means of evaluating sustainability and optimizing environmental design,

[0975] A means of visualizing redevelopment plans based on regional characteristics using augmented reality technology,

[0976] Scenario analysis methods for predicting long-term future impacts,

[0977] A system that includes this.

[0978] (Claim 2)

[0979] A method for summarizing case studies obtained from a success story database using natural language processing,

[0980] A means of visualizing the generated redevelopment plan using augmented reality technology in order to provide it to the user,

[0981] The system according to claim 1, further comprising:

[0982] (Claim 3)

[0983] A means of collecting user feedback and incorporating that feedback into revisions to the redevelopment plan,

[0984] A means of presenting the optimal strategy based on the results of long-term scenario analysis,

[0985] The system according to claim 1, further comprising:

[0986] "Example 2 of combining an emotion engine"

[0987] (Claim 1)

[0988] Means of collecting local information,

[0989] Means for analyzing the collected information,

[0990] A means of identifying similar cases by referring to information on successful cases,

[0991] A generative AI model means for generating redevelopment plans based on regional characteristics,

[0992] A means of simulating the economic results of the generated plan,

[0993] A means of evaluating sustainability and optimizing environmental plans,

[0994] A scenario analysis method for predicting long-term future impacts,

[0995] An emotion engine that analyzes user emotional information and reflects it in the redevelopment plan,

[0996] A system that includes this.

[0997] (Claim 2)

[0998] A method for summarizing case studies obtained from success story information using natural language processing,

[0999] A means of visualizing the generated redevelopment plan and providing it to the user,

[1000] The system according to claim 1, further comprising:

[1001] (Claim 3)

[1002] A means of receiving user feedback and incorporating that feedback into revisions to the redevelopment plan,

[1003] A means to analyze user sentiment data and improve the effectiveness of the plan,

[1004] The system according to claim 1, further comprising:

[1005] "Application example 2 when combining with an emotional engine"

[1006] (Claim 1)

[1007] Means of collecting local information,

[1008] Means for analyzing the collected information,

[1009] A means of identifying similar cases by referring to information on successful cases,

[1010] A generative AI means for generating redevelopment plans based on regional characteristics,

[1011] A means of simulating the economic impact of the generated plan,

[1012] A means of evaluating sustainability and optimizing environmental design,

[1013] Scenario analysis methods for predicting long-term future impacts,

[1014] A means of visually providing regional development plans through AR display,

[1015] A means of collecting users' emotional feedback using an emotion analysis engine,

[1016] A system that includes this.

[1017] (Claim 2)

[1018] A method for summarizing information obtained from success stories using natural language processing,

[1019] A means of visualizing the generated redevelopment plan and providing it to users,

[1020] A means of dynamically adjusting the plan by integrating the announcement and the emotional feedback of the user,

[1021] The system according to claim 1, further comprising:

[1022] (Claim 3)

[1023] A means of receiving feedback from users and incorporating that feedback into revisions to the redevelopment plan,

[1024] A means of presenting the optimal strategy based on the results of long-term scenario analysis,

[1025] A means to strengthen regional development plans using success story information and generative AI models,

[1026] The system according to claim 1, further comprising: [Explanation of Symbols]

[1027] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting information about the region, The means of analyzing the collected information, A means of identifying similar cases by referring to a database of reference cases, A generation algorithm means for generating redevelopment plans based on regional characteristics, A means of simulating the economic effects of the generated plan, A means of evaluating sustainability and optimizing environmental design, A means of visualizing redevelopment plans based on regional characteristics using augmented reality technology, Scenario analysis methods for predicting long-term future impacts, A system that includes this.

2. A method for summarizing case studies obtained from a success story database using natural language processing, A means of visualizing the generated redevelopment plan using augmented reality technology in order to provide it to the user, The system according to claim 1, further comprising:

3. A means of collecting user feedback and incorporating that feedback into revisions to the redevelopment plan, A means of presenting the optimal strategy based on the results of long-term scenario analysis, The system according to claim 1, further comprising:

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