system

Abstract

We provide the system. [Solution] A means for generating data in a format that can be output by a 3D printing device based on the generated design proposal, A means for constructing a generative artificial intelligence model that predicts economic effects, A means of generating and visually presenting design proposals, A means for integrating and pre-processing the collected data, A means of setting up different simulation scenarios and comparing the results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the spread of urban sports, the demand for facilities has been increasing. However, there is a problem that it is difficult to make an investment decision because it is difficult to predict demand and economic effects. Also, in the conventional facility design process, it is difficult to specifically visualize a design plan and obtain prompt feedback, which may hinder the smooth progress of a project.

Means for Solving the Problems

[0005] This invention provides a facility design and simulation system using generative AI, thereby supporting investment decisions through demand forecasting and economic impact simulations. By visually presenting design proposals generated based on collected integrated data and generating data in a format printable by a 3D printer, it is possible to quickly obtain concrete feedback. Furthermore, a function to compare different simulation scenarios supports more effective decision-making.

[0006] "Generated design proposals" refer to specific suggestions regarding the structure and layout of urban sports facilities, created using a generative AI model.

[0007] A "three-dimensional printing device" is a device used to convert digital designs into physical three-dimensional objects.

[0008] A "generative artificial intelligence model" is an AI program that uses machine learning algorithms to perform predictions and design.

[0009] "Economic effect" refers to the impact that facility investment has on the local economy, and specifically includes an increase in tourists, job creation, and expansion of local consumption.

[0010] "To present visually" means to show information or design proposals to users through visual media (including graphics and renderings).

[0011] "Collected data" refers to various datasets such as demographic data, economic indicators, and facility usage data collected for facility design and simulation.

[0012] "Preprocessing" refers to the process of converting collected data into a format that can be used by AI models, and performing data cleansing and normalization.

[0013] A "simulation scenario" is a method of creating multiple hypothetical situations by changing the conditions and parameters being predicted. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference numeral 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.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is implemented in a computer system for supporting the design and commercialization of urban sports facilities. The system consists of multiple components that work together to forecast facility demand, analyze economic impact, generate facility design proposals, and visualize the results. This enables local governments and developers to make more informed decisions.

[0036] The server first collects multiple data sources, including demographic data, tourist trends, and usage information for existing facilities. Next, it performs data cleaning and normalization on the collected data to prepare it for use by the model. Based on this clean dataset, a generative AI model is built to predict the demand for urban sports facilities in each region.

[0037] When simulating the economic effects of facility investments, the server utilizes a generated AI model to analyze the impact on increasing tourism to the region and creating jobs. This allows users to evaluate future economic benefits and conduct concrete investment evaluations. The results are presented to the user via a terminal, and an interface is provided for comparing different scenarios.

[0038] In generating facility design proposals, the server uses AI to create designs that take into account user needs, regional characteristics, and environmental conditions. These design proposals are visually presented to the user on a terminal, allowing for easy review and evaluation. Once the design proposal is finalized, the server converts it into a data format suitable for output on a 3D printer (e.g., STL format).

[0039] As a concrete example, consider a case where a local government is considering building a new skateboard park. The user inputs data tailored to the characteristics of the region into the system via a terminal. Based on this information, the server predicts demand and economic effects, and generates and presents a design proposal. The user then provides feedback on the design proposal, downloads the final design as data for a 3D printing device, and makes a decision on construction. In this way, the system supports consensus building among stakeholders and ensures smooth project progress.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server automatically collects data such as demographic trends, tourism data, and utilization rates of existing facilities from various data sources, and stores this data in an integrated database.

[0043] Step 2:

[0044] The server detects errors and missing data from the collected data, performs cleansing, and converts it into a format usable by the AI ​​model through a normalization process.

[0045] Step 3:

[0046] The server uses a machine learning model based on the formatted data to perform regional facility demand forecasts and outputs the forecast results.

[0047] Step 4:

[0048] The terminal instructs the user to perform an economic impact simulation based on the demand forecast results and according to the simulation scenario set by the user.

[0049] Step 5:

[0050] The server runs an AI model based on the given simulation conditions, analyzes the potential impact on the local economy, and generates results.

[0051] Step 6:

[0052] The terminal visually presents simulation results to the user in graphs and charts, and provides an interface for comparing the results of multiple scenarios.

[0053] Step 7:

[0054] The server uses AI generation based on design criteria selected by the user to create a design proposal for the facility.

[0055] Step 8:

[0056] The device provides functionality to visualize the generated design proposals on a user interface, enabling review and feedback.

[0057] Step 9:

[0058] The server converts the approved design proposals into a format that can be output by a 3D printer and provides a download link for the data via the terminal.

[0059] (Example 1)

[0060] 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."

[0061] In recent years, while the demand for sports facilities in urban areas has been increasing, there is a need for facility designs that are appropriate for the region and for accurate predictions of economic effects. However, conventional methods were time-consuming to collect and analyze data, lacking efficiency. Furthermore, it was difficult to visually grasp the predicted economic effects, leading to problems with the quality of decision-making.

[0062] 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.

[0063] In this invention, the server includes means for generating information in a format that can be output by a 3D printing device based on the generated design outline, means for constructing a generative artificial intelligence model that predicts economic impact, and means for generating and visually presenting the design outline. This enables rapid and accurate prediction of the economic effects of sports facilities suitable for a region, and allows for intuitive evaluation and revision of design proposals.

[0064] A "generative artificial intelligence model" is an artificial intelligence technology that generates new information and predictions based on collected data.

[0065] A "3D printing device" is a device that outputs digital designs as physical three-dimensional objects.

[0066] "Economic impact" refers to the total effect that a particular region or project has on the economy.

[0067] A "design overview" is a description of the outline of the plan for a physical space, such as a sports facility.

[0068] "Presenting visually" is a method of expressing information clearly through visual means to facilitate understanding.

[0069] An "information processing system" is a system designed to collect, analyze, store, and process data.

[0070] A "user terminal" is an electronic device used by a user to access information and functions.

[0071] This invention is an information processing system for supporting the design and commercialization of sports facilities in urban areas. This system primarily consists of three components: a server, terminals, and users.

[0072] The server first collects relevant information from various data sources. This data includes demographics, tourist trends, and usage information for existing facilities. This data is stored in a database and undergoes data cleaning and normalization so that it can be used by generative AI models. Data cleaning corrects inconsistencies and missing information and standardizes the format.

[0073] Based on the collected and processed data, the server utilizes a generated AI model to predict facility demand and economic impact for each region. Specifically, the AI ​​model simulates the impact of increased tourism and the local economy. For example, by inputting a prompt such as, "Please output the estimated annual usage of a skateboard park in a city with a population of 200,000," the server will perform demand forecasting and generate design proposals.

[0074] The generated design proposal is sent from the server to the terminal and visualized. The terminal uses 3D modeling software to present the design proposal to the user in three dimensions, allowing the user to intuitively review and evaluate the plan. The user can evaluate the visualized design proposal and provide feedback.

[0075] Furthermore, after the user confirms the final design, the server converts the design into a format that can be output by a 3D printing device, such as STL format. This process makes it possible to output the facility design in a physical form in a specific format and utilize it in actual construction.

[0076] This system allows users to efficiently design sports facilities optimized for their region and predict their economic impact, supporting quick and accurate decision-making.

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

[0078] Step 1:

[0079] The server collects demographic data, tourist trends, and information on the use of existing facilities from diverse data sources. Input consists of raw data obtained through APIs and database connections. This data is stored in a cloud database for future analysis.

[0080] Step 2:

[0081] The server performs data cleaning and normalization on the collected data. Data cleaning involves imputing missing values ​​and removing outliers. Normalization unifies the data format and ensures consistency. This results in a standardized and clean dataset.

[0082] Step 3:

[0083] The server uses a generative AI model to forecast demand for urban sports facilities based on a clean dataset. The model is input with population growth and tourism data, and outputs predictive data through regression analysis and model learning using this data.

[0084] Step 4:

[0085] The server uses a generated AI model to simulate the economic effects of facility construction. It inputs regional economic indicators and previously predicted demand data to evaluate the potential increase in tourists to the region and job creation. As a result, it outputs a detailed economic impact report.

[0086] Step 5:

[0087] The server utilizes AI technology to generate facility design proposals that take into account user needs and regional characteristics. User profiles and environmental conditions are used as input data necessary for the design. The generated design proposals are output as 3D model data.

[0088] Step 6:

[0089] The terminal visually presents the design proposals received from the server. Using 3D modeling software, it displays 3D model data in a way that is easily understood visually by humans. Users can then evaluate the visualized design proposals.

[0090] Step 7:

[0091] Users evaluate design proposals and provide feedback via their terminals. The server receives this feedback and revises the design proposals. The final design proposal is converted to STL format, suitable for output on a 3D printing machine, and provided to the user.

[0092] (Application Example 1)

[0093] 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."

[0094] In designing virtual stores, there is a challenge in efficiently generating and visualizing optimal designs by reflecting customer behavior data in real time. In particular, there is a lack of tools for quickly and effectively evaluating and providing feedback on design proposals in three-dimensional space.

[0095] 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.

[0096] In this invention, the server includes means for generating data in a format that can be output by a three-dimensional printing device based on the generated design proposals, means for constructing a generative artificial intelligence model that predicts economic effects, and means for visualizing the design proposals on a display device in three-dimensional space in real time and optimizing the design based on customer behavior data. This enables users to effectively evaluate and quickly optimize virtual store design proposals.

[0097] "A means of generating data in a format that can be output by a 3D printing device based on the generated design proposal" refers to the process of converting digital data into a format that can be output as a physical model in order to visualize the design proposal of a virtual store in three dimensions.

[0098] "A means of constructing a generative artificial intelligence model to predict economic effects" refers to a method of simulating and predicting the economic impact of virtual store design using AI technology.

[0099] "A means of generating and visually presenting design proposals" refers to a technology that uses digital tools to create a virtual design of a store and visually displays the results to the user.

[0100] "Means for integrating and pre-processing collected data" refers to the process of aggregating data obtained from various sources and preparing it in a format suitable for analysis and model building.

[0101] "Methods for setting different simulation scenarios and comparing the results" refers to techniques for performing simulations based on multiple design hypotheses, comparing the results side-by-side, and evaluating optimal performance.

[0102] "A means of visualizing design proposals on a display device in three-dimensional space in real time and optimizing the design based on customer behavior data" refers to a technology that uses a visual device to display design proposals in three-dimensional space, and dynamically modifies and optimizes the design by reflecting actual customer data.

[0103] The "function that allows users to evaluate design proposals using a display device and provide feedback through operation" is an interface that allows users to view visualized virtual designs through a device and provide interactive feedback.

[0104] The system for realizing this invention incorporates advanced technology to assist in the design and optimization of virtual stores. The server first collects and integrates customer behavior data, demographics, and market trends. Next, it cleans and preprocesses this data into a format usable by the generating AI model. This allows for the construction of an AI model that predicts the economic impact of the virtual store and optimizes the customer experience.

[0105] The server generates store design proposals in three-dimensional space and presents them visually using display devices such as Oculus Quest and Microsoft HoloLens®. Based on the displayed design, users provide direct feedback, which is reflected in the system in real time. Through this process, the store design proposals are continuously optimized and converted into appropriate data formats for output on a 3D printer.

[0106] As a concrete example, consider a clothing store designing a virtual store layout. The user wears smart glasses and walks around the virtual store, checking the placement of products and customer traffic. The server collects customer movement and eye-tracking information and uses a generated AI model to suggest the optimal layout. An example of a prompt used in this process would be: "Based on the latest customer traffic data, generate a design plan to optimize the virtual store layout for the clothing store. Pay particular attention to the placement around the cash registers and the back of the shelves."

[0107] Such a system allows users to repeatedly evaluate designs within a virtual environment and efficiently determine the optimal store layout. As a result, it promotes data-driven decision-making based on real-time information.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server collects customer behavior data, demographic data, and market trend data. This includes acquiring data streams in real time from sensor devices and online platforms. This collected data is used as input data for generative AI models.

[0111] Step 2:

[0112] The server cleans and preprocesses the collected data, including imputing missing values, scaling, and denoising. The cleaned data is in a standardized format, contributing to improved accuracy of the AI ​​model. The output of this step becomes the input for the next AI model building step.

[0113] Step 3:

[0114] The server builds a generative AI model using a clean dataset. This model is optimized to predict the economic impact of a store and customer flow. In this process, a learning algorithm adjusts the model parameters to generate the optimal response. The generated AI model is used to generate design proposals.

[0115] Step 4:

[0116] The server uses a generative AI model to create design proposals for a virtual store. Considering the conditions and constraints based on the prompts, the model proposes the optimal layout and design. The server then prepares to output these generated design proposals in a 3D format.

[0117] Step 5:

[0118] The server enables visualization in three-dimensional space based on the generated design proposals. The design proposals are sent to display devices such as Oculus Quest and Microsoft HoloLens, and presented to the user as 3D visualizations. This visualization allows for a concrete experience of the design proposals.

[0119] Step 6:

[0120] Users explore a visualized virtual store and provide feedback as they move around. User actions and eye-tracking information are sent to a server, which then generates new data.

[0121] Step 7:

[0122] The server continuously optimizes itself based on user feedback. By combining the AI ​​model's predictions with user feedback, it evolves into a more accurate design proposal. Ultimately, the output design data provides the optimal store layout that aligns with the user's intentions.

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

[0124] This invention enables a more sophisticated decision-making process by integrating an emotion engine into a computer system that supports the design and commercialization of urban sports facilities. The system includes, as its main components, a data collection and pre-processing module, a demand forecasting and economic impact forecasting module, a design proposal generation module, an emotion engine, and a user interface.

[0125] The server first collects demographic data, tourism data, and usage information of existing facilities from various data sources and integrates them into a database. Next, it cleanses the collected data and converts it into a format usable by AI models through a normalization process.

[0126] Subsequently, the server builds a generative AI model, predicts facility demand for each region, and generates design proposals based on the predicted demand.

[0127] For the generated design proposals, the server recognizes the user's emotions through an emotion engine and evaluates the user's response. By analyzing the feedback and responses provided by the user using their device, the emotion engine extracts areas for improvement in the design proposal and automatically incorporates them into the design. This process results in a more convincing design proposal that can meet the user's latent needs.

[0128] After the above process is completed and the design proposal is finalized, the server converts the data into a format that can be output by a 3D printer (e.g., STL format) and presents a download link to the user via the terminal. The user can then use this data to create a physical model.

[0129] As a concrete example, consider a case where a user evaluates a skateboard park design proposal via their device. When the user reviews a new design, the emotion engine analyzes the user's facial expressions and keystrokes, prioritizing design elements that elicit positive reactions. Based on these positive reactions, the design proposal is refined and the final version is presented, facilitating smoother coordination with all parties involved.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server automatically collects a wide variety of data from external data sources, such as demographic data, tourism data, and usage status of existing facilities, and stores it in an integrated database.

[0133] Step 2:

[0134] The server cleanses the collected data, removes outliers, and supplements missing data, converting it into a format that can be analyzed by the AI ​​model.

[0135] Step 3:

[0136] The server builds a generative AI model based on the formatted data and predicts the demand for urban sports facilities in each region.

[0137] Step 4:

[0138] The server uses AI to generate facility design proposals based on predicted demand. These design proposals take into account user needs and regional characteristics.

[0139] Step 5:

[0140] The device visually presents the generated design proposals to the user via a user interface, prompting them to evaluate them.

[0141] Step 6:

[0142] The emotion engine uses the device to collect user facial expression data and reactions in real time and analyzes the user's emotional state.

[0143] Step 7:

[0144] The server analyzes feedback from the emotion engine, prioritizes design elements that elicit positive responses, and refines the design proposal.

[0145] Step 8:

[0146] The device will then present the improved design proposal to the user again for final evaluation and approval.

[0147] Step 9:

[0148] The server converts the approved design proposals into a data format that can be output by a 3D printer and provides the user with a download link via the terminal.

[0149] Step 10:

[0150] Users can download the data from the provided link and create a physical model using a 3D printer.

[0151] (Example 2)

[0152] 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".

[0153] In designing modern urban sports facilities, it is difficult to efficiently formulate design proposals that accurately reflect local characteristics and user needs, and a high degree of accuracy is required in predicting the economic effects and demand of the design. Furthermore, traditional methods make it difficult to appropriately reflect users' emotions and opinions, often resulting in dissatisfaction or shortcomings in the final design proposal. There is a need to solve these problems and provide a more effective and convincing design process.

[0154] 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.

[0155] In this invention, the server includes means for integrating and normalizing data from various collected information sources to convert it into a form usable by a generative artificial intelligence model; means for predicting facility demand in each region using the generative artificial intelligence model and generating design proposals based on the results; and means including an emotion analysis device for analyzing user input information and emotional responses and improving the design proposals. This makes it possible to quickly formulate design proposals that accurately reflect user needs and regional characteristics, further improve the design proposals by incorporating user emotional responses, and present them in the most optimal form.

[0156] "Integrating and normalizing data from diverse sources" is the process of centrally combining information obtained from multiple different data sources and arranging it into a consistent format through statistical processing and transformation.

[0157] "Predicting regional facility demand using generative artificial intelligence models" refers to a technique that analyzes and estimates future facility usage demand by applying machine learning and data analysis technologies based on regional characteristics and historical data.

[0158] "Including an emotion analysis device that analyzes user input information and emotional responses to improve design proposals" means that the system is equipped with an analysis device that analyzes emotional feedback and directly entered opinions provided by users and uses that data to make modifications and improvements to design proposals.

[0159] "Converting to a data format that can be output by a 3D printing device" refers to the process of changing a digital design proposal into a digital format (e.g., STL format) that can be understood by a 3D printer or other output device, and then saving it, in order to manufacture it as a physical model.

[0160] "Providing a function that allows users to evaluate design proposals via a terminal and automatically generates revised proposals based on those evaluations" means a system that has the ability to collect feedback on design proposals through a user interface and automatically propose revised proposals that reflect that feedback.

[0161] This invention provides a configuration for a computer system integrating an emotion engine to support the design and commercialization of urban sports facilities. Through data collection, integration, and analysis, this system automatically generates sports facility designs that meet user needs, supporting efficient decision-making.

[0162] The server retrieves demographic data, tourism data, and usage information for existing facilities from diverse sources. This data is collected using API calls and database queries and integrated into a database. Next, the server uses the Python Pandas library to cleanse and normalize the data, thereby converting it into a format usable by AI models.

[0163] The generative AI model is built using machine learning frameworks such as TENSORFLOW® or PyTorch. This model predicts facility demand for each region and creates appropriate design proposals based on the collected data. The generated design proposals are visually presented on the terminal through the user interface.

[0164] Users evaluate design proposals using a terminal, and input information from the terminal and emotional responses detected by the emotion engine are sent to the server. The emotion engine analyzes the user's facial expressions and input operations, and uses this data to improve the design proposals. Design elements that receive many positive responses are retained, and improvement suggestions are automatically provided.

[0165] Finally, the server converts the improved design into a format that can be printed on a 3D printer (e.g., STL format) and provides it to the user via the terminal. The user can then use this data to create a physical model.

[0166] As a concrete example, consider a case where a user evaluates a design proposal for a skateboard park. In this case, an example of a prompt message might be, "Based on the latest demographic and tourism data, forecast the demand for sports facilities and generate an appropriate design proposal." This system automates the entire process from design evaluation to improvement, facilitating smooth coordination among stakeholders.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server collects demographic data, tourism data, and usage information for existing facilities from various information sources. Specifically, it retrieves data using Web APIs and imports local files, and stores this data in a database. The input is raw data from diverse information sources, and the output is an integrated database. This integrated data forms the basis for subsequent analysis.

[0170] Step 2:

[0171] The server performs data cleansing on the collected data. Specifically, it uses the Python Pandas library to detect and correct missing and outlier values, and remove duplicate data. The input for this step is the raw data obtained in step 1, and the output is a cleansed, consistent dataset. This improves the quality of the data and enables more accurate predictions.

[0172] Step 3:

[0173] The server normalizes the cleansed data and transforms it so that it can be used by the AI ​​model. Specifically, it scales and encodes the data to prepare the dataset for the model's input format. The input is the formatted data from step 2, and the output is the standardized data for the AI ​​model input. This normalized data improves the training and prediction accuracy of the AI ​​model.

[0174] Step 4:

[0175] The server performs demand forecasting using a generative AI model. Using TensorFlow or PyTorch, it processes normalized data to predict facility demand for each region. The input is the data prepared in step 3, and the output is the result of the demand forecast. Based on this forecast, the server is ready to proceed with design proposal generation.

[0176] Step 5:

[0177] The server generates design proposals based on demand forecasts. The design proposal generation module analyzes the output of the AI ​​model and creates facility design proposals that reflect user needs and regional characteristics. The input is demand forecast data, and the output is an initial design proposal. The generated proposals are then prepared for visual presentation.

[0178] Step 6:

[0179] The terminal visually presents the generated design proposal to the user. The user reviews the design proposal on the screen and provides feedback through the interface. The input is design proposal data from the server, and the output is user evaluation data. This allows the user to express their opinion on the design.

[0180] Step 7:

[0181] The server utilizes an emotion engine to analyze user feedback and derive improvements to the design proposal. The emotion engine analyzes user emotional responses and keystrokes to identify positive response elements. Input is user feedback and analysis information, and output is a design proposal including improvements. This process enables proposals that better meet user expectations.

[0182] Step 8:

[0183] The server compiles the improved design proposal into a final format and converts it into a format that can be output by a 3D printer. Specifically, it exports the design proposal data to formats such as STL. The input is the finalized design proposal, and the output is a file for 3D printing. With data in this format, the system is ready to create a physical prototype.

[0184] Step 9:

[0185] The terminal provides the user with a download link for the generated 3D data. The user can download the data via the link and proceed with 3D printing or model making. The input is the 3D data from the server, and the output is the download link and the data provided to the user.

[0186] (Application Example 2)

[0187] 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".

[0188] In modern store design, there is a demand for designs that accurately reflect customer needs. However, traditional design processes fail to effectively utilize user emotions and reactions, resulting in a challenge in achieving designs that are optimal for market demand and customer expectations. Furthermore, predicting the number of visitors to a store and improving its economic impact remains a difficult challenge.

[0189] 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.

[0190] In this invention, the server includes means for generating data in a format that can be output by a three-dimensional printing device based on the generated design proposal, means for constructing a generative artificial intelligence model that predicts economic effects, and means for analyzing user emotions and reflecting them in improving the design proposal. This makes it possible to reflect customer emotions in real time in store design and provide optimized design proposals. Furthermore, it is possible to simulate an increase in the number of visitors and improve economic effects.

[0191] "Means for generating data" refers to a method that has the function of converting the generated design proposal into data in a format that can be output by a three-dimensional printing device.

[0192] A "generative artificial intelligence model" is an intelligent system that uses machine learning algorithms to predict economic effects and calculates the performance of a facility under specific conditions.

[0193] "Means of generating and visually presenting design proposals" refers to the process of creating a concrete and visually understandable design prototype for the user and displaying it on a screen.

[0194] "Means for integrating and pre-processing collected data" refers to methods for organizing and integrating data obtained from various sources to make it analyzable.

[0195] "Methods for setting up different simulation scenarios and comparing the results" refers to methods for preparing multiple hypotheses and environmental conditions, and analyzing and evaluating the performance in each scenario.

[0196] "A means of analyzing user emotions and reflecting them in improving design proposals" refers to a system that analyzes user emotions in real time and adaptively improves the design based on that analysis.

[0197] "Means of acquiring evaluation data and adjusting design proposals" refers to the process of receiving user feedback and adjusting the design based on that feedback.

[0198] The embodiments for carrying out the invention are shown below.

[0199] In the system based on this invention, the server first collects demographic data, tourism data, and usage information of existing facilities from various data sources to form an integrated database. Before being stored in the database, the collected data undergoes a cleansing and normalization process to convert it into a format suitable for analysis.

[0200] The server then uses a generative artificial intelligence model to predict facility demand for each region and generates design proposals based on this. These design proposals are converted into a format that can be output by a 3D printer and presented visually through the user's terminal. The server incorporates an emotion engine to capture the user's emotions as a reaction to the design proposals, and has the ability to analyze emotions in real time through the user's facial expressions and actions. Based on this emotion analysis, the server automatically improves the design and provides the user with an optimized design proposal.

[0201] In this system, for example, a user visually evaluates a new interior design for a cafe. If the evaluation is accompanied by positive emotions, it indicates that design elements such as glass walls and the extensive use of plants are well-received by the user. This information is immediately reflected in improvements to the next design proposal. The entire operation on the terminal is supported by an interface that provides a comfortable user experience.

[0202] The hardware used includes computer servers to support data collection and analysis, and smart devices for users to visually review designs. The software includes image processing libraries such as OpenCV and sentiment analysis libraries such as DeepFace for emotion recognition.

[0203] A possible example of a specific prompt message would be something like, "We are considering interior design proposals for a new cafe. Please generate new proposals incorporating positive design elements that users have previously found appealing."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server collects information from various data sources, including demographic data, tourism data, and usage information for existing facilities. It uses external databases and APIs as input and stores the collected data as an initial dataset. This data serves as the foundation for integrating the information necessary for subsequent processing.

[0207] Step 2:

[0208] The server performs cleansing and normalization processes on the collected data. The input is the initial dataset obtained in step 1, and by processing it such as imputing missing values, removing outliers, and matching data types, it generates a clean, normalized dataset suitable for analysis as output. This dataset is used for training AI models and demand forecasting in the system.

[0209] Step 3:

[0210] The server uses a generative artificial intelligence model to predict facility demand in each region based on a clean dataset. The input is a normalized dataset, which is fed into the AI ​​model to execute the prediction algorithm, producing predicted values ​​for facility demand as output. These predicted values ​​are used to generate design proposals.

[0211] Step 4:

[0212] The server generates design proposals based on predicted facility demand and converts them into a data format for visual presentation on a terminal. Predicted facility demand is used as input, and a 3D modeling tool is used to generate output data that can serve as a visual design proposal. This data is then sent to a smart device for user evaluation.

[0213] Step 5:

[0214] The user visually evaluates the design proposals sent via their device. The input is a 3D design proposal sent from the server, and the emotion recognition system monitors the user's gaze, facial expressions, and movements in real time. This results in the output of user emotion data.

[0215] Step 6:

[0216] The server evaluates the design proposal based on the obtained user emotion data and automatically incorporates improvements to the design. User emotion response data is used as input, and an emotion analysis algorithm is applied to generate an optimized new design proposal as output. This new design proposal is then put into a further evaluation and improvement cycle.

[0217] 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.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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 shown 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is implemented in a computer system for supporting the design and commercialization of urban sports facilities. The system consists of multiple components that work together to forecast facility demand, analyze economic impact, generate facility design proposals, and visualize the results. This enables local governments and developers to make more informed decisions.

[0234] The server first collects multiple data sources, including demographic data, tourist trends, and usage information for existing facilities. Next, it performs data cleaning and normalization on the collected data to prepare it for use by the model. Based on this clean dataset, a generative AI model is built to predict the demand for urban sports facilities in each region.

[0235] When simulating the economic effects of facility investments, the server utilizes a generated AI model to analyze the impact on increasing tourism to the region and creating jobs. This allows users to evaluate future economic benefits and conduct concrete investment evaluations. The results are presented to the user via a terminal, and an interface is provided for comparing different scenarios.

[0236] In generating facility design proposals, the server uses AI to create designs that take into account user needs, regional characteristics, and environmental conditions. These design proposals are visually presented to the user on a terminal, allowing for easy review and evaluation. Once the design proposal is finalized, the server converts it into a data format suitable for output on a 3D printer (e.g., STL format).

[0237] As a concrete example, consider a case where a local government is considering building a new skateboard park. The user inputs data tailored to the characteristics of the region into the system via a terminal. Based on this information, the server predicts demand and economic effects, and generates and presents a design proposal. The user then provides feedback on the design proposal, downloads the final design as data for a 3D printing device, and makes a decision on construction. In this way, the system supports consensus building among stakeholders and ensures smooth project progress.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The server automatically collects data such as demographic trends, tourism data, and utilization rates of existing facilities from various data sources, and stores this data in an integrated database.

[0241] Step 2:

[0242] The server detects errors and missing data from the collected data, performs cleansing, and converts it into a format usable by the AI ​​model through a normalization process.

[0243] Step 3:

[0244] The server uses a machine learning model based on the formatted data to perform regional facility demand forecasts and outputs the forecast results.

[0245] Step 4:

[0246] The terminal instructs the user to perform an economic impact simulation based on the demand forecast results and according to the simulation scenario set by the user.

[0247] Step 5:

[0248] The server runs an AI model based on the given simulation conditions, analyzes the potential impact on the local economy, and generates results.

[0249] Step 6:

[0250] The terminal visually presents simulation results to the user in graphs and charts, and provides an interface for comparing the results of multiple scenarios.

[0251] Step 7:

[0252] The server uses AI generation based on design criteria selected by the user to create a design proposal for the facility.

[0253] Step 8:

[0254] The device provides functionality to visualize the generated design proposals on a user interface, enabling review and feedback.

[0255] Step 9:

[0256] The server converts the approved design proposals into a format that can be output by a 3D printer and provides a download link for the data via the terminal.

[0257] (Example 1)

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

[0259] In recent years, while the demand for sports facilities in urban areas has been increasing, there is a need for facility designs that are appropriate for the region and for accurate predictions of economic effects. However, conventional methods were time-consuming to collect and analyze data, lacking efficiency. Furthermore, it was difficult to visually grasp the predicted economic effects, leading to problems with the quality of decision-making.

[0260] 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.

[0261] In this invention, the server includes means for generating information in a format that can be output by a 3D printing device based on the generated design outline, means for constructing a generative artificial intelligence model that predicts economic impact, and means for generating and visually presenting the design outline. This enables rapid and accurate prediction of the economic effects of sports facilities suitable for a region, and allows for intuitive evaluation and revision of design proposals.

[0262] A "generative artificial intelligence model" is an artificial intelligence technology that generates new information and predictions based on collected data.

[0263] A "3D printing device" is a device that outputs digital designs as physical three-dimensional objects.

[0264] "Economic impact" refers to the total effect that a particular region or project has on the economy.

[0265] A "design overview" is a description of the outline of the plan for a physical space, such as a sports facility.

[0266] "Presenting visually" is a method of expressing information clearly through visual means to facilitate understanding.

[0267] An "information processing system" is a system designed to collect, analyze, store, and process data.

[0268] A "user terminal" is an electronic device used by a user to access information and functions.

[0269] This invention is an information processing system for supporting the design and commercialization of sports facilities in urban areas. This system primarily consists of three components: a server, terminals, and users.

[0270] The server first collects relevant information from various data sources. This data includes demographics, tourist trends, and usage information for existing facilities. This data is stored in a database and undergoes data cleaning and normalization so that it can be used by generative AI models. Data cleaning corrects inconsistencies and missing information and standardizes the format.

[0271] Based on the collected and processed data, the server utilizes a generated AI model to predict facility demand and economic impact for each region. Specifically, the AI ​​model simulates the impact of increased tourism and the local economy. For example, by inputting a prompt such as, "Please output the estimated annual usage of a skateboard park in a city with a population of 200,000," the server will perform demand forecasting and generate design proposals.

[0272] The generated design proposal is sent from the server to the terminal and visualized. The terminal uses 3D modeling software to present the design proposal to the user in three dimensions, allowing the user to intuitively review and evaluate the plan. The user can evaluate the visualized design proposal and provide feedback.

[0273] Furthermore, after the user confirms the final design, the server converts the design into a format that can be output by a 3D printing device, such as STL format. This process makes it possible to output the facility design in a physical form in a specific format and utilize it in actual construction.

[0274] This system allows users to efficiently design sports facilities optimized for their region and predict their economic impact, supporting quick and accurate decision-making.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The server collects demographic data, tourist trends, and information on the use of existing facilities from diverse data sources. Input consists of raw data obtained through APIs and database connections. This data is stored in a cloud database for future analysis.

[0278] Step 2:

[0279] The server performs data cleaning and normalization on the collected data. Data cleaning involves imputing missing values ​​and removing outliers. Normalization unifies the data format and ensures consistency. This results in a standardized and clean dataset.

[0280] Step 3:

[0281] The server uses a generative AI model to predict the demand for urban sports facilities based on a clean dataset. Population growth and tourism data are input into the model, and through regression analysis and model learning using these data, prediction data is output.

[0282] Step 4:

[0283] The server uses a generative AI model to simulate the economic effects of facility construction. Regional economic indicators and the previously predicted demand data are input, and an assessment of the increase in tourists and job creation in the region is carried out. As a result, a detailed economic effect report is output.

[0284] Step 5:

[0285] The server utilizes AI technology to generate facility design proposals considering user needs and regional characteristics. User profiles and environmental conditions are used as input data required for the design. The generated design proposals are output as 3D model data.

[0286] Step 6:

[0287] The terminal visually presents the design proposals received from the server. Using 3D modeling software, the 3D model data is displayed in a form that is easy for humans to visually understand. The user can evaluate the visualized design proposals.

[0288] Step 7:

[0289] The user evaluates the design proposals through the terminal and provides feedback. The server receives the feedback and revises the design proposals. The final design proposals are converted into the STL format suitable for output by a 3D printing device and provided to the user.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In designing virtual stores, there is a challenge in efficiently generating and visualizing optimal designs by reflecting customer behavior data in real time. In particular, there is a lack of tools for quickly and effectively evaluating and providing feedback on design proposals in three-dimensional space.

[0293] 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.

[0294] In this invention, the server includes means for generating data in a format that can be output by a three-dimensional printing device based on the generated design proposals, means for constructing a generative artificial intelligence model that predicts economic effects, and means for visualizing the design proposals on a display device in three-dimensional space in real time and optimizing the design based on customer behavior data. This enables users to effectively evaluate and quickly optimize virtual store design proposals.

[0295] "A means of generating data in a format that can be output by a 3D printing device based on the generated design proposal" refers to the process of converting digital data into a format that can be output as a physical model in order to visualize the design proposal of a virtual store in three dimensions.

[0296] "A means of constructing a generative artificial intelligence model to predict economic effects" refers to a method of simulating and predicting the economic impact of virtual store design using AI technology.

[0297] "A means of generating and visually presenting design proposals" refers to a technology that uses digital tools to create a virtual design of a store and visually displays the results to the user.

[0298] "Means for integrating and pre-processing collected data" refers to the process of aggregating data obtained from various sources and preparing it in a format suitable for analysis and model building.

[0299] "Methods for setting different simulation scenarios and comparing the results" refers to techniques for performing simulations based on multiple design hypotheses, comparing the results side-by-side, and evaluating optimal performance.

[0300] "A means of visualizing design proposals on a display device in three-dimensional space in real time and optimizing the design based on customer behavior data" refers to a technology that uses a visual device to display design proposals in three-dimensional space, and dynamically modifies and optimizes the design by reflecting actual customer data.

[0301] The "function that allows users to evaluate design proposals using a display device and provide feedback through operation" is an interface that allows users to view visualized virtual designs through a device and provide interactive feedback.

[0302] The system for realizing this invention incorporates advanced technology to assist in the design and optimization of virtual stores. The server first collects and integrates customer behavior data, demographics, and market trends. Next, it cleans and preprocesses this data into a format usable by the generating AI model. This allows for the construction of an AI model that predicts the economic impact of the virtual store and optimizes the customer experience.

[0303] The server generates store design proposals in three-dimensional space and presents them visually using display devices such as Oculus Quest and Microsoft HoloLens. Based on the displayed design, users provide direct feedback, which is reflected in the system in real time. Through this process, the store design proposals are continuously optimized and converted into appropriate data formats for output on a 3D printer.

[0304] As a specific example, consider the case where a clothing store designs a virtual layout for a new store. The user wears smart glasses and walks around in the virtual store to check the product layout and customer flow. The server collects the customer's footsteps and eye line information and proposes an optimal layout using a generative AI model. An example of the prompt text used in this process is "Please generate a design plan for optimizing the virtual store layout of a clothing store based on the latest customer flow data. Pay particular attention to the layout around the cash register and behind the product shelves."

[0305] With such a system, the user can repeatedly perform design evaluations in a virtual environment and efficiently determine the optimal store layout. As a result, decision-making based on information from real-time data is promoted.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The server collects customer behavior data, demographics, and market trend data. This includes the operation of obtaining data streams from sensor devices and online platforms in real time. The collected data is used as input data for the generative AI model.

[0309] Step 2:

[0310] The server cleans up the collected data and performs preprocessing. This includes operations such as filling in missing values in the data, scaling, and noise removal. The cleaned data is in a standardized format and contributes to improving the accuracy of the AI model. The output of this step serves as the input to the next AI model construction step.

[0311] Step 3:

[0312] The server builds a generative AI model using a clean dataset. This model is optimized to predict the economic impact of a store and customer flow. In this process, a learning algorithm adjusts the model parameters to generate the optimal response. The generated AI model is used to generate design proposals.

[0313] Step 4:

[0314] The server uses a generative AI model to create design proposals for a virtual store. Considering the conditions and constraints based on the prompts, the model proposes the optimal layout and design. The server then prepares to output these generated design proposals in a 3D format.

[0315] Step 5:

[0316] The server enables visualization in three-dimensional space based on the generated design proposals. The design proposals are sent to display devices such as Oculus Quest and Microsoft HoloLens, and presented to the user as 3D visualizations. This visualization allows for a concrete experience of the design proposals.

[0317] Step 6:

[0318] Users explore a visualized virtual store and provide feedback as they move around. User actions and eye-tracking information are sent to a server, which then generates new data.

[0319] Step 7:

[0320] The server continuously optimizes itself based on user feedback. By combining the AI ​​model's predictions with user feedback, it evolves into a more accurate design proposal. Ultimately, the output design data provides the optimal store layout that aligns with the user's intentions.

[0321] 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.

[0322] This invention enables a more sophisticated decision-making process by integrating an emotion engine into a computer system that supports the design and commercialization of urban sports facilities. The system includes, as its main components, a data collection and pre-processing module, a demand forecasting and economic impact forecasting module, a design proposal generation module, an emotion engine, and a user interface.

[0323] The server first collects demographic data, tourism data, and usage information of existing facilities from various data sources and integrates them into a database. Next, it cleanses the collected data and converts it into a format usable by AI models through a normalization process.

[0324] Subsequently, the server builds a generative AI model, predicts facility demand for each region, and generates design proposals based on the predicted demand.

[0325] For the generated design proposals, the server recognizes the user's emotions through an emotion engine and evaluates the user's response. By analyzing the feedback and responses provided by the user using their device, the emotion engine extracts areas for improvement in the design proposal and automatically incorporates them into the design. This process results in a more convincing design proposal that can meet the user's latent needs.

[0326] After the above process is completed and the design proposal is finalized, the server converts the data into a format that can be output by a 3D printer (e.g., STL format) and presents a download link to the user via the terminal. The user can then use this data to create a physical model.

[0327] As a concrete example, consider a case where a user evaluates a skateboard park design proposal via their device. When the user reviews a new design, the emotion engine analyzes the user's facial expressions and keystrokes, prioritizing design elements that elicit positive reactions. Based on these positive reactions, the design proposal is refined and the final version is presented, facilitating smoother coordination with all parties involved.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The server automatically collects a wide variety of data from external data sources, such as demographic data, tourism data, and usage status of existing facilities, and stores it in an integrated database.

[0331] Step 2:

[0332] The server cleanses the collected data, removes outliers, and supplements missing data, converting it into a format that can be analyzed by the AI ​​model.

[0333] Step 3:

[0334] The server builds a generative AI model based on the formatted data and predicts the demand for urban sports facilities in each region.

[0335] Step 4:

[0336] The server uses AI to generate facility design proposals based on predicted demand. These design proposals take into account user needs and regional characteristics.

[0337] Step 5:

[0338] The device visually presents the generated design proposals to the user via a user interface, prompting them to evaluate them.

[0339] Step 6:

[0340] The emotion engine uses the device to collect user facial expression data and reactions in real time and analyzes the user's emotional state.

[0341] Step 7:

[0342] The server analyzes feedback from the emotion engine, prioritizes design elements that elicit positive responses, and refines the design proposal.

[0343] Step 8:

[0344] The device will then present the improved design proposal to the user again for final evaluation and approval.

[0345] Step 9:

[0346] The server converts the approved design proposals into a data format that can be output by a 3D printer and provides the user with a download link via the terminal.

[0347] Step 10:

[0348] Users can download the data from the provided link and create a physical model using a 3D printer.

[0349] (Example 2)

[0350] 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".

[0351] In designing modern urban sports facilities, it is difficult to efficiently formulate design proposals that accurately reflect local characteristics and user needs, and a high degree of accuracy is required in predicting the economic effects and demand of the design. Furthermore, traditional methods make it difficult to appropriately reflect users' emotions and opinions, often resulting in dissatisfaction or shortcomings in the final design proposal. There is a need to solve these problems and provide a more effective and convincing design process.

[0352] 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.

[0353] In this invention, the server includes means for integrating and normalizing data from various collected information sources to convert it into a form usable by a generative artificial intelligence model; means for predicting facility demand in each region using the generative artificial intelligence model and generating design proposals based on the results; and means including an emotion analysis device for analyzing user input information and emotional responses and improving the design proposals. This makes it possible to quickly formulate design proposals that accurately reflect user needs and regional characteristics, further improve the design proposals by incorporating user emotional responses, and present them in the most optimal form.

[0354] "Integrating and normalizing data from diverse sources" is the process of centrally combining information obtained from multiple different data sources and arranging it into a consistent format through statistical processing and transformation.

[0355] "Predicting regional facility demand using generative artificial intelligence models" refers to a technique that analyzes and estimates future facility usage demand by applying machine learning and data analysis technologies based on regional characteristics and historical data.

[0356] "Including an emotion analysis device that analyzes user input information and emotional responses to improve design proposals" means that the system is equipped with an analysis device that analyzes emotional feedback and directly entered opinions provided by users and uses that data to make modifications and improvements to design proposals.

[0357] "Converting to a data format that can be output by a 3D printing device" refers to the process of changing a digital design proposal into a digital format (e.g., STL format) that can be understood by a 3D printer or other output device, and then saving it, in order to manufacture it as a physical model.

[0358] "Providing a function that allows users to evaluate design proposals via a terminal and automatically generates revised proposals based on those evaluations" means a system that has the ability to collect feedback on design proposals through a user interface and automatically propose revised proposals that reflect that feedback.

[0359] This invention provides a configuration for a computer system integrating an emotion engine to support the design and commercialization of urban sports facilities. Through data collection, integration, and analysis, this system automatically generates sports facility designs that meet user needs, supporting efficient decision-making.

[0360] The server retrieves demographic data, tourism data, and usage information for existing facilities from diverse sources. This data is collected using API calls and database queries and integrated into a database. Next, the server uses the Python Pandas library to cleanse and normalize the data, thereby converting it into a format usable by AI models.

[0361] The generative AI model is built using machine learning frameworks such as TensorFlow or PyTorch. This model predicts facility demand for each region and creates appropriate design proposals based on the collected data. The generated design proposals are visually presented on the terminal through the user interface.

[0362] Users evaluate design proposals using a terminal, and input information from the terminal and emotional responses detected by the emotion engine are sent to the server. The emotion engine analyzes the user's facial expressions and input operations, and uses this data to improve the design proposals. Design elements that receive many positive responses are retained, and improvement suggestions are automatically provided.

[0363] Finally, the server converts the improved design into a format that can be printed on a 3D printer (e.g., STL format) and provides it to the user via the terminal. The user can then use this data to create a physical model.

[0364] As a concrete example, consider a case where a user evaluates a design proposal for a skateboard park. In this case, an example of a prompt message might be, "Based on the latest demographic and tourism data, forecast the demand for sports facilities and generate an appropriate design proposal." This system automates the entire process from design evaluation to improvement, facilitating smooth coordination among stakeholders.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The server collects demographic data, tourism data, and usage information for existing facilities from various information sources. Specifically, it retrieves data using Web APIs and imports local files, and stores this data in a database. The input is raw data from diverse information sources, and the output is an integrated database. This integrated data forms the basis for subsequent analysis.

[0368] Step 2:

[0369] The server performs data cleansing on the collected data. Specifically, it uses the Python Pandas library to detect and correct missing and outlier values, and remove duplicate data. The input for this step is the raw data obtained in step 1, and the output is a cleansed, consistent dataset. This improves the quality of the data and enables more accurate predictions.

[0370] Step 3:

[0371] The server normalizes the cleansed data and transforms it so that it can be used by the AI ​​model. Specifically, it scales and encodes the data to prepare the dataset for the model's input format. The input is the formatted data from step 2, and the output is the standardized data for the AI ​​model input. This normalized data improves the training and prediction accuracy of the AI ​​model.

[0372] Step 4:

[0373] The server performs demand forecasting using a generative AI model. Using TensorFlow or PyTorch, it processes normalized data to predict facility demand for each region. The input is the data prepared in step 3, and the output is the result of the demand forecast. Based on this forecast, the server is ready to proceed with design proposal generation.

[0374] Step 5:

[0375] The server generates design proposals based on demand forecasts. The design proposal generation module analyzes the output of the AI ​​model and creates facility design proposals that reflect user needs and regional characteristics. The input is demand forecast data, and the output is an initial design proposal. The generated proposals are then prepared for visual presentation.

[0376] Step 6:

[0377] The terminal visually presents the generated design proposal to the user. The user reviews the design proposal on the screen and provides feedback through the interface. The input is design proposal data from the server, and the output is user evaluation data. This allows the user to express their opinion on the design.

[0378] Step 7:

[0379] The server utilizes an emotion engine to analyze user feedback and derive improvements to the design proposal. The emotion engine analyzes user emotional responses and keystrokes to identify positive response elements. Input is user feedback and analysis information, and output is a design proposal including improvements. This process enables proposals that better meet user expectations.

[0380] Step 8:

[0381] The server compiles the improved design proposal into a final format and converts it into a format that can be output by a 3D printer. Specifically, it exports the design proposal data to formats such as STL. The input is the finalized design proposal, and the output is a file for 3D printing. With data in this format, the system is ready to create a physical prototype.

[0382] Step 9:

[0383] The terminal provides the user with a download link for the generated 3D data. The user can download the data via the link and proceed with 3D printing or model making. The input is the 3D data from the server, and the output is the download link and the data provided to the user.

[0384] (Application Example 2)

[0385] 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."

[0386] In modern store design, there is a demand for designs that accurately reflect customer needs. However, traditional design processes fail to effectively utilize user emotions and reactions, resulting in a challenge in achieving designs that are optimal for market demand and customer expectations. Furthermore, predicting the number of visitors to a store and improving its economic impact remains a difficult challenge.

[0387] 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.

[0388] In this invention, the server includes means for generating data in a format that can be output by a three-dimensional printing device based on the generated design proposal, means for constructing a generative artificial intelligence model that predicts economic effects, and means for analyzing user emotions and reflecting them in improving the design proposal. This makes it possible to reflect customer emotions in real time in store design and provide optimized design proposals. Furthermore, it is possible to simulate an increase in the number of visitors and improve economic effects.

[0389] "Means for generating data" refers to a method that has the function of converting the generated design proposal into data in a format that can be output by a three-dimensional printing device.

[0390] A "generative artificial intelligence model" is an intelligent system that uses machine learning algorithms to predict economic effects and calculates the performance of a facility under specific conditions.

[0391] "Means of generating and visually presenting design proposals" refers to the process of creating a concrete and visually understandable design prototype for the user and displaying it on a screen.

[0392] "Means for integrating and pre-processing collected data" refers to methods for organizing and integrating data obtained from various sources to make it analyzable.

[0393] "Methods for setting up different simulation scenarios and comparing the results" refers to methods for preparing multiple hypotheses and environmental conditions, and analyzing and evaluating the performance in each scenario.

[0394] "A means of analyzing user emotions and reflecting them in improving design proposals" refers to a system that analyzes user emotions in real time and adaptively improves the design based on that analysis.

[0395] "Means of acquiring evaluation data and adjusting design proposals" refers to the process of receiving user feedback and adjusting the design based on that feedback.

[0396] The embodiments for carrying out the invention are shown below.

[0397] In the system based on this invention, the server first collects demographic data, tourism data, and usage information of existing facilities from various data sources to form an integrated database. Before being stored in the database, the collected data undergoes a cleansing and normalization process to convert it into a format suitable for analysis.

[0398] The server then uses a generative artificial intelligence model to predict facility demand for each region and generates design proposals based on this. These design proposals are converted into a format that can be output by a 3D printer and presented visually through the user's terminal. The server incorporates an emotion engine to capture the user's emotions as a reaction to the design proposals, and has the ability to analyze emotions in real time through the user's facial expressions and actions. Based on this emotion analysis, the server automatically improves the design and provides the user with an optimized design proposal.

[0399] In this system, for example, a user visually evaluates a new interior design for a cafe. If the evaluation is accompanied by positive emotions, it indicates that design elements such as glass walls and the extensive use of plants are well-received by the user. This information is immediately reflected in improvements to the next design proposal. The entire operation on the terminal is supported by an interface that provides a comfortable user experience.

[0400] The hardware used includes computer servers to support data collection and analysis, and smart devices for users to visually review designs. The software includes image processing libraries such as OpenCV and sentiment analysis libraries such as DeepFace for emotion recognition.

[0401] A possible example of a specific prompt message would be something like, "We are considering interior design proposals for a new cafe. Please generate new proposals incorporating positive design elements that users have previously found appealing."

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server collects information from various data sources, including demographic data, tourism data, and usage information for existing facilities. It uses external databases and APIs as input and stores the collected data as an initial dataset. This data serves as the foundation for integrating the information necessary for subsequent processing.

[0405] Step 2:

[0406] The server performs cleansing and normalization processes on the collected data. The input is the initial dataset obtained in step 1, and by processing it such as imputing missing values, removing outliers, and matching data types, it generates a clean, normalized dataset suitable for analysis as output. This dataset is used for training AI models and demand forecasting in the system.

[0407] Step 3:

[0408] The server uses a generative artificial intelligence model to predict facility demand in each region based on a clean dataset. The input is a normalized dataset, which is fed into the AI ​​model to execute the prediction algorithm, producing predicted values ​​for facility demand as output. These predicted values ​​are used to generate design proposals.

[0409] Step 4:

[0410] The server generates design proposals based on predicted facility demand and converts them into a data format for visual presentation on a terminal. Predicted facility demand is used as input, and a 3D modeling tool is used to generate output data that can serve as a visual design proposal. This data is then sent to a smart device for user evaluation.

[0411] Step 5:

[0412] The user visually evaluates the design proposals sent via their device. The input is a 3D design proposal sent from the server, and the emotion recognition system monitors the user's gaze, facial expressions, and movements in real time. This results in the output of user emotion data.

[0413] Step 6:

[0414] The server evaluates the design proposal based on the obtained user emotion data and automatically incorporates improvements to the design. User emotion response data is used as input, and an emotion analysis algorithm is applied to generate an optimized new design proposal as output. This new design proposal is then put into a further evaluation and improvement cycle.

[0415] 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.

[0416] 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 those described above. 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 shown 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention is implemented in a computer system for supporting the design and commercialization of urban sports facilities. The system consists of multiple components that work together to forecast facility demand, analyze economic impact, generate facility design proposals, and visualize the results. This enables local governments and developers to make more informed decisions.

[0432] The server first collects multiple data sources, including demographic data, tourist trends, and usage information for existing facilities. Next, it performs data cleaning and normalization on the collected data to prepare it for use by the model. Based on this clean dataset, a generative AI model is built to predict the demand for urban sports facilities in each region.

[0433] When simulating the economic effects of facility investments, the server utilizes a generated AI model to analyze the impact on increasing tourism to the region and creating jobs. This allows users to evaluate future economic benefits and conduct concrete investment evaluations. The results are presented to the user via a terminal, and an interface is provided for comparing different scenarios.

[0434] In generating facility design proposals, the server uses AI to create designs that take into account user needs, regional characteristics, and environmental conditions. These design proposals are visually presented to the user on a terminal, allowing for easy review and evaluation. Once the design proposal is finalized, the server converts it into a data format suitable for output on a 3D printer (e.g., STL format).

[0435] As a concrete example, consider a case where a local government is considering building a new skateboard park. The user inputs data tailored to the characteristics of the region into the system via a terminal. Based on this information, the server predicts demand and economic effects, and generates and presents a design proposal. The user then provides feedback on the design proposal, downloads the final design as data for a 3D printing device, and makes a decision on construction. In this way, the system supports consensus building among stakeholders and ensures smooth project progress.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The server automatically collects data such as demographic trends, tourism data, and utilization rates of existing facilities from various data sources, and stores this data in an integrated database.

[0439] Step 2:

[0440] The server detects errors and missing data from the collected data, performs cleansing, and converts it into a format usable by the AI ​​model through a normalization process.

[0441] Step 3:

[0442] The server uses a machine learning model based on the formatted data to perform regional facility demand forecasts and outputs the forecast results.

[0443] Step 4:

[0444] The terminal instructs the user to perform an economic impact simulation based on the demand forecast results and according to the simulation scenario set by the user.

[0445] Step 5:

[0446] The server runs an AI model based on the given simulation conditions, analyzes the potential impact on the local economy, and generates results.

[0447] Step 6:

[0448] The terminal visually presents simulation results to the user in graphs and charts, and provides an interface for comparing the results of multiple scenarios.

[0449] Step 7:

[0450] The server uses AI generation based on design criteria selected by the user to create a design proposal for the facility.

[0451] Step 8:

[0452] The device provides functionality to visualize the generated design proposals on a user interface, enabling review and feedback.

[0453] Step 9:

[0454] The server converts the approved design proposals into a format that can be output by a 3D printer and provides a download link for the data via the terminal.

[0455] (Example 1)

[0456] 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."

[0457] In recent years, while the demand for sports facilities in urban areas has been increasing, there is a need for facility designs that are appropriate for the region and for accurate predictions of economic effects. However, conventional methods were time-consuming to collect and analyze data, lacking efficiency. Furthermore, it was difficult to visually grasp the predicted economic effects, leading to problems with the quality of decision-making.

[0458] 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.

[0459] In this invention, the server includes means for generating information in a format that can be output by a 3D printing device based on the generated design outline, means for constructing a generative artificial intelligence model that predicts economic impact, and means for generating and visually presenting the design outline. This enables rapid and accurate prediction of the economic effects of sports facilities suitable for a region, and allows for intuitive evaluation and revision of design proposals.

[0460] A "generative artificial intelligence model" is an artificial intelligence technology that generates new information and predictions based on collected data.

[0461] A "3D printing device" is a device that outputs digital designs as physical three-dimensional objects.

[0462] "Economic impact" refers to the total effect that a particular region or project has on the economy.

[0463] A "design overview" is a description of the outline of the plan for a physical space, such as a sports facility.

[0464] "Presenting visually" is a method of expressing information clearly through visual means to facilitate understanding.

[0465] An "information processing system" is a system designed to collect, analyze, store, and process data.

[0466] A "user terminal" is an electronic device used by a user to access information and functions.

[0467] This invention is an information processing system for supporting the design and commercialization of sports facilities in urban areas. This system primarily consists of three components: a server, terminals, and users.

[0468] The server first collects relevant information from various data sources. This data includes demographics, tourist trends, and usage information for existing facilities. This data is stored in a database and undergoes data cleaning and normalization so that it can be used by generative AI models. Data cleaning corrects inconsistencies and missing information and standardizes the format.

[0469] Based on the collected and processed data, the server utilizes a generated AI model to predict facility demand and economic impact for each region. Specifically, the AI ​​model simulates the impact of increased tourism and the local economy. For example, by inputting a prompt such as, "Please output the estimated annual usage of a skateboard park in a city with a population of 200,000," the server will perform demand forecasting and generate design proposals.

[0470] The generated design proposal is sent from the server to the terminal and visualized. The terminal uses 3D modeling software to present the design proposal to the user in three dimensions, allowing the user to intuitively review and evaluate the plan. The user can evaluate the visualized design proposal and provide feedback.

[0471] Furthermore, after the user confirms the final design, the server converts the design into a format that can be output by a 3D printing device, such as STL format. This process makes it possible to output the facility design in a physical form in a specific format and utilize it in actual construction.

[0472] This system allows users to efficiently design sports facilities optimized for their region and predict their economic impact, supporting quick and accurate decision-making.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The server collects demographic data, tourist trends, and information on the use of existing facilities from diverse data sources. Input consists of raw data obtained through APIs and database connections. This data is stored in a cloud database for future analysis.

[0476] Step 2:

[0477] The server performs data cleaning and normalization on the collected data. Data cleaning involves imputing missing values ​​and removing outliers. Normalization unifies the data format and ensures consistency. This results in a standardized and clean dataset.

[0478] Step 3:

[0479] The server uses a generative AI model to forecast demand for urban sports facilities based on a clean dataset. The model is input with population growth and tourism data, and outputs predictive data through regression analysis and model learning using this data.

[0480] Step 4:

[0481] The server uses a generated AI model to simulate the economic effects of facility construction. It inputs regional economic indicators and previously predicted demand data to evaluate the potential increase in tourists to the region and job creation. As a result, it outputs a detailed economic impact report.

[0482] Step 5:

[0483] The server utilizes AI technology to generate facility design proposals that take into account user needs and regional characteristics. User profiles and environmental conditions are used as input data necessary for the design. The generated design proposals are output as 3D model data.

[0484] Step 6:

[0485] The terminal visually presents the design proposals received from the server. Using 3D modeling software, it displays 3D model data in a way that is easily understood visually by humans. Users can then evaluate the visualized design proposals.

[0486] Step 7:

[0487] Users evaluate design proposals and provide feedback via their terminals. The server receives this feedback and revises the design proposals. The final design proposal is converted to STL format, suitable for output on a 3D printing machine, and provided to the user.

[0488] (Application Example 1)

[0489] 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."

[0490] In designing virtual stores, there is a challenge in efficiently generating and visualizing optimal designs by reflecting customer behavior data in real time. In particular, there is a lack of tools for quickly and effectively evaluating and providing feedback on design proposals in three-dimensional space.

[0491] 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.

[0492] In this invention, the server includes means for generating data in a format that can be output by a three-dimensional printing device based on the generated design proposals, means for constructing a generative artificial intelligence model that predicts economic effects, and means for visualizing the design proposals on a display device in three-dimensional space in real time and optimizing the design based on customer behavior data. This enables users to effectively evaluate and quickly optimize virtual store design proposals.

[0493] "A means of generating data in a format that can be output by a 3D printing device based on the generated design proposal" refers to the process of converting digital data into a format that can be output as a physical model in order to visualize the design proposal of a virtual store in three dimensions.

[0494] "A means of constructing a generative artificial intelligence model to predict economic effects" refers to a method of simulating and predicting the economic impact of virtual store design using AI technology.

[0495] "A means of generating and visually presenting design proposals" refers to a technology that uses digital tools to create a virtual design of a store and visually displays the results to the user.

[0496] "Means for integrating and pre-processing collected data" refers to the process of aggregating data obtained from various sources and preparing it in a format suitable for analysis and model building.

[0497] "Methods for setting different simulation scenarios and comparing the results" refers to techniques for performing simulations based on multiple design hypotheses, comparing the results side-by-side, and evaluating optimal performance.

[0498] "A means of visualizing design proposals on a display device in three-dimensional space in real time and optimizing the design based on customer behavior data" refers to a technology that uses a visual device to display design proposals in three-dimensional space, and dynamically modifies and optimizes the design by reflecting actual customer data.

[0499] The "function that allows users to evaluate design proposals using a display device and provide feedback through operation" is an interface that allows users to view visualized virtual designs through a device and provide interactive feedback.

[0500] The system for realizing this invention incorporates advanced technology to assist in the design and optimization of virtual stores. The server first collects and integrates customer behavior data, demographics, and market trends. Next, it cleans and preprocesses this data into a format usable by the generating AI model. This allows for the construction of an AI model that predicts the economic impact of the virtual store and optimizes the customer experience.

[0501] The server generates store design proposals in three-dimensional space and presents them visually using display devices such as Oculus Quest and Microsoft HoloLens. Based on the displayed design, users provide direct feedback, which is reflected in the system in real time. Through this process, the store design proposals are continuously optimized and converted into appropriate data formats for output on a 3D printer.

[0502] As a concrete example, consider a clothing store designing a virtual store layout. The user wears smart glasses and walks around the virtual store, checking the placement of products and customer traffic. The server collects customer movement and eye-tracking information and uses a generated AI model to suggest the optimal layout. An example of a prompt used in this process would be: "Based on the latest customer traffic data, generate a design plan to optimize the virtual store layout for the clothing store. Pay particular attention to the placement around the cash registers and the back of the shelves."

[0503] Such a system allows users to repeatedly evaluate designs within a virtual environment and efficiently determine the optimal store layout. As a result, it promotes data-driven decision-making based on real-time information.

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The server collects customer behavior data, demographic data, and market trend data. This includes acquiring data streams in real time from sensor devices and online platforms. This collected data is used as input data for generative AI models.

[0507] Step 2:

[0508] The server cleans and preprocesses the collected data, including imputing missing values, scaling, and denoising. The cleaned data is in a standardized format, contributing to improved accuracy of the AI ​​model. The output of this step becomes the input for the next AI model building step.

[0509] Step 3:

[0510] The server builds a generative AI model using a clean dataset. This model is optimized to predict the economic impact of a store and customer flow. In this process, a learning algorithm adjusts the model parameters to generate the optimal response. The generated AI model is used to generate design proposals.

[0511] Step 4:

[0512] The server uses a generative AI model to create design proposals for a virtual store. Considering the conditions and constraints based on the prompts, the model proposes the optimal layout and design. The server then prepares to output these generated design proposals in a 3D format.

[0513] Step 5:

[0514] The server enables visualization in three-dimensional space based on the generated design proposals. The design proposals are sent to display devices such as Oculus Quest and Microsoft HoloLens, and presented to the user as 3D visualizations. This visualization allows for a concrete experience of the design proposals.

[0515] Step 6:

[0516] Users explore a visualized virtual store and provide feedback as they move around. User actions and eye-tracking information are sent to a server, which then generates new data.

[0517] Step 7:

[0518] The server continuously optimizes itself based on user feedback. By combining the AI ​​model's predictions with user feedback, it evolves into a more accurate design proposal. Ultimately, the output design data provides the optimal store layout that aligns with the user's intentions.

[0519] 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.

[0520] This invention enables a more sophisticated decision-making process by integrating an emotion engine into a computer system that supports the design and commercialization of urban sports facilities. The system includes, as its main components, a data collection and pre-processing module, a demand forecasting and economic impact forecasting module, a design proposal generation module, an emotion engine, and a user interface.

[0521] The server first collects demographic data, tourism data, and usage information of existing facilities from various data sources and integrates them into a database. Next, it cleanses the collected data and converts it into a format usable by AI models through a normalization process.

[0522] Subsequently, the server builds a generative AI model, predicts facility demand for each region, and generates design proposals based on the predicted demand.

[0523] For the generated design proposals, the server recognizes the user's emotions through an emotion engine and evaluates the user's response. By analyzing the feedback and responses provided by the user using their device, the emotion engine extracts areas for improvement in the design proposal and automatically incorporates them into the design. This process results in a more convincing design proposal that can meet the user's latent needs.

[0524] After the above process is completed and the design proposal is finalized, the server converts the data into a format that can be output by a 3D printer (e.g., STL format) and presents a download link to the user via the terminal. The user can then use this data to create a physical model.

[0525] As a concrete example, consider a case where a user evaluates a skateboard park design proposal via their device. When the user reviews a new design, the emotion engine analyzes the user's facial expressions and keystrokes, prioritizing design elements that elicit positive reactions. Based on these positive reactions, the design proposal is refined and the final version is presented, facilitating smoother coordination with all parties involved.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The server automatically collects a wide variety of data from external data sources, such as demographic data, tourism data, and usage status of existing facilities, and stores it in an integrated database.

[0529] Step 2:

[0530] The server cleanses the collected data, removes outliers, and supplements missing data, converting it into a format that can be analyzed by the AI ​​model.

[0531] Step 3:

[0532] The server builds a generative AI model based on the formatted data and predicts the demand for urban sports facilities in each region.

[0533] Step 4:

[0534] The server uses AI to generate facility design proposals based on predicted demand. These design proposals take into account user needs and regional characteristics.

[0535] Step 5:

[0536] The device visually presents the generated design proposals to the user via a user interface, prompting them to evaluate them.

[0537] Step 6:

[0538] The emotion engine uses the device to collect user facial expression data and reactions in real time and analyzes the user's emotional state.

[0539] Step 7:

[0540] The server analyzes feedback from the emotion engine, prioritizes design elements that elicit positive responses, and refines the design proposal.

[0541] Step 8:

[0542] The device will then present the improved design proposal to the user again for final evaluation and approval.

[0543] Step 9:

[0544] The server converts the approved design proposals into a data format that can be output by a 3D printer and provides the user with a download link via the terminal.

[0545] Step 10:

[0546] Users can download the data from the provided link and create a physical model using a 3D printer.

[0547] (Example 2)

[0548] 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."

[0549] In designing modern urban sports facilities, it is difficult to efficiently formulate design proposals that accurately reflect local characteristics and user needs, and a high degree of accuracy is required in predicting the economic effects and demand of the design. Furthermore, traditional methods make it difficult to appropriately reflect users' emotions and opinions, often resulting in dissatisfaction or shortcomings in the final design proposal. There is a need to solve these problems and provide a more effective and convincing design process.

[0550] 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.

[0551] In this invention, the server includes means for integrating and normalizing data from various collected information sources to convert it into a form usable by a generative artificial intelligence model; means for predicting facility demand in each region using the generative artificial intelligence model and generating design proposals based on the results; and means including an emotion analysis device for analyzing user input information and emotional responses and improving the design proposals. This makes it possible to quickly formulate design proposals that accurately reflect user needs and regional characteristics, further improve the design proposals by incorporating user emotional responses, and present them in the most optimal form.

[0552] "Integrating and normalizing data from diverse sources" is the process of centrally combining information obtained from multiple different data sources and arranging it into a consistent format through statistical processing and transformation.

[0553] "Predicting regional facility demand using generative artificial intelligence models" refers to a technique that analyzes and estimates future facility usage demand by applying machine learning and data analysis technologies based on regional characteristics and historical data.

[0554] "Including an emotion analysis device that analyzes user input information and emotional responses to improve design proposals" means that the system is equipped with an analysis device that analyzes emotional feedback and directly entered opinions provided by users and uses that data to make modifications and improvements to design proposals.

[0555] "Converting to a data format that can be output by a 3D printing device" refers to the process of changing a digital design proposal into a digital format (e.g., STL format) that can be understood by a 3D printer or other output device, and then saving it, in order to manufacture it as a physical model.

[0556] "Providing a function that allows users to evaluate design proposals via a terminal and automatically generates revised proposals based on those evaluations" means a system that has the ability to collect feedback on design proposals through a user interface and automatically propose revised proposals that reflect that feedback.

[0557] This invention provides a configuration for a computer system integrating an emotion engine to support the design and commercialization of urban sports facilities. Through data collection, integration, and analysis, this system automatically generates sports facility designs that meet user needs, supporting efficient decision-making.

[0558] The server retrieves demographic data, tourism data, and usage information for existing facilities from diverse sources. This data is collected using API calls and database queries and integrated into a database. Next, the server uses the Python Pandas library to cleanse and normalize the data, thereby converting it into a format usable by AI models.

[0559] The generative AI model is built using machine learning frameworks such as TensorFlow or PyTorch. This model predicts facility demand for each region and creates appropriate design proposals based on the collected data. The generated design proposals are visually presented on the terminal through the user interface.

[0560] Users evaluate design proposals using a terminal, and input information from the terminal and emotional responses detected by the emotion engine are sent to the server. The emotion engine analyzes the user's facial expressions and input operations, and uses this data to improve the design proposals. Design elements that receive many positive responses are retained, and improvement suggestions are automatically provided.

[0561] Finally, the server converts the improved design into a format that can be printed on a 3D printer (e.g., STL format) and provides it to the user via the terminal. The user can then use this data to create a physical model.

[0562] As a concrete example, consider a case where a user evaluates a design proposal for a skateboard park. In this case, an example of a prompt message might be, "Based on the latest demographic and tourism data, forecast the demand for sports facilities and generate an appropriate design proposal." This system automates the entire process from design evaluation to improvement, facilitating smooth coordination among stakeholders.

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] The server collects demographic data, tourism data, and usage information for existing facilities from various information sources. Specifically, it retrieves data using Web APIs and imports local files, and stores this data in a database. The input is raw data from diverse information sources, and the output is an integrated database. This integrated data forms the basis for subsequent analysis.

[0566] Step 2:

[0567] The server performs data cleansing on the collected data. Specifically, it uses the Python Pandas library to detect and correct missing and outlier values, and remove duplicate data. The input for this step is the raw data obtained in step 1, and the output is a cleansed, consistent dataset. This improves the quality of the data and enables more accurate predictions.

[0568] Step 3:

[0569] The server normalizes the cleansed data and transforms it so that it can be used by the AI ​​model. Specifically, it scales and encodes the data to prepare the dataset for the model's input format. The input is the formatted data from step 2, and the output is the standardized data for the AI ​​model input. This normalized data improves the training and prediction accuracy of the AI ​​model.

[0570] Step 4:

[0571] The server performs demand forecasting using a generative AI model. Using TensorFlow or PyTorch, it processes normalized data to predict facility demand for each region. The input is the data prepared in step 3, and the output is the result of the demand forecast. Based on this forecast, the server is ready to proceed with design proposal generation.

[0572] Step 5:

[0573] The server generates design proposals based on demand forecasts. The design proposal generation module analyzes the output of the AI ​​model and creates facility design proposals that reflect user needs and regional characteristics. The input is demand forecast data, and the output is an initial design proposal. The generated proposals are then prepared for visual presentation.

[0574] Step 6:

[0575] The terminal visually presents the generated design proposal to the user. The user reviews the design proposal on the screen and provides feedback through the interface. The input is design proposal data from the server, and the output is user evaluation data. This allows the user to express their opinion on the design.

[0576] Step 7:

[0577] The server utilizes an emotion engine to analyze user feedback and derive improvements to the design proposal. The emotion engine analyzes user emotional responses and keystrokes to identify positive response elements. Input is user feedback and analysis information, and output is a design proposal including improvements. This process enables proposals that better meet user expectations.

[0578] Step 8:

[0579] The server compiles the improved design proposal into a final format and converts it into a format that can be output by a 3D printer. Specifically, it exports the design proposal data to formats such as STL. The input is the finalized design proposal, and the output is a file for 3D printing. With data in this format, the system is ready to create a physical prototype.

[0580] Step 9:

[0581] The terminal provides the user with a download link for the generated 3D data. The user can download the data via the link and proceed with 3D printing or model making. The input is the 3D data from the server, and the output is the download link and the data provided to the user.

[0582] (Application Example 2)

[0583] 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."

[0584] In modern store design, there is a demand for designs that accurately reflect customer needs. However, traditional design processes fail to effectively utilize user emotions and reactions, resulting in a challenge in achieving designs that are optimal for market demand and customer expectations. Furthermore, predicting the number of visitors to a store and improving its economic impact remains a difficult challenge.

[0585] 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.

[0586] In this invention, the server includes means for generating data in a format that can be output by a three-dimensional printing device based on the generated design proposal, means for constructing a generative artificial intelligence model that predicts economic effects, and means for analyzing user emotions and reflecting them in improving the design proposal. This makes it possible to reflect customer emotions in real time in store design and provide optimized design proposals. Furthermore, it is possible to simulate an increase in the number of visitors and improve economic effects.

[0587] "Means for generating data" refers to a method that has the function of converting the generated design proposal into data in a format that can be output by a three-dimensional printing device.

[0588] A "generative artificial intelligence model" is an intelligent system that uses machine learning algorithms to predict economic effects and calculates the performance of a facility under specific conditions.

[0589] "Means of generating and visually presenting design proposals" refers to the process of creating a concrete and visually understandable design prototype for the user and displaying it on a screen.

[0590] "Means for integrating and pre-processing collected data" refers to methods for organizing and integrating data obtained from various sources to make it analyzable.

[0591] "Methods for setting up different simulation scenarios and comparing the results" refers to methods for preparing multiple hypotheses and environmental conditions, and analyzing and evaluating the performance in each scenario.

[0592] "A means of analyzing user emotions and reflecting them in improving design proposals" refers to a system that analyzes user emotions in real time and adaptively improves the design based on that analysis.

[0593] "Means of acquiring evaluation data and adjusting design proposals" refers to the process of receiving user feedback and adjusting the design based on that feedback.

[0594] The embodiments for carrying out the invention are shown below.

[0595] In the system based on this invention, the server first collects demographic data, tourism data, and usage information of existing facilities from various data sources to form an integrated database. Before being stored in the database, the collected data undergoes a cleansing and normalization process to convert it into a format suitable for analysis.

[0596] The server then uses a generative artificial intelligence model to predict facility demand for each region and generates design proposals based on this. These design proposals are converted into a format that can be output by a 3D printer and presented visually through the user's terminal. The server incorporates an emotion engine to capture the user's emotions as a reaction to the design proposals, and has the ability to analyze emotions in real time through the user's facial expressions and actions. Based on this emotion analysis, the server automatically improves the design and provides the user with an optimized design proposal.

[0597] In this system, for example, a user visually evaluates a new interior design for a cafe. If the evaluation is accompanied by positive emotions, it indicates that design elements such as glass walls and the extensive use of plants are well-received by the user. This information is immediately reflected in improvements to the next design proposal. The entire operation on the terminal is supported by an interface that provides a comfortable user experience.

[0598] The hardware used includes computer servers to support data collection and analysis, and smart devices for users to visually review designs. The software includes image processing libraries such as OpenCV and sentiment analysis libraries such as DeepFace for emotion recognition.

[0599] A possible example of a specific prompt message would be something like, "We are considering interior design proposals for a new cafe. Please generate new proposals incorporating positive design elements that users have previously found appealing."

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The server collects information from various data sources, including demographic data, tourism data, and usage information for existing facilities. It uses external databases and APIs as input and stores the collected data as an initial dataset. This data serves as the foundation for integrating the information necessary for subsequent processing.

[0603] Step 2:

[0604] The server performs cleansing and normalization processes on the collected data. The input is the initial dataset obtained in step 1, and by processing it such as imputing missing values, removing outliers, and matching data types, it generates a clean, normalized dataset suitable for analysis as output. This dataset is used for training AI models and demand forecasting in the system.

[0605] Step 3:

[0606] The server uses a generative artificial intelligence model to predict facility demand in each region based on a clean dataset. The input is a normalized dataset, which is fed into the AI ​​model to execute the prediction algorithm, producing predicted values ​​for facility demand as output. These predicted values ​​are used to generate design proposals.

[0607] Step 4:

[0608] The server generates design proposals based on predicted facility demand and converts them into a data format for visual presentation on a terminal. Predicted facility demand is used as input, and a 3D modeling tool is used to generate output data that can serve as a visual design proposal. This data is then sent to a smart device for user evaluation.

[0609] Step 5:

[0610] The user visually evaluates the design proposals sent via their device. The input is a 3D design proposal sent from the server, and the emotion recognition system monitors the user's gaze, facial expressions, and movements in real time. This results in the output of user emotion data.

[0611] Step 6:

[0612] The server evaluates the design proposal based on the obtained user emotion data and automatically incorporates improvements to the design. User emotion response data is used as input, and an emotion analysis algorithm is applied to generate an optimized new design proposal as output. This new design proposal is then put into a further evaluation and improvement cycle.

[0613] 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.

[0614] 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 those described above. 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 shown 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.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] This invention is implemented in a computer system for supporting the design and commercialization of urban sports facilities. The system consists of multiple components that work together to forecast facility demand, analyze economic impact, generate facility design proposals, and visualize the results. This enables local governments and developers to make more informed decisions.

[0631] The server first collects multiple data sources, including demographic data, tourist trends, and usage information for existing facilities. Next, it performs data cleaning and normalization on the collected data to prepare it for use by the model. Based on this clean dataset, a generative AI model is built to predict the demand for urban sports facilities in each region.

[0632] When simulating the economic effects of facility investments, the server utilizes a generated AI model to analyze the impact on increasing tourism to the region and creating jobs. This allows users to evaluate future economic benefits and conduct concrete investment evaluations. The results are presented to the user via a terminal, and an interface is provided for comparing different scenarios.

[0633] In generating facility design proposals, the server uses AI to create designs that take into account user needs, regional characteristics, and environmental conditions. These design proposals are visually presented to the user on a terminal, allowing for easy review and evaluation. Once the design proposal is finalized, the server converts it into a data format suitable for output on a 3D printer (e.g., STL format).

[0634] As a concrete example, consider a case where a local government is considering building a new skateboard park. The user inputs data tailored to the characteristics of the region into the system via a terminal. Based on this information, the server predicts demand and economic effects, and generates and presents a design proposal. The user then provides feedback on the design proposal, downloads the final design as data for a 3D printing device, and makes a decision on construction. In this way, the system supports consensus building among stakeholders and ensures smooth project progress.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The server automatically collects data such as demographic trends, tourism data, and utilization rates of existing facilities from various data sources, and stores this data in an integrated database.

[0638] Step 2:

[0639] The server detects errors and missing data from the collected data, performs cleansing, and converts it into a format usable by the AI ​​model through a normalization process.

[0640] Step 3:

[0641] The server uses a machine learning model based on the formatted data to perform regional facility demand forecasts and outputs the forecast results.

[0642] Step 4:

[0643] The terminal instructs the user to perform an economic impact simulation based on the demand forecast results and according to the simulation scenario set by the user.

[0644] Step 5:

[0645] The server runs an AI model based on the given simulation conditions, analyzes the potential impact on the local economy, and generates results.

[0646] Step 6:

[0647] The terminal visually presents simulation results to the user in graphs and charts, and provides an interface for comparing the results of multiple scenarios.

[0648] Step 7:

[0649] The server uses AI generation based on design criteria selected by the user to create a design proposal for the facility.

[0650] Step 8:

[0651] The device provides functionality to visualize the generated design proposals on a user interface, enabling review and feedback.

[0652] Step 9:

[0653] The server converts the approved design proposals into a format that can be output by a 3D printer and provides a download link for the data via the terminal.

[0654] (Example 1)

[0655] 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".

[0656] In recent years, while the demand for sports facilities in urban areas has been increasing, there is a need for facility designs that are appropriate for the region and for accurate predictions of economic effects. However, conventional methods were time-consuming to collect and analyze data, lacking efficiency. Furthermore, it was difficult to visually grasp the predicted economic effects, leading to problems with the quality of decision-making.

[0657] 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.

[0658] In this invention, the server includes means for generating information in a format that can be output by a 3D printing device based on the generated design outline, means for constructing a generative artificial intelligence model that predicts economic impact, and means for generating and visually presenting the design outline. This enables rapid and accurate prediction of the economic effects of sports facilities suitable for a region, and allows for intuitive evaluation and revision of design proposals.

[0659] A "generative artificial intelligence model" is an artificial intelligence technology that generates new information and predictions based on collected data.

[0660] A "3D printing device" is a device that outputs digital designs as physical three-dimensional objects.

[0661] "Economic impact" refers to the total effect that a particular region or project has on the economy.

[0662] A "design overview" is a description of the outline of the plan for a physical space, such as a sports facility.

[0663] "Presenting visually" is a method of expressing information clearly through visual means to facilitate understanding.

[0664] An "information processing system" is a system designed to collect, analyze, store, and process data.

[0665] A "user terminal" is an electronic device used by a user to access information and functions.

[0666] This invention is an information processing system for supporting the design and commercialization of sports facilities in urban areas. This system primarily consists of three components: a server, terminals, and users.

[0667] The server first collects relevant information from various data sources. This data includes demographics, tourist trends, and usage information for existing facilities. This data is stored in a database and undergoes data cleaning and normalization so that it can be used by generative AI models. Data cleaning corrects inconsistencies and missing information and standardizes the format.

[0668] Based on the collected and processed data, the server utilizes a generated AI model to predict facility demand and economic impact for each region. Specifically, the AI ​​model simulates the impact of increased tourism and the local economy. For example, by inputting a prompt such as, "Please output the estimated annual usage of a skateboard park in a city with a population of 200,000," the server will perform demand forecasting and generate design proposals.

[0669] The generated design proposal is sent from the server to the terminal and visualized. The terminal uses 3D modeling software to present the design proposal to the user in three dimensions, allowing the user to intuitively review and evaluate the plan. The user can evaluate the visualized design proposal and provide feedback.

[0670] Furthermore, after the user confirms the final design, the server converts the design into a format that can be output by a 3D printing device, such as STL format. This process makes it possible to output the facility design in a physical form in a specific format and utilize it in actual construction.

[0671] This system allows users to efficiently design sports facilities optimized for their region and predict their economic impact, supporting quick and accurate decision-making.

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] The server collects demographic data, tourist trends, and information on the use of existing facilities from diverse data sources. Input consists of raw data obtained through APIs and database connections. This data is stored in a cloud database for future analysis.

[0675] Step 2:

[0676] The server performs data cleaning and normalization on the collected data. Data cleaning involves imputing missing values ​​and removing outliers. Normalization unifies the data format and ensures consistency. This results in a standardized and clean dataset.

[0677] Step 3:

[0678] The server uses a generative AI model to forecast demand for urban sports facilities based on a clean dataset. The model is input with population growth and tourism data, and outputs predictive data through regression analysis and model learning using this data.

[0679] Step 4:

[0680] The server uses a generated AI model to simulate the economic effects of facility construction. It inputs regional economic indicators and previously predicted demand data to evaluate the potential increase in tourists to the region and job creation. As a result, it outputs a detailed economic impact report.

[0681] Step 5:

[0682] The server utilizes AI technology to generate facility design proposals that take into account user needs and regional characteristics. User profiles and environmental conditions are used as input data necessary for the design. The generated design proposals are output as 3D model data.

[0683] Step 6:

[0684] The terminal visually presents the design proposals received from the server. Using 3D modeling software, it displays 3D model data in a way that is easily understood visually by humans. Users can then evaluate the visualized design proposals.

[0685] Step 7:

[0686] Users evaluate design proposals and provide feedback via their terminals. The server receives this feedback and revises the design proposals. The final design proposal is converted to STL format, suitable for output on a 3D printing machine, and provided to the user.

[0687] (Application Example 1)

[0688] 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".

[0689] In designing virtual stores, there is a challenge in efficiently generating and visualizing optimal designs by reflecting customer behavior data in real time. In particular, there is a lack of tools for quickly and effectively evaluating and providing feedback on design proposals in three-dimensional space.

[0690] 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.

[0691] In this invention, the server includes means for generating data in a format that can be output by a three-dimensional printing device based on the generated design proposals, means for constructing a generative artificial intelligence model that predicts economic effects, and means for visualizing the design proposals on a display device in three-dimensional space in real time and optimizing the design based on customer behavior data. This enables users to effectively evaluate and quickly optimize virtual store design proposals.

[0692] "A means of generating data in a format that can be output by a 3D printing device based on the generated design proposal" refers to the process of converting digital data into a format that can be output as a physical model in order to visualize the design proposal of a virtual store in three dimensions.

[0693] "A means of constructing a generative artificial intelligence model to predict economic effects" refers to a method of simulating and predicting the economic impact of virtual store design using AI technology.

[0694] "A means of generating and visually presenting design proposals" refers to a technology that uses digital tools to create a virtual design of a store and visually displays the results to the user.

[0695] "Means for integrating and pre-processing collected data" refers to the process of aggregating data obtained from various sources and preparing it in a format suitable for analysis and model building.

[0696] "Methods for setting different simulation scenarios and comparing the results" refers to techniques for performing simulations based on multiple design hypotheses, comparing the results side-by-side, and evaluating optimal performance.

[0697] "A means of visualizing design proposals on a display device in three-dimensional space in real time and optimizing the design based on customer behavior data" refers to a technology that uses a visual device to display design proposals in three-dimensional space, and dynamically modifies and optimizes the design by reflecting actual customer data.

[0698] The "function that allows users to evaluate design proposals using a display device and provide feedback through operation" is an interface that allows users to view visualized virtual designs through a device and provide interactive feedback.

[0699] The system for realizing this invention incorporates advanced technology to assist in the design and optimization of virtual stores. The server first collects and integrates customer behavior data, demographics, and market trends. Next, it cleans and preprocesses this data into a format usable by the generating AI model. This allows for the construction of an AI model that predicts the economic impact of the virtual store and optimizes the customer experience.

[0700] The server generates store design proposals in three-dimensional space and presents them visually using display devices such as Oculus Quest and Microsoft HoloLens. Based on the displayed design, users provide direct feedback, which is reflected in the system in real time. Through this process, the store design proposals are continuously optimized and converted into appropriate data formats for output on a 3D printer.

[0701] As a concrete example, consider a clothing store designing a virtual store layout. The user wears smart glasses and walks around the virtual store, checking the placement of products and customer traffic. The server collects customer movement and eye-tracking information and uses a generated AI model to suggest the optimal layout. An example of a prompt used in this process would be: "Based on the latest customer traffic data, generate a design plan to optimize the virtual store layout for the clothing store. Pay particular attention to the placement around the cash registers and the back of the shelves."

[0702] Such a system allows users to repeatedly evaluate designs within a virtual environment and efficiently determine the optimal store layout. As a result, it promotes data-driven decision-making based on real-time information.

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] The server collects customer behavior data, demographic data, and market trend data. This includes acquiring data streams in real time from sensor devices and online platforms. This collected data is used as input data for generative AI models.

[0706] Step 2:

[0707] The server cleans and preprocesses the collected data, including imputing missing values, scaling, and denoising. The cleaned data is in a standardized format, contributing to improved accuracy of the AI ​​model. The output of this step becomes the input for the next AI model building step.

[0708] Step 3:

[0709] The server builds a generative AI model using a clean dataset. This model is optimized to predict the economic impact of a store and customer flow. In this process, a learning algorithm adjusts the model parameters to generate the optimal response. The generated AI model is used to generate design proposals.

[0710] Step 4:

[0711] The server uses a generative AI model to create design proposals for a virtual store. Considering the conditions and constraints based on the prompts, the model proposes the optimal layout and design. The server then prepares to output these generated design proposals in a 3D format.

[0712] Step 5:

[0713] The server enables visualization in three-dimensional space based on the generated design proposals. The design proposals are sent to display devices such as Oculus Quest and Microsoft HoloLens, and presented to the user as 3D visualizations. This visualization allows for a concrete experience of the design proposals.

[0714] Step 6:

[0715] Users explore a visualized virtual store and provide feedback as they move around. User actions and eye-tracking information are sent to a server, which then generates new data.

[0716] Step 7:

[0717] The server continuously optimizes itself based on user feedback. By combining the AI ​​model's predictions with user feedback, it evolves into a more accurate design proposal. Ultimately, the output design data provides the optimal store layout that aligns with the user's intentions.

[0718] 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.

[0719] This invention enables a more sophisticated decision-making process by integrating an emotion engine into a computer system that supports the design and commercialization of urban sports facilities. The system includes, as its main components, a data collection and pre-processing module, a demand forecasting and economic impact forecasting module, a design proposal generation module, an emotion engine, and a user interface.

[0720] The server first collects demographic data, tourism data, and usage information of existing facilities from various data sources and integrates them into a database. Next, it cleanses the collected data and converts it into a format usable by AI models through a normalization process.

[0721] Subsequently, the server builds a generative AI model, predicts facility demand for each region, and generates design proposals based on the predicted demand.

[0722] For the generated design proposals, the server recognizes the user's emotions through an emotion engine and evaluates the user's response. By analyzing the feedback and responses provided by the user using their device, the emotion engine extracts areas for improvement in the design proposal and automatically incorporates them into the design. This process results in a more convincing design proposal that can meet the user's latent needs.

[0723] After the above process is completed and the design proposal is finalized, the server converts the data into a format that can be output by a 3D printer (e.g., STL format) and presents a download link to the user via the terminal. The user can then use this data to create a physical model.

[0724] As a concrete example, consider a case where a user evaluates a skateboard park design proposal via their device. When the user reviews a new design, the emotion engine analyzes the user's facial expressions and keystrokes, prioritizing design elements that elicit positive reactions. Based on these positive reactions, the design proposal is refined and the final version is presented, facilitating smoother coordination with all parties involved.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The server automatically collects a wide variety of data from external data sources, such as demographic data, tourism data, and usage status of existing facilities, and stores it in an integrated database.

[0728] Step 2:

[0729] The server cleanses the collected data, removes outliers, and supplements missing data, converting it into a format that can be analyzed by the AI ​​model.

[0730] Step 3:

[0731] The server builds a generative AI model based on the formatted data and predicts the demand for urban sports facilities in each region.

[0732] Step 4:

[0733] The server uses AI to generate facility design proposals based on predicted demand. These design proposals take into account user needs and regional characteristics.

[0734] Step 5:

[0735] The device visually presents the generated design proposals to the user via a user interface, prompting them to evaluate them.

[0736] Step 6:

[0737] The emotion engine uses the device to collect user facial expression data and reactions in real time and analyzes the user's emotional state.

[0738] Step 7:

[0739] The server analyzes feedback from the emotion engine, prioritizes design elements that elicit positive responses, and refines the design proposal.

[0740] Step 8:

[0741] The device will then present the improved design proposal to the user again for final evaluation and approval.

[0742] Step 9:

[0743] The server converts the approved design proposals into a data format that can be output by a 3D printer and provides the user with a download link via the terminal.

[0744] Step 10:

[0745] Users can download the data from the provided link and create a physical model using a 3D printer.

[0746] (Example 2)

[0747] 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".

[0748] In designing modern urban sports facilities, it is difficult to efficiently formulate design proposals that accurately reflect local characteristics and user needs, and a high degree of accuracy is required in predicting the economic effects and demand of the design. Furthermore, traditional methods make it difficult to appropriately reflect users' emotions and opinions, often resulting in dissatisfaction or shortcomings in the final design proposal. There is a need to solve these problems and provide a more effective and convincing design process.

[0749] 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.

[0750] In this invention, the server includes means for integrating and normalizing data from various collected information sources to convert it into a form usable by a generative artificial intelligence model; means for predicting facility demand in each region using the generative artificial intelligence model and generating design proposals based on the results; and means including an emotion analysis device for analyzing user input information and emotional responses and improving the design proposals. This makes it possible to quickly formulate design proposals that accurately reflect user needs and regional characteristics, further improve the design proposals by incorporating user emotional responses, and present them in the most optimal form.

[0751] "Integrating and normalizing data from diverse sources" is the process of centrally combining information obtained from multiple different data sources and arranging it into a consistent format through statistical processing and transformation.

[0752] "Predicting regional facility demand using generative artificial intelligence models" refers to a technique that analyzes and estimates future facility usage demand by applying machine learning and data analysis technologies based on regional characteristics and historical data.

[0753] "Including an emotion analysis device that analyzes user input information and emotional responses to improve design proposals" means that the system is equipped with an analysis device that analyzes emotional feedback and directly entered opinions provided by users and uses that data to make modifications and improvements to design proposals.

[0754] "Converting to a data format that can be output by a 3D printing device" refers to the process of changing a digital design proposal into a digital format (e.g., STL format) that can be understood by a 3D printer or other output device, and then saving it, in order to manufacture it as a physical model.

[0755] "Providing a function that allows users to evaluate design proposals via a terminal and automatically generates revised proposals based on those evaluations" means a system that has the ability to collect feedback on design proposals through a user interface and automatically propose revised proposals that reflect that feedback.

[0756] This invention provides a configuration for a computer system integrating an emotion engine to support the design and commercialization of urban sports facilities. Through data collection, integration, and analysis, this system automatically generates sports facility designs that meet user needs, supporting efficient decision-making.

[0757] The server retrieves demographic data, tourism data, and usage information for existing facilities from diverse sources. This data is collected using API calls and database queries and integrated into a database. Next, the server uses the Python Pandas library to cleanse and normalize the data, thereby converting it into a format usable by AI models.

[0758] The generative AI model is built using machine learning frameworks such as TensorFlow or PyTorch. This model predicts facility demand for each region and creates appropriate design proposals based on the collected data. The generated design proposals are visually presented on the terminal through the user interface.

[0759] Users evaluate design proposals using a terminal, and input information from the terminal and emotional responses detected by the emotion engine are sent to the server. The emotion engine analyzes the user's facial expressions and input operations, and uses this data to improve the design proposals. Design elements that receive many positive responses are retained, and improvement suggestions are automatically provided.

[0760] Finally, the server converts the improved design into a format that can be printed on a 3D printer (e.g., STL format) and provides it to the user via the terminal. The user can then use this data to create a physical model.

[0761] As a concrete example, consider a case where a user evaluates a design proposal for a skateboard park. In this case, an example of a prompt message might be, "Based on the latest demographic and tourism data, forecast the demand for sports facilities and generate an appropriate design proposal." This system automates the entire process from design evaluation to improvement, facilitating smooth coordination among stakeholders.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The server collects demographic data, tourism data, and usage information for existing facilities from various information sources. Specifically, it retrieves data using Web APIs and imports local files, and stores this data in a database. The input is raw data from diverse information sources, and the output is an integrated database. This integrated data forms the basis for subsequent analysis.

[0765] Step 2:

[0766] The server performs data cleansing on the collected data. Specifically, it uses the Python Pandas library to detect and correct missing and outlier values, and remove duplicate data. The input for this step is the raw data obtained in step 1, and the output is a cleansed, consistent dataset. This improves the quality of the data and enables more accurate predictions.

[0767] Step 3:

[0768] The server normalizes the cleansed data and transforms it so that it can be used by the AI ​​model. Specifically, it scales and encodes the data to prepare the dataset for the model's input format. The input is the formatted data from step 2, and the output is the standardized data for the AI ​​model input. This normalized data improves the training and prediction accuracy of the AI ​​model.

[0769] Step 4:

[0770] The server performs demand forecasting using a generative AI model. Using TensorFlow or PyTorch, it processes normalized data to predict facility demand for each region. The input is the data prepared in step 3, and the output is the result of the demand forecast. Based on this forecast, the server is ready to proceed with design proposal generation.

[0771] Step 5:

[0772] The server generates design proposals based on demand forecasts. The design proposal generation module analyzes the output of the AI ​​model and creates facility design proposals that reflect user needs and regional characteristics. The input is demand forecast data, and the output is an initial design proposal. The generated proposals are then prepared for visual presentation.

[0773] Step 6:

[0774] The terminal visually presents the generated design proposal to the user. The user reviews the design proposal on the screen and provides feedback through the interface. The input is design proposal data from the server, and the output is user evaluation data. This allows the user to express their opinion on the design.

[0775] Step 7:

[0776] The server utilizes an emotion engine to analyze user feedback and derive improvements to the design proposal. The emotion engine analyzes user emotional responses and keystrokes to identify positive response elements. Input is user feedback and analysis information, and output is a design proposal including improvements. This process enables proposals that better meet user expectations.

[0777] Step 8:

[0778] The server compiles the improved design proposal into a final format and converts it into a format that can be output by a 3D printer. Specifically, it exports the design proposal data to formats such as STL. The input is the finalized design proposal, and the output is a file for 3D printing. With data in this format, the system is ready to create a physical prototype.

[0779] Step 9:

[0780] The terminal provides the user with a download link for the generated 3D data. The user can download the data via the link and proceed with 3D printing or model making. The input is the 3D data from the server, and the output is the download link and the data provided to the user.

[0781] (Application Example 2)

[0782] 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".

[0783] In modern store design, there is a demand for designs that accurately reflect customer needs. However, traditional design processes fail to effectively utilize user emotions and reactions, resulting in a challenge in achieving designs that are optimal for market demand and customer expectations. Furthermore, predicting the number of visitors to a store and improving its economic impact remains a difficult challenge.

[0784] 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.

[0785] In this invention, the server includes means for generating data in a format that can be output by a three-dimensional printing device based on the generated design proposal, means for constructing a generative artificial intelligence model that predicts economic effects, and means for analyzing user emotions and reflecting them in improving the design proposal. This makes it possible to reflect customer emotions in real time in store design and provide optimized design proposals. Furthermore, it is possible to simulate an increase in the number of visitors and improve economic effects.

[0786] "Means for generating data" refers to a method that has the function of converting the generated design proposal into data in a format that can be output by a three-dimensional printing device.

[0787] A "generative artificial intelligence model" is an intelligent system that uses machine learning algorithms to predict economic effects and calculates the performance of a facility under specific conditions.

[0788] "Means of generating and visually presenting design proposals" refers to the process of creating a concrete and visually understandable design prototype for the user and displaying it on a screen.

[0789] "Means for integrating and pre-processing collected data" refers to methods for organizing and integrating data obtained from various sources to make it analyzable.

[0790] "Methods for setting up different simulation scenarios and comparing the results" refers to methods for preparing multiple hypotheses and environmental conditions, and analyzing and evaluating the performance in each scenario.

[0791] "A means of analyzing user emotions and reflecting them in improving design proposals" refers to a system that analyzes user emotions in real time and adaptively improves the design based on that analysis.

[0792] "Means of acquiring evaluation data and adjusting design proposals" refers to the process of receiving user feedback and adjusting the design based on that feedback.

[0793] The embodiments for carrying out the invention are shown below.

[0794] In the system based on this invention, the server first collects demographic data, tourism data, and usage information of existing facilities from various data sources to form an integrated database. Before being stored in the database, the collected data undergoes a cleansing and normalization process to convert it into a format suitable for analysis.

[0795] The server then uses a generative artificial intelligence model to predict facility demand for each region and generates design proposals based on this. These design proposals are converted into a format that can be output by a 3D printer and presented visually through the user's terminal. The server incorporates an emotion engine to capture the user's emotions as a reaction to the design proposals, and has the ability to analyze emotions in real time through the user's facial expressions and actions. Based on this emotion analysis, the server automatically improves the design and provides the user with an optimized design proposal.

[0796] In this system, for example, a user visually evaluates a new interior design for a cafe. If the evaluation is accompanied by positive emotions, it indicates that design elements such as glass walls and the extensive use of plants are well-received by the user. This information is immediately reflected in improvements to the next design proposal. The entire operation on the terminal is supported by an interface that provides a comfortable user experience.

[0797] The hardware used includes computer servers to support data collection and analysis, and smart devices for users to visually review designs. The software includes image processing libraries such as OpenCV and sentiment analysis libraries such as DeepFace for emotion recognition.

[0798] A possible example of a specific prompt message would be something like, "We are considering interior design proposals for a new cafe. Please generate new proposals incorporating positive design elements that users have previously found appealing."

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The server collects information from various data sources, including demographic data, tourism data, and usage information for existing facilities. It uses external databases and APIs as input and stores the collected data as an initial dataset. This data serves as the foundation for integrating the information necessary for subsequent processing.

[0802] Step 2:

[0803] The server performs cleansing and normalization processes on the collected data. The input is the initial dataset obtained in step 1, and by processing it such as imputing missing values, removing outliers, and matching data types, it generates a clean, normalized dataset suitable for analysis as output. This dataset is used for training AI models and demand forecasting in the system.

[0804] Step 3:

[0805] The server uses a generative artificial intelligence model to predict facility demand in each region based on a clean dataset. The input is a normalized dataset, which is fed into the AI ​​model to execute the prediction algorithm, producing predicted values ​​for facility demand as output. These predicted values ​​are used to generate design proposals.

[0806] Step 4:

[0807] The server generates design proposals based on predicted facility demand and converts them into a data format for visual presentation on a terminal. Predicted facility demand is used as input, and a 3D modeling tool is used to generate output data that can serve as a visual design proposal. This data is then sent to a smart device for user evaluation.

[0808] Step 5:

[0809] The user visually evaluates the design proposals sent via their device. The input is a 3D design proposal sent from the server, and the emotion recognition system monitors the user's gaze, facial expressions, and movements in real time. This results in the output of user emotion data.

[0810] Step 6:

[0811] The server evaluates the design proposal based on the obtained user emotion data and automatically incorporates improvements to the design. User emotion response data is used as input, and an emotion analysis algorithm is applied to generate an optimized new design proposal as output. This new design proposal is then put into a further evaluation and improvement cycle.

[0812] 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.

[0813] 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 those described above. 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 shown 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.

[0814] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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."

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A means for generating data in a format that can be output by a 3D printing device based on the generated design proposal,

[0836] A means for constructing a generative artificial intelligence model that predicts economic effects,

[0837] A means of generating and visually presenting design proposals,

[0838] A means for integrating and pre-processing the collected data,

[0839] A means of setting up different simulation scenarios and comparing the results,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, which simulates an increase in tourists to a region using a generative artificial intelligence model.

[0843] (Claim 3)

[0844] The system according to claim 1, which provides a function on the terminal for the user to evaluate a design proposal and request revisions.

[0845] "Example 1"

[0846] (Claim 1)

[0847] A means for generating information in a format that can be output by a 3D printing device, based on the generated design outline,

[0848] A means of constructing a generative artificial intelligence model to predict economic impacts,

[0849] A means of generating and visually presenting a design overview,

[0850] A means for integrating and pre-processing the collected information,

[0851] A means of setting up different simulation scenarios and comparing the results,

[0852] An information processing system that includes this.

[0853] (Claim 2)

[0854] The information processing system according to claim 1, which simulates an increase in tourists to a region using a generative artificial intelligence model.

[0855] (Claim 3)

[0856] The information processing system according to claim 1, which provides a function on the user terminal for the user to evaluate the design outline and request revisions.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A means for generating data in a format that can be output by a 3D printing device based on the generated design proposal,

[0860] A means for constructing a generative artificial intelligence model that predicts economic effects,

[0861] A means of generating and visually presenting design proposals,

[0862] A means for integrating and pre-processing the collected data,

[0863] A means of setting up different simulation scenarios and comparing the results,

[0864] A means of visualizing design proposals in real time on a display device in three-dimensional space and optimizing the design based on customer behavior data,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, which uses a generative artificial intelligence model to simulate an increase in customers in a region and optimizes store placement.

[0868] (Claim 3)

[0869] The system according to claim 1, which provides a function for a user to evaluate a design proposal using a display device and to provide feedback through operation.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means of integrating and normalizing data from diverse collected sources to transform it into a form usable by generative artificial intelligence models,

[0873] A means for predicting facility demand in each region using a generative artificial intelligence model and generating design proposals based on the results,

[0874] A means including an emotion analysis device that analyzes user input information and emotional responses to improve design proposals,

[0875] A means for evaluating the generated design proposal and providing a display device for visually presenting the revised proposal,

[0876] A means of converting the finalized design proposal into a data format that can be output by a three-dimensional output device,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, which uses a generative artificial intelligence model to simulate the increase in tourists to a region and its economic impact.

[0880] (Claim 3)

[0881] The system according to claim 1, which provides a function for a user to evaluate a design proposal via a terminal and for the system to automatically generate a revised proposal based on that evaluation.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] A means for generating data in a format that can be output by a 3D printing device based on the generated design proposal,

[0885] A means for constructing a generative artificial intelligence model that predicts economic effects,

[0886] A means of generating and visually presenting design proposals,

[0887] A means for integrating and pre-processing the collected data,

[0888] A means of setting up different simulation scenarios and comparing the results,

[0889] A means of analyzing user emotions and reflecting them in improving design proposals,

[0890] A means of acquiring evaluation data using human reactions and adjusting the design proposal,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, which simulates an increase in the number of visitors to a store facility using a generated artificial intelligence model.

[0894] (Claim 3)

[0895] The system according to claim 1, which provides a function on a terminal for a human to evaluate a design proposal and request revisions. [Explanation of Symbols]

[0896] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating data in a format that can be output by a 3D printing device based on the generated design proposal, A means for constructing a generative artificial intelligence model that predicts economic effects, A means of generating and visually presenting design proposals, A means for integrating and pre-processing the collected data, A means of setting up different simulation scenarios and comparing the results, A system that includes this.

2. The system according to claim 1, which simulates an increase in tourists to a region using a generative artificial intelligence model.

3. The system according to claim 1, which provides a function on the terminal for the user to evaluate a design proposal and request revisions.

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