AI-based restaurant franchise startup consulting and profitability analysis system

Abstract

The present invention is characterized in that it comprises a user terminal having: a user input unit for user input; and a display unit for displaying at least one screen of an application or program for food service franchise startup consulting and profitability analysis that is already installed, in an AI-based food service franchise startup consulting and profitability analysis system.

Description

Technology Field

[0001] The present invention relates to an AI-based food service franchise startup consulting and profitability analysis system. Background Technology

[0002] Since startup consulting methods are primarily based on offline market research, expert empirical judgment, and limited sample data, there are limitations in enabling entrepreneurs to make quantitative and objective decisions. Furthermore, existing commercial area analysis and profitability forecasting techniques utilize data from a given point in time, making it difficult to reflect dynamic market changes and failing to contribute to continuous operational optimization after the startup.

[0003] Furthermore, while startup consulting systems provided some functions to analyze pedestrian traffic and consumption patterns in planned business locations, these relied on static data. Due to the lack of real-time data collection and machine learning-based prediction capabilities, it was difficult to expect high accuracy. Additionally, analyses of competitors' sales status, characteristics of key consumer groups, startup costs, and profitability were often performed individually or utilized simple statistical methods, posing a problem that made it difficult for entrepreneurs to make comprehensive judgments.

[0004] In other words, while existing consulting methods for franchise startups support entrepreneurs based on fixed patterns, they struggle to provide customized consulting that reflects the changing market environment as well as the individual entrepreneur's investment tendencies and operational styles. In particular, they have limited capabilities for predicting long-term growth potential by considering expected sales changes in a specific region after launch, or for analyzing cost volatility and profit fluctuations that may occur during the operation process in real time.

[0005] Accordingly, there is a growing need for an AI-based startup consulting and profitability analysis system that can support the owner's decision-making by analyzing the commercial area of ​​the planned startup location, evaluating competitor and consumer characteristics, predicting startup costs and expected profitability, and simultaneously analyzing post-startup operational data in real time. Prior art literature

[65535] Korean Patent Publication No. 10-2623050 (January 24, 2024) Korean Patent Publication No. 10-2689376 (July 26, 2024) The problem to be solved

[0006] The present invention provides an AI-based food service franchise startup consulting and profitability analysis system, enabling entrepreneurs to select an optimal startup location and analyze expected investment costs and profitability based on more precise and objective data. Unlike existing startup consulting methods that rely on empirical and limited data, the invention collects and analyzes large-scale real-time data and utilizes machine learning and deep learning techniques to provide quantitative analysis results that support the entrepreneur's decision-making.

[0007] By analyzing population density, floating population, commercial district types, the number of competitors, and sales status of a planned business location, it is possible to predict changes in customer inflow by specific time periods, thereby enabling entrepreneurs to more accurately assess the growth potential of a specific area. Furthermore, by linking and analyzing data from delivery applications, social media, and search portals, it is possible to quantitatively predict the demand for specific menu items, allowing entrepreneurs to formulate business strategies that reflect the impact of changes in consumer trends at a specific point in time.

[0008] We can provide an AI-based food service franchise startup consulting and profitability analysis system that goes beyond simple startup consulting to support operational optimization through continuous data learning after launch. This system analyzes the owner's management style and investment propensity to recommend customized startup models, and collects real-time sales and cost data to provide automated management feedback, thereby assisting the founder in making optimal decisions. This system calculates the founder's initial investment costs, evaluates the potential for investment recovery by analyzing expected operating costs and break-even points, precisely predicts expected profitability when starting a business in a specific region by learning from data of similar past startup cases, and enables the founder to establish long-term management strategies by considering the variability of operating costs. means of solving the problem

[0009] An AI-based food service franchise startup consulting and profitability analysis system according to one embodiment of the present invention is characterized by comprising: a user input unit for user input; and a display unit for displaying at least one screen of an application or program for food service franchise startup consulting and profitability analysis that is already installed; and a user terminal.

[0010] The system includes a server connected to the user terminal via a wired or wireless network, wherein the server includes a communication unit; a storage unit; and a control unit. The control unit collects communication network traffic, GPS-based movement patterns, and public transportation usage data of a region to identify population density, floating population, and commercial area types in a planned business location, and identifies changes in customer inflow by specific time periods through time-series analysis thereof; quantifies customer preference for each brand by linking delivery app, SNS, and search portal data to analyze the number of competitors, sales status, menu prices, customer reviews, and online ratings, and compares and analyzes average order volume and price competitiveness for specific menus; identifies consumption patterns by age, gender, and income level by comprehensively evaluating local card payment data, membership sign-up information, and online search trends to segment the main consumer base within the commercial area, and forms detailed customer groups by applying an AI-based clustering model; and calculates the probability of customer inflow by considering major commercial facilities, public transportation usage patterns, and distance from residential areas by analyzing location-based service usage data of the region to predict customer movement paths and visit frequency.

[0011] The above control unit is characterized by extracting the average rent and labor cost growth rates of the region using public databases, regional real estate transaction information, and labor market statistics to subdivide and analyze regional operating costs into rent, labor costs, material costs, maintenance costs, utility costs, and marketing costs, and identifying future cost volatility using a time series forecasting model; to calculate the expected startup cost in a specific region, it learns past startup case data to derive the average investment cost incurred in similar commercial areas and store sizes, and sets a reliable range of startup costs by excluding abnormally high or low cases by applying a new learning-based anomaly detection algorithm; to calculate the payback period relative to investment based on the startup time and expected operating period, it derives the break-even point by comparing the monthly average expected sales and operating costs in the specific region, and quantitatively evaluates the possibility of investment recovery after startup by backtracking and analyzing the expected time of profit generation; to optimize the profit model, it calculates the number of customers required and the average transaction value per sales target by considering operating cost fluctuation factors, and simulates fluctuations in profitability due to seasonal factors, promotional effects, and competitor price changes through multiple regression analysis reflecting real-time operating data.

[0012] The above-described control unit is characterized by analyzing the real estate price increase rate, population growth rate, and development plans for new commercial facilities in the startup area to identify factors likely to increase sales after a startup in a specific area, deriving correlations by comparing them with sales growth rates after startup in similar past areas, and performing mention frequency and sentiment analysis for specific menus by analyzing content from Instagram, YouTube, and blogs using natural language processing (NLP) technology to evaluate the preferences of potential consumers for specific brands and menus by combining customer visit data and social media trend analysis, predicting consumption patterns reflecting real-time trend changes, and dynamically adjusting store operation strategies based on demand forecast results. This involves learning past sales data from nearby areas to predict potential sales increases or decreases when specific events, including local festivals, sports games, and large-scale concerts, occur, and recommending strategies such as deploying additional personnel, applying promotions, and focusing sales on specific menus during the event period. Furthermore, to quantitatively predict expected customer loyalty, brand revisit rates, and word-of-mouth effects at a specific point in time after a startup, the control unit analyzes past customer review data, membership sign-up rates, and SNS sharing frequency to learn patterns of high customer retention rates in specific stores and evaluates the long-term brand growth potential in the area.

[0013] The above-described control unit is characterized by applying spatial data analysis techniques to score customer density and movement paths by weighting the average to derive a candidate location for starting a business that meets the most favorable location conditions within a given area by linking and analyzing multiple data sources including satellite maps, mobile carrier location data, card usage history, and delivery platform data, and visually presenting a location suitable for starting a business within a specific commercial district; classifying the owner's survey response data, past startup cases, and preferred business operation methods using a machine learning-based decision tree algorithm to analyze the owner's personal management style and investment propensity, and recommending a corresponding startup model; providing a dynamic price optimization algorithm that calculates the optimal menu price within a specific area by reflecting real-time competitor price fluctuation data, comparing average prices per menu item collected from delivery apps and online ordering systems, and performing price elasticity analysis to calculate the optimal point where customer response is maximized at a specific price range; and, for optimizing the store's internal layout, performing space utilization simulations based on customer movement data and table turnover rate, and proposing measures to improve operational efficiency by comparing average waiting times and customer satisfaction in specific layouts.

[0014] The control unit collects real-time operational data during the initial six months after opening to continuously analyze sales, customer counts, return visit rates, and promotion effectiveness, and applies a non-linear time-series forecasting model to analyze the growth curve of each store using multiple variables in order to recommend real-time operational improvement measures tailored to the store owner's operating style; predicts the point of operational stabilization based on the bivariate or multivariate distribution of sales trends during the initial period of opening; and, to provide the store owner with immediate response strategies when specific sales reduction factors—such as increased material costs, rising labor costs, or intensified promotions by competitors—are detected, it applies Principal Component Analysis (PCA) and K-means clustering to form groups of stores with similar patterns, derives effective response measures within those groups, and evaluates the risk of sales reduction for each store based on weights; utilizes an AI-based virtual customer analysis model to simulate customer reactions to specific menus; predicts sales probabilities for each menu based on probability distributions using Monte Carlo simulation techniques to optimize menu composition and promotion strategies by reflecting real-time feedback; progressively corrects the expected profitability of the corresponding menu through Bayesian updates; and learns from continuous operational data after opening to create new To provide founders with a more sophisticated prediction of success potential, the invention is characterized by combining a multiple regression model and an LSTM-based deep learning model to learn the growth patterns of the founder's store over time, and vectorizing key operational variables of each store, including average transaction value, table turnover rate, and delivery proportion, to construct a business prediction model for new founders. Effects of the invention

[0015] By providing an AI-based food service franchise startup consulting and profitability analysis system, it enables entrepreneurs to select optimal startup locations and analyze estimated investment costs and profitability based on more precise and objective data. Unlike existing startup consulting methods that rely on empirical and limited data, it collects and analyzes large-scale real-time data and utilizes machine learning and deep learning techniques to provide quantitative analysis results that support entrepreneurs' decision-making.

[0016] By analyzing population density, floating population, commercial district types, the number of competitors, and sales status of a planned business location, it is possible to predict changes in customer inflow by specific time periods, thereby enabling entrepreneurs to more accurately assess the growth potential of a specific area. Furthermore, by linking and analyzing data from delivery applications, social media, and search portals, it is possible to quantitatively predict the demand for specific menu items, allowing entrepreneurs to formulate business strategies that reflect the impact of changes in consumer trends at a specific point in time.

[0017] This provides an AI-based food service franchise startup consulting and profitability analysis system that goes beyond simple startup consulting to support operational optimization through continuous data learning after launch. It analyzes the owner's management style and investment propensity to recommend customized startup models, and collects real-time sales and cost data to provide automated management feedback, thereby assisting the founder in making optimal decisions. This system calculates the founder's initial investment costs, evaluates the potential for return on investment by analyzing expected operating costs and break-even points, precisely predicts expected profitability in specific regions by learning from data of similar past startup cases, and enables the founder to establish long-term management strategies by considering the variability of operating costs. Brief explanation of the drawing

[0018] FIG. 1 is a diagram illustrating the schematic configuration of an AI-based food service franchise startup consulting and profitability analysis system according to one embodiment of the present invention. FIG. 2 is a diagram illustrating the specific configuration of an AI-based food service franchise startup consulting and profitability analysis system according to one embodiment of the present invention. FIG. 3 is a flowchart illustrating the operation of an AI-based food service franchise startup consulting and profitability analysis system according to one embodiment of the present invention. Specific details for implementing the invention

[0019] Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the drawings, identical reference numbers or symbols refer to components that perform substantially the same function, and the size of each component in the drawings may be exaggerated for clarity and convenience of explanation. However, the technical concept of the present invention and its core components and operations are not limited only to the components or operations described in the following embodiments. In describing the present invention, if it is determined that a detailed description of known technologies or components related to the present invention may unnecessarily obscure the essence of the present invention, such detailed description will be omitted.

[0020] In embodiments of the present invention, terms including ordinal numbers, such as first, second, etc., are used solely for the purpose of distinguishing one component from another, and singular expressions include plural expressions unless the context clearly indicates otherwise. Furthermore, in embodiments of the present invention, terms such as 'composed of,' 'include,' 'have,' etc., should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. Additionally, in embodiments of the present invention, 'module' or 'part' performs at least one function or operation and may be implemented in hardware or software, or as a combination of hardware and software, or may be integrated into at least one module and implemented as at least one processor. Furthermore, in embodiments of the present invention, 'at least one' among a plurality of elements refers not only to all of the plurality of elements but also to each individual element excluding the remainder or all combinations thereof. Additionally, "configured to" may be used interchangeably with, depending on the context, for example, "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." "Configured to" does not necessarily mean that it is "specifically designed to" in hardware. Instead, in some situations, the expression "device configured to" may mean that the device is "capable of" doing so in conjunction with other devices or components.For example, the phrase “a processor configured (or set) to perform A, B, and C” may mean a dedicated processor for performing said operations (e.g., an embedded processor), or a generic-purpose processor (e.g., a CPU or an application processor) capable of performing said operations by executing one or more software programs stored in a memory device.

[0021] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. This description is intended to be detailed enough for a person skilled in the art to easily practice the invention, and it should be noted that the technical scope and concept of the present invention are not limited thereby.

[0022] FIG. 1 is a diagram illustrating the schematic configuration of an AI-based food service franchise startup consulting and profitability analysis system according to one embodiment of the present invention, FIG. 2 is a diagram illustrating the specific configuration of an AI-based food service franchise startup consulting and profitability analysis system according to one embodiment of the present invention, and FIG. 3 is a flowchart illustrating the operation of an AI-based food service franchise startup consulting and profitability analysis system according to one embodiment of the present invention.

[0023] Referring to FIGS. 1 to 3, an AI-based food service franchise startup consulting and profitability analysis system according to one embodiment of the present invention comprises a user input unit (110) for user input; and a display unit (120) for displaying at least one screen of an application or program for food service franchise startup consulting and profitability analysis that has been installed, and comprises a server (200) connected to the user terminal (100) via a wired or wireless network.

[0024] AI according to one embodiment of the present invention refers to a technology that includes various techniques such as machine learning, deep learning, natural language processing (NLP), time series analysis, cluster analysis, and reinforcement learning, and means a system designed to analyze collected data and identify specific patterns to predict future results or derive optimal decisions. The AI ​​can process not only structured data but also unstructured data, and has the characteristics of learning correlations between data and continuously improving performance through an iterative feedback process.

[0025] AI can be implemented in various forms, and methods such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning can be applied. Supervised learning is a method of training a model based on historical data to predict the output for a given input, and it can be utilized to solve classification and regression problems. Unsupervised learning is a method of discovering patterns in data without clear correct answers, and it can be used for clustering or dimensionality reduction. Semi-supervised learning is a method that improves performance by utilizing data where labels exist only for a portion of the data, while reinforcement learning is a technique in which an agent learns by interacting with the environment to maximize rewards, making it advantageous for solving optimization problems.

[0026] Deep learning is a technology that models complex non-linear relationships using multi-layer neural networks and can be utilized in various fields such as image recognition, speech recognition, and natural language processing. Convolutional Neural Networks (CNNs) are primarily used for image data processing, while Recurrent Neural Networks (RNNs) and Long-Term Recurrent Neural Networks (LSTMs) can be utilized as models suitable for time-series data or natural language processing. Additionally, applications such as machine translation, sentence generation, and semantic analysis based on Transformer models are also possible.

[0027] AI can be implemented in various ways depending on the nature of the given problem, and predictive performance can be improved by combining multiple models using ensemble learning techniques, in addition to a single model. AI models can process not only static data but also real-time data streams, and can also be operated in an online learning manner that reflects new patterns through continuous learning.

[0028] The AI ​​of the present invention is not limited to specific algorithms or data processing methods, and various artificial intelligence technologies may be applied. Furthermore, future AI technologies may be utilized to realize the concept of the present invention and may be applied flexibly without relying on specific models or techniques. The role and function of the AI ​​may be adjusted in various ways depending on the implementation method of the present invention and may include a wide range of artificial intelligence technologies without being limited to specific technical implementations.

[0029] A food service franchise according to one embodiment of the present invention refers to a form of food service business in which a headquarters provides a certain system and operating method to franchisees to maintain the same brand and standardized services. The food service franchise applies recipes, menu composition, service processes, interior design, marketing strategies, etc., developed by the headquarters to franchisees according to unified standards, and franchisees can increase operational efficiency by utilizing the headquarters' management know-how and brand recognition.

[0030] Food service franchises generally operate through a contract between a franchisor and a franchisee, with the franchisor playing a role in supporting the opening of franchise locations and managing overall store operations. Franchisees pay initial startup costs, operate their stores utilizing the franchisor's brand, menu, and operational manuals, and may pay a fixed royalty to the franchisor. The franchisor can provide continuous training, quality control, marketing support, and logistics to ensure smooth operations, and may conduct regular evaluations and monitoring to maintain the consistency of the franchise system.

[0031] Foodservice franchises are designed to provide consumers with consistent quality and service, and can maintain brand identity by applying standardized menus and cooking methods. The headquarters can continuously strengthen the competitiveness of franchisees by developing new menus, conducting customer satisfaction surveys, and analyzing market trends, and can also provide customized operational strategies tailored to the consumption patterns of specific regions. Furthermore, operational efficiency can be maximized by utilizing various sales channels, such as delivery services, takeout, and kiosk ordering systems.

[0032] The operational methods of food service franchises may change in accordance with market conditions and technological advancements, and can expand from existing offline-centric stores to the introduction of online-based ordering systems, data-driven store operations, and automated cooking systems. The food service franchise of the present invention is not limited to a specific form or operational method, and can be applied by combining various business models and technological elements to maximize the efficiency of franchise operations and increase the likelihood of success for the founder.

[0033] The food service franchise startup consulting and profitability analysis according to one embodiment of the present invention refers to a series of processes and systems that support an entrepreneur planning to start a food service franchise in selecting an optimal location, analyzing expected investment and operating costs, and predicting future profitability. Food service franchise startup consulting plays a role in providing necessary information to the entrepreneur during the process of preparing for market entry and helping to establish effective startup strategies; furthermore, beyond simply providing startup information, it can support more precise startup decision-making through data-based analysis.

[0034] Food service franchise startup consulting generally involves elements such as market research, commercial area analysis, competitor analysis, customer demand forecasting, menu selection, initial investment cost analysis, operating cost analysis, and break-even point (BEP) analysis. Commercial area analysis can be conducted to determine the suitability of a business in a planned location by evaluating factors such as population density, pedestrian traffic, the distribution of commercial facilities, and the characteristics of the main consumer base. Competitor analysis can be applied to assess the entrepreneur's potential for market entry by utilizing data on the number of similar businesses in the area, average sales levels, menu price ranges, brand awareness, and customer reviews and ratings.

[0035] Profitability analysis is a process designed to predict the return a founder can expect relative to their investment. It can be conducted by calculating the break-even point, which includes initial startup costs (interior design, kitchen equipment, franchise fees, security deposits, etc.) and operating expenses (rent, labor, materials, marketing, etc.). The process may involve forecasting expected monthly sales in a specific region and comparing them to estimated operating costs to assess the potential for investment recovery within a certain period. Additionally, a customized profit model tailored to the founder's investment style and operational approach may be applied.

[0036] The food service franchise startup consulting and profitability analysis of the present invention is not limited to specific techniques or methodologies, but can be implemented in a manner that supports the founder in establishing more sophisticated business strategies by utilizing various analysis techniques and data sources. Startup simulations may be provided by comprehensively considering the founder's management style, target sales, operational efficiency, and customer acquisition strategies, and management strategies may be continuously optimized by reflecting real-time operational data after the startup. Accordingly, the present invention can provide the effect of enabling the founder to make more stable decisions and maximize long-term profitability.

[0037] An application or program according to one embodiment of the present invention refers to software that can be executed on a user terminal and is developed to perform a specific function. The application or program may be executed independently on a single device, or it may be implemented in a form that exchanges data and performs specific operations by interacting with a server via a network. Additionally, it may include functions for receiving and processing user input, displaying operation results on a screen, or performing specific actions, and it may be executed in a local environment or operated as a cloud-based service.

[0038] Applications or programs can be implemented to run on various operating systems without being dependent on a specific platform. For example, in a mobile environment, they can be provided in the form of native applications, web-based applications, or hybrid applications running on operating systems such as iOS and Android, while in a desktop environment, they can function as standalone software or web applications running on operating systems such as Windows, macOS, and Linux. Additionally, when implemented as a web-based program, they may be provided in a form accessible through a browser, or they may be linked with external systems using APIs (Application Programming Interfaces).

[0039] The application or program of the present invention can be designed to visually provide information related to startup consulting and profitability analysis through the display of a user terminal, and to generate analysis results by processing user input in real time. The algorithm for performing data analysis may be executed in a local environment or may perform advanced analysis by linking with a remote server. In addition, it may include functions to provide customized analysis results based on user account information and to save or retrieve specific data according to user requests.

[0040] Applications or programs are not limited to specific functions or implementation methods and can be applied in various ways depending on user requirements and environments. They may include functions for automating data input and analysis, and may provide user-customized information in real time by utilizing AI-based models. Furthermore, applications or programs may add new functions through continuous updates and expansions, and can be implemented to enable comprehensive operation by including elements such as databases, networks, and user interfaces. The present invention is not limited to a specific development environment or execution method and can be applied to enable various technical implementations.

[0041] A user terminal (100) according to one embodiment of the present invention includes, for example, a personal computer, a server computer, a handheld or laptop device, a mobile device (mobile phone, PDA, media player, etc.), a multiprocessor system, a consumer electronic device, a mini computer, a mainframe computer, a distributed computing environment including any of the aforementioned systems or devices, and an edge computing environment in which data is processed at the edge where data is generated rather than at a central server, and the configuration is not limited only to what is described.

[0042] The user terminal (100) may include at least one processor and memory. Here, the processor may include, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), and may have multiple cores.

[0043] The memory may be volatile memory (e.g., RAM, etc.), non-volatile memory (e.g., ROM, flash memory, etc.), or a combination thereof. Additionally, the user terminal (100) may include additional storage. The storage includes, but is not limited to, magnetic storage, optical storage, etc. The storage may store computer-readable instructions for implementing one or more embodiments disclosed herein, and may also store other computer-readable instructions for implementing an operating system, an application program, etc. Computer-readable instructions stored in the storage may be loaded into memory to be executed by a processor.

[0044] Additionally, the user terminal (100) may include a user input unit (110) and an output device. The user input unit (110) may include, for example, a keyboard, a mouse, a pen, a voice input device, a touch input device, an infrared camera, a video input device, or any other input device. Additionally, the output device may include, for example, one or more displays, speakers, printers, or any other output devices. Additionally, the computing device may use an input device or an output device provided in another computing device as the user input unit (110) or output device. Additionally, the computing device may include a communication module that enables the computing device to communicate with another device. Here, the communication module may include a modem, a network interface card (NIC), an integrated network interface, a radio frequency transmitter / receiver, an infrared port, a USB connection, or other interfaces for connecting the computing device to another computing device. The communication module may include a wired connection or a wireless connection.

[0045] Each component of the user terminal (100) may be connected by various interconnections such as a bus (e.g., peripheral component interconnection (PCI), USB, firmware (IEEE 1394), optical bus structure, etc.) or may be interconnected by a network. Terms such as “component,” “system,” etc. used in this specification generally refer to computer-related entities that are hardware, a combination of hardware and software, software, or running software.

[0046] A user terminal (100) according to one embodiment of the present invention may include a display unit (120), and the method of implementing the display of the display unit (120) is not limited and may be implemented in various display methods such as liquid crystal, plasma, light-emitting diode, organic light-emitting diode, surface-conduction electron-emitter, carbon nanotube, nanocrystal, etc. In the case of a liquid crystal method, the display unit (120) includes a liquid crystal display panel, a backlight unit that supplies light to the liquid crystal display panel, and a panel driving unit that drives the liquid crystal display panel. Meanwhile, the display unit (120) may be implemented as an OLED panel, which is a self-emissive element, without a backlight unit.

[0047] A server (200) according to one embodiment of the present invention may include a communication unit (210), a storage unit (220), and a control unit (230).

[0048] A communication unit (210) according to one embodiment of the present invention can communicate with a user terminal (100) or other external electronic devices, etc., via wired or wireless communication methods. Therefore, in addition to a connection unit including a connector or terminal for wired connection, it can be implemented in various other communication methods. For example, it can be configured to perform one or more of Wi-Fi, Bluetooth, Zigbee, infrared communication, Radio Control, Ultra-Wide Band (UWM), Wireless USB, and Near Field Communication (NFC). The communication unit (210) may include communication modules such as Bluetooth Low Energy (BLE), Serial Port Profile (SPP), Wi-Fi Direct, infrared communication, Zigbee, and Near Field Communication (NFC). Additionally, the communication unit (210) can be implemented in the form of a device, a software module, a circuit, a chip, etc.

[0049] A communication unit (210) according to one embodiment of the present invention may include various communication modules as described above, and may include an IoT communication module having an IoT network for each carrier. An IoT communication module may refer to any IoT communication network in which a plurality of objects having separate communication units (210) are connected through a network to enable services based on various platforms. By using such an IoT communication module, a smoother communication network can be provided within a set area.

[0050] A storage unit (220) according to one embodiment of the present invention may receive information from a user terminal (100) or from an external search platform through a communication unit (210) and store it, and may receive text information and image information included in a plurality of web pages received by a control unit (230) and store it. The storage unit (220) may store various data according to the processing and control of the control unit (230) described later. The storage unit (220) may be accessed by the control unit (230) to perform reading, recording, modification, deletion, updating, etc. of data. The storage unit (220) may include non-volatile memory such as flash memory, hard disk drive, and SSD (solid-state drive) so as to preserve data regardless of whether system power is provided to the server (200). Additionally, the storage unit (220) may include volatile memory, such as a buffer or RAM, for temporarily loading data processed by the control unit (230).

[0051] A control unit (230) according to one embodiment of the present invention can perform control for the operation of various components of a server (200). The control unit (230) may include a control program (or instruction) that enables such control operation, an inactive memory in which the control program is installed, a volatile memory in which at least a portion of the installed control program is loaded, and at least one processor or CPU (Central Processing Unit) that executes the loaded control program. In addition, such a control program may be stored in an external electronic device other than the server (200).

[0052] The control program may include program(s) implemented in at least one form among BIOS, device driver, operating system, firmware, platform, and application. In one embodiment, the application may be pre-installed or stored in the server (200) at the time of manufacturing the server (200), or may be installed in the server (200) based on the received data by receiving data of the application from an external source when it is used later. The data of the application may be downloaded to the server (200) from an external server, such as an application market, for example, a platform according to the present invention, but is not limited thereto. Meanwhile, the control unit (230) may be implemented in the form of a device, S / W module, circuit, chip, etc., or a combination thereof.

[0053] A control unit according to one embodiment of the present invention collects communication network traffic, GPS-based movement patterns, and public transportation usage data of a region to identify population density, floating population, and commercial area types in a planned business location, identifies changes in customer inflow by specific time periods through time-series analysis thereof, quantifies customer preference for each brand by linking delivery app, SNS, and search portal data to analyze the number of competitors, sales status, menu prices, customer reviews, and online ratings, and compares and analyzes the average order volume and price competitiveness for each specific menu; identifies consumption patterns by age, gender, and income level by comprehensively evaluating local card payment data, membership sign-up information, and online search trends to segment the main consumer base within the commercial area, forms detailed customer groups by applying an AI-based clustering model, and calculates the probability of customer inflow considering major commercial facilities, public transportation usage patterns, and distance from residential areas by analyzing location-based service usage data of the region to predict customer movement paths and visit frequency.

[0054] A control unit according to one embodiment of the present invention may be implemented by collecting and analyzing various data sources to identify population density, floating population, and commercial area types in a planned startup area. Communication network traffic data for the area may include data transmission volume by specific time period and day of the week, base station connection frequency, Wi-Fi connection records, etc., and based on this, the flow of floating population in the specific area can be estimated. GPS-based movement pattern data, including vehicle navigation systems, smartphone location services, and transportation card usage data, may be utilized to analyze movement frequency and major movement routes in the specific area. Public transportation usage data may be applied to evaluate accessibility to the startup area by analyzing the number of passengers by specific time period and route based on boarding and alighting records collected from buses, subways, taxi hailing systems, etc. Such data may be applied to identify changes in customer inflow by specific time period using time-series analysis techniques, and may be designed to reflect seasonality, weekday and weekend patterns, and changes during holidays and special event periods.

[0055] The control unit of the present invention can quantitatively analyze the market share of competitors by collecting store lists, order frequency, delivery areas, average price ranges, discount information, etc., provided by delivery applications to analyze the number of competitors, sales status, menu prices, customer reviews, and online ratings. Social media data, including the frequency of mentions for specific brands and menus, sentiment analysis results of consumer reviews, and hashtag and keyword trends, can be utilized to quantify brand preference. Search portal data, including search volume for specific menus or brands, related search terms, regional search trends, and user reviews and rating data, can be applied to evaluate the awareness and market competitiveness of a specific franchise. Through such analysis, it can be utilized to compare the characteristics of competitors' main customer bases, pricing strategies, and promotional methods, and to quantitatively evaluate the average order volume and price competitiveness for each specific menu.

[0056] To segment key consumer groups within a commercial area, credit card payment data can be applied by analyzing customer payment amounts, consumption patterns by time of day, and the proportion of spending by specific brands and industries. Membership sign-up information can be utilized to assess customer brand loyalty based on loyalty programs and to classify customer groups with specific consumption patterns. Online search trends can be applied to measure interest in specific brands or menus and to reflect changes in consumption trends within the market. By applying AI-based clustering models to this data, specific customer groups can be formed; for instance, they can be segmented and analyzed into categories such as family customers, office workers seeking lunch, customers for evening company dinners, and delivery customers.

[0057] To predict customer movement paths and visit frequency, location-based service usage data, including smartphone GPS data, vehicle navigation data, and transportation card usage data, can be applied by analyzing usage patterns of major commercial facilities and public transportation. For example, the probability of customer inflow from a specific area can be calculated by evaluating the distance relationships between shopping malls, subway stations, bus stops, and residential areas, and this can be implemented by quantitatively assessing how closely a specific store's location aligns with customer movement paths.

[0058] The control unit of the present invention combines such diverse data to enable founders to perform more sophisticated market analysis, and can be implemented in various ways without being limited to specific data sources or analysis techniques. A person skilled in the art can apply the concept of the present invention to combine various forms of data collection and analysis methods, and a flexible design is possible to expand startup consulting and profitability analysis functions by reflecting future technological advancements.

[0059] A control unit according to one embodiment of the present invention is characterized by extracting the average rent and labor cost growth rates of a region by utilizing public databases, regional real estate transaction information, and labor market statistics to subdivide and analyze regional operating costs into rent, labor costs, material costs, maintenance costs, utility costs, and marketing costs, and identifying future cost volatility using a time series forecasting model; learning past startup case data to derive the average investment cost incurred in similar commercial areas and store sizes to calculate the expected startup cost in a specific region, and applying a machine learning-based anomaly detection algorithm to exclude abnormally high or low cases to set a reliable range of startup costs; deriving the break-even point by comparing the average monthly sales and operating costs in a specific region to calculate the payback period relative to the investment based on the startup time and expected operating period, and quantitatively evaluating the possibility of investment recovery after startup by backtracking and analyzing the expected time of profit generation; calculating the number of customers required and the average transaction value per sales target by considering operating cost fluctuation factors to optimize the profit model, and simulating fluctuations in profitability due to seasonal factors, promotional effects, competitor price changes, etc. through multiple regression analysis reflecting real-time operating data.

[0060] A control unit according to one embodiment of the present invention may be implemented by performing a quantitative evaluation using various data sources to subdivide and analyze regional operating costs. For rent analysis, the average rent level by region may be calculated based on public databases and regional real estate transaction information, and future rent growth rates may be predicted by reflecting commercial real estate market trends. To this end, regional rent fluctuation patterns may be derived by learning commercial real estate transaction data from the past several years, and expected rent changes in a specific startup region may be quantitatively analyzed by applying a time-series forecasting model to the data.

[0061] For labor cost analysis, labor market statistical data can be utilized to derive the average labor cost for the region, and the potential for future operating cost increases can be predicted by considering wage growth rates by industry. By reflecting regional minimum wage levels, average salaries by sector, and recruitment market trends, the recruitment costs required for specific food service businesses can be calculated, and labor cost optimization strategies can be proposed by taking into account changes in labor demand by time of day and season.

[0062] To analyze material costs, data on food ingredient distribution networks is collected to analyze the price volatility of key raw materials in specific regions, and estimated material costs can be calculated by reflecting supply chain stability and seasonal supply and demand changes. Optimal cost reduction strategies can be derived by considering price negotiation power with regional food ingredient suppliers, logistics costs, and the ratio of domestic raw materials to imported ones.

[0063] To analyze maintenance costs, facility maintenance records from similar industries can be analyzed to calculate average maintenance costs by sector. By analyzing the usage and maintenance cycles of cooking equipment, HVAC systems, and electrical and plumbing facilities, expected costs are quantitatively evaluated, and long-term maintenance cost forecasting is possible by reflecting the expected lifespan and replacement costs of specific equipment.

[0064] For utility analysis, energy consumption costs, including regional water, sewage, electricity, and gas rates, are evaluated, and projected utility costs after business launch can be calculated by considering average energy consumption patterns by industry. By analyzing projected monthly energy consumption based on the planned store's area, operating hours, and cooking equipment usage, a cost optimization strategy can be established by taking into account government support policies and potential for reduction.

[0065] To analyze marketing costs, the projected marketing budget after launch can be calculated by analyzing local consumption trends, the effectiveness of advertising channels, and competitors' marketing strategies. By quantitatively analyzing the effectiveness of social media advertising, flyer distribution, and promotional events, support can be provided to help entrepreneurs select the most appropriate marketing strategy.

[0066] To calculate estimated startup costs in a specific region, data from past startup cases is trained to derive average investment costs incurred in similar commercial areas and store sizes. By applying a machine learning-based anomaly detection algorithm to exclude abnormally high or low cases, a highly reliable range of startup costs can be established. Through this, entrepreneurs can predict the possibility of excessive costs at the time of startup and efficiently allocate their investment budget.

[0067] To calculate the payback period relative to the investment based on the startup timing and expected operating period, the break-even point is derived by comparing the average monthly expected sales and operating costs in a specific region. Furthermore, the feasibility of recovering the investment after startup can be quantitatively evaluated by backtracking the expected timing of profit generation. Break-even point (BEP) analysis can be applied by evaluating the cost-to-sales ratio to predict within how many months of startup the initial investment can be recovered.

[0068] To optimize the revenue model, the required number of customers and average transaction value per sales target are calculated by considering factors affecting operating cost fluctuations. Furthermore, through multiple regression analysis reflecting real-time operational data, fluctuations in profitability caused by seasonal factors, promotional effects, and competitor price changes can be simulated. For example, by reflecting expected customer influx patterns at specific times, sales growth rates when applying discount promotions, and changes in demand due to price adjustments, it enables founders to establish optimal pricing policies and sales strategies.

[0069] The control unit of the present invention is not limited to specific data sources or analysis techniques and can be designed to evaluate startup costs and profitability by including various analysis elements required by the founder. Furthermore, unlike existing static data-based analysis, it can continuously optimize startup strategies by reflecting real-time data and can be flexibly expanded without relying on specific models or algorithms. Through this, the founder can respond quickly to changes in the market environment and make more sophisticated decisions.

[0070] A control unit according to one embodiment of the present invention analyzes the real estate price increase rate, population growth rate, and new commercial facility development plans of a startup area to identify factors with a high probability of increasing sales after a startup in a specific area, derives a correlation by comparing it with the sales growth rate after a startup in a similar area in the past, and performs mention frequency and sentiment analysis for a specific menu by analyzing content such as Instagram, YouTube, and blogs using natural language processing (NLP) technology to evaluate the preference of potential consumers for a specific brand and menu by combining customer visit data and social media trend analysis, predicts consumption patterns reflecting real-time trend changes, and predicts the possibility of sales increase or decrease by learning past sales data of nearby areas when specific events, including local festivals, sports games, and large concerts, occur in order to dynamically adjust store operation strategies based on demand forecast results, recommends strategies such as deploying additional personnel, applying promotions, and focusing sales on specific menus during the event period, and learns patterns of high customer retention rates at a specific store by analyzing past customer review data, membership sign-up rates, and SNS sharing frequency to quantitatively predict customer loyalty, brand revisit rates, and word-of-mouth effects expected at a specific point in time after a startup, and evaluates the long-term brand growth potential in the area.

[0071] A control unit according to one embodiment of the present invention may be implemented by comprehensively analyzing the real estate price growth rate, population growth rate, and new commercial facility development plans of a startup area to identify factors with a high probability of increasing sales after a startup in a specific area. The real estate price growth rate may be derived by collecting commercial and residential real estate transaction data from the area over the past few years, analyzing average price fluctuation patterns, and predicting the potential for future increases. The population growth rate may be applied by analyzing the scale of incoming population by considering administrative data, population migration records, and construction plans for new apartments and residential complexes in the area, and by predicting changes in dining-out demand due to the increase in the resident population. The new commercial facility development plans may be analyzed by evaluating the potential for future commercial district formation in the planned startup area by considering data on new commercial facility construction permits, development plans for large shopping malls and office complexes, and plans for expanding public facilities in the area. By deriving correlations by comparing these factors with the sales growth rate after a startup in similar past areas, the expected growth potential after a startup in a specific area can be quantitatively evaluated.

[0072] This can be implemented by analyzing content data from platforms such as Instagram, YouTube, and blogs using Natural Language Processing (NLP) technology, combining customer visit data with social media trend analysis to evaluate the preferences of potential consumers for specific brands and menus. It involves measuring the frequency of mentions for specific brands or menus on these social media and online platforms and distinguishing between positive and negative reactions through keyword sentiment analysis. By performing hashtag and trend analysis for specific brands or menus, it is possible to predict when consumer interest will increase at specific times and, based on this, quantitatively evaluate brand preference within the market. To reflect real-time trend changes, it can be applied by detecting moments when specific brands or menus are mentioned rapidly on social media, and analyzing related search volume, online review growth rates, and changes in consumer reactions at those times to predict the growth potential of specific food service brands within the market.

[0073] To dynamically adjust store operational strategies based on demand forecasting results, this can be implemented by learning from historical sales data of nearby regions to predict potential sales fluctuations during specific events, such as local festivals, sporting events, or large-scale concerts. By considering the date and time of the specific event, the type of event, the estimated number of visitors, and the distance between the event venue and the startup location, the impact of similar past events on local dining sales can be analyzed. Based on this, it is possible to simulate expected sales changes during the event period and predict time slots requiring additional staffing, optimal timing for specific promotions, and the timing for intensive sales of popular menu items. Since sales growth patterns may vary by event, machine learning models utilizing historical event data can be applied to propose optimal operational strategies for each event type.

[0074] To quantitatively predict expected customer loyalty, brand return rates, and word-of-mouth effects at a specific point in time after launch, this can be implemented by analyzing historical customer review data, membership sign-up rates, and social media sharing frequency to learn patterns of high customer retention rates at specific stores. Customer review data can be evaluated based on the number of reviews, ratings, and sentiment analysis results provided by online review platforms, while membership sign-up rates can be analyzed by considering factors such as the enrollment rate in a specific brand's loyalty program, point usage frequency, and repeat visit rates. Social media sharing frequency can be applied by measuring the rate at which a specific brand or menu is shared by consumers and evaluating brand diffusion effects by analyzing hashtags and post reach. Based on this, it can be utilized to quantitatively assess the potential for forming a highly brand-loyal customer base in a specific region and to predict the long-term brand growth potential within that area.

[0075] The control unit of the present invention is not limited to a specific data collection method or analysis technique, and can be implemented by combining various data sources and analysis techniques to optimize the founder's startup strategy and operational plan. Furthermore, it can be designed to continuously learn operational data after the startup to flexibly respond to market changes, and can apply various forms of AI-based analysis techniques without being dependent on specific algorithms or models. Through this, the founder can establish a more precise startup strategy and adjust operational plans in real time according to changes in the external environment.

[0076] A control unit according to an embodiment of the present invention may be implemented to derive a candidate location for starting a business that satisfies the most favorable location conditions within a given area by linking and analyzing multiple data sources (satellite maps, mobile carrier location data, card usage history, delivery platform data, etc.), by applying spatial data analysis techniques to score the weighted average of customer density and movement paths, and visually present a location suitable for starting a business within a specific commercial district; to analyze the personal management style and investment tendencies of the business owner (aggressive investment, stable operation, minimum cost startup, etc.), by classifying the owner's survey response data, past startup cases, and preferred business operation methods using a machine learning-based decision tree algorithm to recommend an appropriate startup model; to provide a dynamic price optimization algorithm that calculates the optimal menu price within a specific area by reflecting real-time competitor price fluctuation data, by comparing average prices per menu collected from delivery apps and online ordering systems and performing price elasticity analysis to calculate the optimal point where customer response is maximized at a specific price range; and to optimize the store's internal layout, by performing space utilization simulations based on customer movement data and table turnover rate, and by comparing the average waiting time and customer satisfaction in a specific layout to propose a method to increase operational efficiency. there is.

[0077] A control unit according to one embodiment of the present invention may be implemented by applying various spatial data analysis techniques to derive a candidate location for a startup by linking and analyzing multiple data sources. Satellite map data may be utilized to analyze the topography, road network, and distribution of major buildings in the area, and mobile carrier location data may be applied to identify population density and real-time changes in floating population in a specific area. Card usage history may be utilized as an indicator to evaluate the intensity of consumption activity within the region and may be used to analyze the average transaction value, frequency of visits, and sales variability by time of day in a specific commercial district. Delivery platform data may be applied to analyze dining-out demand patterns in the area, including information such as delivery order frequency, popular menu items, and delivery areas.

[0078] The control unit can evaluate potential startup sites by converting collected data into weighted average scores using spatial analysis techniques. Customer density can be calculated by evaluating whether customer inflow is concentrated in a specific area during certain time periods based on floating population data, while movement paths can be implemented by quantitatively evaluating the likelihood of a visit within a specific commercial district through the analysis of major transportation networks and customer movement data. During the startup site selection process, the optimal location can be derived by comprehensively considering factors such as the layout of commercial facilities in a specific area, the density of competitors, and accessibility to public transportation; furthermore, the analysis results can be visualized and presented to the entrepreneur for intuitive understanding.

[0079] To analyze the personal management style and investment propensity of business owners, this can be implemented by collecting survey response data, past startup cases, and preferred business operation methods, and classifying startup types by applying a machine learning-based decision tree algorithm. The owners' response data may include preferences regarding business operations, risk tolerance, and the range of initial investment feasibility, while past startup case data can be utilized to recommend startup models exhibiting similar tendencies by learning from cases where businesses were founded with the same investment scale and operation methods. For instance, this can be applied to recommend a model with high initial costs but rapid profit recovery to an entrepreneur who prefers aggressive investment, and to recommend locations with high long-term growth potential to an entrepreneur who desires stable operations.

[0080] To calculate optimal menu prices by reflecting real-time competitor price fluctuation data, this can be implemented by comparing and analyzing average prices per menu item collected from delivery applications and online ordering systems. By monitoring real-time price changes of food service establishments offering the same menu in a specific region and performing price elasticity analysis, the optimal point at which customer response is maximized within a specific price range can be identified. Price elasticity analysis can be conducted by evaluating how customers adjust their consumption patterns in response to price changes, thereby supporting business owners in setting appropriate prices for specific menu items. Furthermore, by additionally considering factors such as the application of promotions and time-of-day discount strategies, a price optimization model capable of maximizing sales under specific conditions can be derived.

[0081] Optimization of the store's interior layout can be implemented by performing space utilization simulations based on customer movement data and table turnover rates. Customer movement data can be utilized to analyze the efficiency of spatial arrangement, including customer movement paths within the store, major waiting points, and order and payment locations. Table turnover data can be applied to evaluate average customer dwell times by specific time periods and to derive strategies to reduce customer waiting times in particular layouts. For instance, layout optimization can be achieved by reducing congestion through the separation of waiting and ordering areas, or by maximizing customer capacity through the application of seating arrangements with high turnover rates. Furthermore, the store design can be adjusted to maximize operational efficiency by comparing and evaluating the impact of specific layouts on customer satisfaction.

[0082] The control unit of the present invention is not limited to specific data analysis techniques or models, and can apply various analysis methods according to the founder's requirements and changes in the market environment. Furthermore, it can be expanded to provide data-based customized consulting to enable the founder to establish optimal startup strategies in a specific region, and can be designed to improve operational efficiency through continuous data analysis even after the startup. Through this, the founder can establish more precise location selection and pricing strategies, and derive optimal store operation plans to maximize customer inflow.

[0083] A control unit according to an embodiment of the present invention collects real-time operational data during the initial six months after opening to continuously analyze sales, customer count, revisit rate, and promotion effectiveness, and to recommend real-time operational improvement measures tailored to the store owner's operating style, applies a non-linear time series forecasting model to analyze the growth curve of each store using multiple variables, predicts the point of operational stabilization based on the bivariate or multivariate distribution of sales trends in the early stages of opening, and, to provide the store owner with an immediate response strategy when specific sales reduction factors are detected, including increased material costs, rising labor costs, and strengthened promotions by competitors, forms a group of stores with similar patterns by applying Principal Component Analysis (PCA) and cluster analysis (K-means), derives effective response measures within the group, and evaluates the risk of sales reduction for each store based on weights, simulates customer reactions to specific menus using an AI-based virtual customer analysis model, predicts sales probabilities for each menu based on probability distributions using Monte Carlo simulation techniques to optimize menu composition and promotion strategies by reflecting real-time feedback, and gradually corrects the expected profitability of the menu through Bayesian updates, and continuously analyzes operational data after opening To provide more sophisticated predictions of success potential to new entrepreneurs through learning, a multiple regression model and an LSTM-based deep learning model are combined to learn the growth patterns of new stores over time, and key operational variables of each store, including average transaction value, table turnover rate, and delivery share, are vectorized to construct a business prediction model for new entrepreneurs.

[0084] A control unit according to one embodiment of the present invention may be implemented by collecting real-time operational data during the initial six months after opening and continuously analyzing sales, the number of customers, the return visit rate, and the effectiveness of promotions. Real-time operational data may include POS systems, online ordering systems, delivery platforms, customer feedback data, employee work schedules, etc., and may be aggregated and analyzed by a central server. Sales analysis may be subdivided by day, week, and month, and may be performed by quantitatively evaluating the growth curve of each store, including sales growth rate, sales patterns by specific day or time of day, and seasonal variability.

[0085] To recommend real-time operational improvement plans tailored to the store owner's management style, a non-linear time-series forecasting model can be applied to analyze the growth curve of each store using multiple variables. By combining variables such as sales, customer count, average transaction value, and promotional effectiveness, initial startup performance can be evaluated, and growth patterns by store can be compared. Sales trends during the initial startup phase can be modeled based on bivariate or multivariate distributions and implemented by predicting the point at which sales at a specific location stabilize (the point of operational stabilization). Through this, the entrepreneur can predict in advance when store operations will be on track and apply necessary marketing strategies and cost-saving measures until that point.

[0086] When specific factors causing a decline in sales, such as increased material costs, rising labor costs, or intensified promotions by competitors, are detected, this system can be implemented by applying Principal Component Analysis (PCA) and K-means clustering to form groups of stores with similar patterns, thereby providing store owners with immediate response strategies. By clustering the operational data of each store to analyze sales decline patterns, effective response measures can be derived within groups of stores that have experienced similar factors. Based on this, the system can be applied to evaluate the risk of sales decline using weighted criteria and propose strategies that require priority action at specific stores. For example, if a competitor's discount promotion has an impact, response strategies may be provided by adjusting the store's pricing strategy or running additional promotions for specific menu items.

[0087] By utilizing an AI-based virtual customer analysis model, it can be implemented to simulate customer reactions to specific menu items and optimize menu composition and promotion strategies by reflecting real-time feedback. Monte Carlo simulation techniques can be employed to predict sales probabilities for each menu item based on probability distributions; if a specific menu item shows a high sales rate among a particular customer segment, customized marketing strategies targeting that segment can be established. Bayesian updates are utilized to reflect real-time sales data and adjust sales probabilities whenever new data is collected, thereby enabling the derivation of continuously optimized menu compositions and pricing strategies.

[0088] To provide new entrepreneurs with more sophisticated predictions of success probability by learning from continuous operational data after startup, this can be implemented by combining multiple regression models and LSTM-based deep learning models to learn the growth patterns of the startup store over time. Key operational variables of each store, including average transaction value, table turnover rate, and delivery share, can be vectorized, and a method can be applied to predict the likelihood of a new entrepreneur exhibiting similar sales patterns based on this. Through this, it is possible to predict the initial sales trends of a store to be operated by a specific entrepreneur and identify anticipated risk factors in advance, thereby enabling the establishment of an optimal startup strategy.

[0089] The control unit of the present invention is not limited to a specific data collection method or analysis model, and can be implemented by combining various data analysis techniques according to the founder's store operation method and changes in the market environment. It can continuously improve operational strategies by reflecting real-time data and can be designed to propose long-term sales growth strategies by learning operational patterns over a certain period after the startup. Through this, the founder can continuously optimize store operations by considering long-term growth potential, rather than simply optimizing decision-making during the initial startup phase.

[0090] A control unit according to an embodiment of the present invention analyzes the impact of climate and seasonal changes in a startup area on sales by linking weather data and past sales data to derive the correlation between environmental variables such as precipitation, temperature, humidity, and fine dust concentration and the effect on customer inflow and average transaction value, and based on this, predicts patterns of sales volume increase or decrease under specific climate conditions; analyzes GPS-based vehicle movement data and public transportation usage patterns using a machine learning-based regression model to predict customer accessibility by specific time slots and days of the week to optimize traffic flow and customer visit patterns in nearby areas; establishes data-based strategies for optimizing store operating hours, intensive promotions during commuting hours, and delivery operation efficiency; analyzes power and energy consumption patterns of a startup candidate site to predict expected power costs in a specific area; quantitatively calculates the daily energy consumption of a specific store by applying a power usage prediction model to optimize the introduction of high-efficiency kitchen equipment and heating and cooling systems; provides an energy saving guide for cost reduction by the entrepreneur; and, to quantitatively evaluate brand loyalty based on customer sentiment analysis, identifies customer facial expressions and voice tones through the analysis of in-store CCTV and voice data, and applies an artificial intelligence sentiment analysis model. It calculates the customer satisfaction index in real time and dynamically adjusts employee training strategies and customer response processes based on it.

[0091] A control unit according to one embodiment of the present invention may be implemented in a manner that analyzes the impact of specific environmental variables on customer inflow and average transaction value by linking weather data with past sales data to analyze the impact of climate and seasonal changes in a startup area on sales. Weather data includes precipitation, temperature, humidity, fine dust concentration, etc., and can be collected in real time to derive correlations with sales data. For example, if precipitation increases, there is a higher tendency to prefer indoor stores and a possibility that the proportion of delivery orders will increase, while if fine dust concentration is high, the utilization rate of outdoor terrace seating may decrease. Based on this, patterns of sales volume increase or decrease under specific climate conditions can be predicted, and support can be provided to the startup owner to optimize menu composition, inventory management, and delivery promotion strategies according to weather changes.

[0092] To optimize traffic flow and customer visit patterns in nearby areas, this can be implemented by analyzing GPS-based vehicle movement data and public transportation usage patterns using machine learning-based regression models to predict customer accessibility by specific time of day and day of the week. GPS data can be used to analyze traffic congestion levels, parking availability, and major road usage patterns in specific areas, while public transportation data can be applied to quantitatively evaluate the potential for customer influx by utilizing bus and subway boarding and alighting records. For instance, pedestrian traffic in downtown office districts is likely to increase during lunchtime, and traffic volume rises during rush hours, which may alter delivery service operations. Based on these analysis results, strategies can be formulated to optimize store operating hours, apply specific promotions tailored to rush hours, or maximize the efficiency of delivery services.

[0093] This can be implemented by analyzing the power and energy consumption patterns of potential business locations to predict expected electricity costs in specific areas and optimize the adoption of high-efficiency kitchen equipment and HVAC systems. By applying a power usage prediction model, the daily energy consumption of a specific store can be quantitatively calculated, and optimization strategies can be proposed by considering kitchen appliance usage patterns, power consumption of refrigerators and freezers, and operating hours of lighting and HVAC systems. For instance, since kitchen appliance utilization increases during peak lunch hours, energy saving measures can be applied by analyzing power consumption by time period to predict maximum demand and prevent overloading. Furthermore, it can support the reduction of operating costs for entrepreneurs by including strategies such as the potential for utilizing renewable energy, the adoption of energy-saving cooking appliances, and cost reduction through power demand response systems.

[0094] To quantitatively evaluate brand loyalty based on customer sentiment analysis, this can be implemented by analyzing in-store CCTV and voice data. CCTV data can be utilized to detect customer facial expressions and behavioral patterns, and satisfaction can be assessed by analyzing changes in facial expressions after ordering specific menu items. Additionally, AI-based image analysis models can be applied to identify positive and negative reactions in real time. Furthermore, voice data can be used to analyze customer interactions during the ordering process to evaluate tone and speech speed, and to calculate a customer satisfaction index by applying sentiment analysis techniques. Based on these findings, employee training strategies can be adjusted, and service quality can be improved by incorporating immediate feedback when customer reactions to specific time slots or menu items are unfavorable.

[0095] The control unit of the present invention is not limited to specific weather data analysis techniques, traffic flow prediction models, power consumption pattern analysis methods, or sentiment analysis techniques, and can be implemented by combining various data analysis methods according to the founder's store operation methods and changes in the market environment. Furthermore, it can be expanded to continuously reflect real-time data to enable the founder to optimize operational strategies in a specific region and to flexibly respond to new market changes. Through this, the founder can analyze weather changes, traffic flow, energy costs, and customer satisfaction in real time, optimize store operations, and be supported in maximizing profitability in the long term.

[0096] A control unit according to an embodiment of the present invention analyzes social media posts, online reviews, and local event data of a startup area using natural language processing (NLP)-based sentiment analysis techniques to recommend customized menus and store concepts by analyzing cultural elements and consumer lifestyle patterns of the startup area; identifies brand keywords and food culture trends preferred by local consumers to optimize the store's interior, menu composition, and promotion strategies; analyzes the founder's past financial records and expenditure patterns using a machine learning-based credit evaluation model to propose customized financial support and investment strategies by analyzing the founder's personal credit and financial status; compares loan products, investment options, and government support policies available to the founder to provide optimal initial funding plans; analyzes the store owner's decision-making patterns by combining POS data, employee attendance and work patterns, and customer review data to provide customized management coaching services by evaluating the store owner's management style and employee management methods in real time after startup; and constructs an automated management feedback system to enhance operational efficiency based on this analysis; and continuously monitors data on changes in local commercial areas, population inflow, and economic growth to optimize the store's long-term branding strategy after startup, and utilizes an AI-based long-term prediction model to predict sales changes expected after one, three, and five years. It simulates and recommends data-driven strategies for brand renewal, store expansion, and business diversification at specific points in time.

[0097] A control unit according to one embodiment of the present invention can be implemented by analyzing social media posts, online reviews, and local event data in a specific region using natural language processing (NLP)-based sentiment analysis techniques to recommend customized menus and store concepts by analyzing cultural elements and consumer lifestyle patterns in the startup region. Social media data can be utilized to analyze food types, restaurant styles, and popular brands frequently mentioned in a specific region, and can evaluate whether specific menus or cooking methods are preferred by local consumers through hashtag and keyword frequency analysis. Online review data can be applied to evaluate positive or negative customer reviews using a sentiment analysis model and to analyze how high the loyalty of a specific store type is within the region. Local event data can be utilized to identify patterns of increased food consumption during specific periods by analyzing local festivals, exhibitions, sports events, etc. Based on these analysis results, the store's interior style, menu composition, and promotion strategies can be optimized; for example, operational strategies can be adjusted to introduce menus that gain attention on social media in regions with a large young consumer base, or to emphasize classic menus in regions with a large traditional consumer base.

[0098] To propose customized financial support and investment strategies by analyzing a founder's personal credit and financial status, a method utilizing machine learning-based credit scoring models to analyze their past financial records and spending patterns can be applied. The system operates by analyzing the founder's credit score, loan repayment history, and monthly spending patterns to calculate the initial investment amount the founder can afford, and then providing the optimal initial financing plan by comparing available loan products, investment options, and government support policies. For instance, in regions where government startup support policies are active, customized financial strategies can be proposed to enable founders to utilize low-interest loans, or support can be provided to help founders select phased investment methods to minimize risk. Furthermore, funding strategies can be adjusted based on the founder's business operational style; for example, the strategy can be set to expand initial marketing costs if an aggressive investment strategy is preferred, or to prioritize reducing operating expenses if stable growth is desired.

[0099] To provide customized management coaching services by evaluating the store owner's management style and employee management methods in real-time since opening, this system can be implemented by analyzing the owner's decision-making patterns through the combination of POS data, employee attendance and work patterns, and customer review data. Based on POS data, the frequency of price adjustments, discount policy application methods, and new menu launch cycles can be analyzed to derive specific patterns and recommend more effective management strategies. Employee attendance data can be utilized to evaluate whether the owner's workforce management is optimized by analyzing individual employee arrival times, shift patterns, and sales contribution relative to working hours. Customer review data can be applied to evaluate service-related feedback consistently appearing at a specific store and to analyze whether the owner is taking appropriate measures to improve customer satisfaction. Based on this, the system can operate by quantitatively evaluating the owner's management style and providing automated management feedback regarding workforce management, inventory control, and sales growth strategies.

[0100] To optimize the long-term branding strategy of a store after opening, a method can be applied that involves continuously monitoring data on changes in local commercial areas, population influx, and economic growth, and utilizing AI-based long-term prediction models to simulate expected sales changes after one, three, and five years. Data on changes in commercial areas can be used to predict the potential for sales growth in a specific region by analyzing factors such as the construction of new commercial facilities, the expansion of transportation networks, and the influx of large franchise brands. Population influx data can be analyzed to include the number of residents moving into the area, population changes by age group, and plans for residential facility development, while economic growth data can be analyzed based on the growth rate of local businesses, changes in consumer spending, and the growth rate of average disposable income. By learning from this data, data-driven recommendations for brand renewal, store expansion, and business diversification strategies can be made at specific points in time. For instance, if competition intensifies in a particular region, this approach can be applied to suggest strategies such as changing brand positioning or expanding stores to adjacent areas with high growth potential.

[0101] The control unit of the present invention is not limited to specific cultural element analysis methods, financial support strategies, management feedback techniques, or long-term forecasting models, and can be implemented by combining various analysis methods according to the founder's operational strategies and changes in the market environment. It can provide various data-driven customized strategies to enable the founder to strengthen brand identity and maintain continuous profitability, and can continuously improve operational plans by reflecting real-time data. Through this, the founder can be supported in responding quickly to market changes, establishing long-term business growth plans, and executing more sophisticated management strategies. Explanation of the symbols

[0102] 100: User terminal 110: User Input Section 120: Display section 200: Server 210: Communications Department 220: Storage section 230: Control unit

Claims

Claim 1 An AI-based food service franchise startup consulting and profitability analysis system comprises: a user terminal having a user input unit for user input; and a display unit for displaying at least one screen of an application or program for food service franchise startup consulting and profitability analysis already installed; and a server connected to the user terminal via a wired or wireless network, wherein the server comprises a communication unit; a storage unit; and a control unit; comprising, wherein the control unit collects communication network traffic, GPS-based movement patterns, and public transportation usage data of the area to identify population density, floating population, and commercial area type of the planned business location, and identifies changes in customer inflow by specific time periods through time-series analysis thereof; quantifies customer preference for each brand by linking delivery app, social media, and search portal data to analyze the number of competitors, sales status, menu prices, customer reviews, and online ratings, and compares and analyzes average order volume and price competitiveness for specific menus; identifies consumption patterns by age, gender, and income level by comprehensively evaluating local card payment data, membership sign-up information, and online search trends to segment the main consumer base within the commercial area, and forms detailed customer groups by applying an AI-based clustering model; calculates the probability of customer inflow considering major commercial facilities, public transportation usage patterns, and distance from residential areas by analyzing location-based service usage data of the area to predict customer movement paths and visit frequency; and wherein the control unit analyzes public databases and local real estate to segment and analyze regional operating costs into rent, labor costs, material costs, maintenance costs, utilities, and marketing costs. By utilizing transaction information and labor market statistics, the average rent and labor cost growth rates of the region are extracted, and future cost volatility is identified using a time-series forecasting model; furthermore, to calculate the expected startup costs in a specific region, data from past startup cases is learned to derive the average investment costs incurred in similar commercial areas and store sizes.It establishes a highly reliable range of startup costs by excluding abnormally high or low cases through the application of machine learning-based anomaly detection algorithms; to calculate the payback period relative to investment based on the startup time and expected operating period, it derives the break-even point by comparing the average monthly expected sales and operating costs in a specific region, and quantitatively evaluates the possibility of investment recovery after startup by backtracking the expected timing of profit generation through analysis; to optimize the revenue model, it calculates the required number of customers and average transaction value per sales target by considering factors affecting operating cost fluctuations, and simulates fluctuations in profitability due to seasonal factors, promotional effects, and competitor price changes through multiple regression analysis reflecting real-time operational data; and the control unit analyzes the real estate price growth rate, population growth rate, and development plans for new commercial facilities in the startup region to identify factors likely to increase sales after startup in a specific region, and derives correlations by comparing them with sales growth rates after startup in similar regions in the past; and to evaluate the preferences of potential consumers for specific brands and menus by combining customer visit data and social media trend analysis, it performs mention frequency and sentiment analysis for specific menus by analyzing content on Instagram, YouTube, and blogs using Natural Language Processing (NLP) technology, and analyzes real-time trend changes To predict reflected consumption patterns and dynamically adjust store operation strategies based on demand forecast results, it learns past sales data from nearby areas to predict potential increases or decreases in sales when specific events, including local festivals, sports games, and large-scale concerts, occur, and recommends strategies such as deploying additional personnel, applying promotions, and focusing sales on specific menus during the event period; to quantitatively predict expected customer loyalty, brand revisit rates, and word-of-mouth effects at a specific point in time after opening, it analyzes past customer review data, membership sign-up rates, and SNS sharing frequency to learn patterns of high customer retention rates at specific stores and evaluates the long-term brand growth potential in the relevant region, and the control unit includes a satellite map,To derive candidate startup sites that meet the most favorable location conditions within a given area by linking and analyzing multiple data sources including mobile carrier location data, credit card usage history, and delivery platform data, spatial data analysis techniques are applied to score the weighted average of customer density and movement paths, and suitable locations for starting a business within a specific commercial district are visually presented; to analyze the business owner's personal management style and investment propensity, the owner's survey response data, past startup cases, and preferred business operation methods are classified using a machine learning-based decision tree algorithm to recommend corresponding startup models; to provide a dynamic price optimization algorithm that calculates optimal menu prices within a specific area by reflecting real-time competitor price fluctuation data, average prices by menu collected from delivery apps and online ordering systems are compared, and price elasticity analysis is performed to calculate the optimal point where customer response is maximized at a specific price range; and to optimize the store interior layout, space utilization simulations are performed based on customer movement data and table turnover rates, and measures to improve operational efficiency are proposed by comparing average waiting times and customer satisfaction in specific layouts, and the control unit collects real-time operational data during the first six months after startup to analyze sales, number of customers, return visit rates, and promotion effects To continuously analyze and recommend real-time operational improvement plans tailored to the store owner's operating style, a non-linear time-series forecasting model is applied to analyze the growth curve of each store using multiple variables; the point of operational stabilization is predicted based on the bivariate or multivariate distribution of sales trends during the initial startup phase; and to provide the store owner with immediate response strategies when specific sales-reducing factors are detected, including increased material costs, rising labor costs, or intensified promotions by competitors, Principal Component Analysis (PCA) and K-means clustering are applied to form groups of stores with similar patterns.Effective response measures are derived within the group to evaluate the risk of sales decline for each store based on weights; an AI-based virtual customer analysis model is utilized to simulate customer reactions to specific menus; Monte Carlo simulation techniques are employed to predict sales probabilities for each menu based on probability distributions to optimize menu composition and promotion strategies by reflecting real-time feedback, and the expected profitability of the menu is progressively corrected through Bayesian updates; to provide new entrepreneurs with more sophisticated predictions of success probability by learning continuous operational data after establishment, multiple regression models and LSTM-based deep learning models are combined to learn the growth patterns of the established store over time; and key operational variables of each store, including average transaction value, table turnover rate, and delivery proportion, are vectorized to construct a business prediction model for new entrepreneurs; the control unit links weather data with historical sales data to derive the correlation between environmental variables, including precipitation, temperature, humidity, and fine dust concentration, and their impact on customer inflow and average transaction value, and based on this, predicts patterns of sales volume increase or decrease under specific climate conditions; the aforementioned correlation indicates that when precipitation increases, the tendency to prefer indoor stores rises and the delivery order ratio increases, and when fine dust concentration is high It is derived based on the decrease in the utilization rate of outdoor terrace seating, and predicts customer accessibility by specific time periods and days of the week by analyzing GPS-based vehicle movement data and public transportation usage data using a machine learning-based regression model; the aforementioned customer accessibility prediction is derived based on public transportation data that quantitatively evaluates the potential for customer inflow by utilizing GPS data to analyze traffic congestion levels, parking availability zones, and major road usage patterns in specific areas, as well as bus and subway boarding and alighting records; through this, strategies for optimizing store operating hours, concentrated promotions during commuting hours, and efficiency improvements in delivery operations are calculated, and the expected electricity costs of a specific area are predicted by analyzing the power and energy consumption patterns of potential startup locations.A system for providing startup location analysis and operational strategies characterized by calculating the daily energy consumption of a specific store by applying a power usage prediction model, determining whether to introduce high-efficiency kitchen equipment and heating / cooling systems based on this, analyzing CCTV video and voice data within the store to identify customers' facial expressions, behavioral patterns, and voice tones, calculating a customer satisfaction index by applying an artificial intelligence sentiment analysis model, and dynamically adjusting employee training strategies and customer service processes based on this. Claim 2 delete Claim 3 delete Claim 4 delete Claim 5 delete Claim 6 delete

Images