How to improve the efficiency of renovation construction estimates

Abstract

Through a renovation support server that utilizes generative AI, we provide a method for improving the efficiency of renovation work estimates, which solves the issues that real estate businesses and others face in their respective business processes, improves productivity across the business, and creates new business opportunities. [Solution] A real estate business owner sends a floor plan of an existing property they are considering purchasing to a renovation support server from their terminal. The renovation support server analyzes structural information, including room dimensions and wall thickness, from the floor plan, and uses a generation AI to select a renovation plan based on the structural information, taking into account furniture placement and daily activity patterns. Based on the plan, the server automatically calculates a construction estimate from the necessary building materials and labor costs. The selected renovation plan and the renovation construction estimate calculated based on the plan are then received on the real estate business owner's terminal.

Description

[Technical Field]

[0001] The present invention relates to a method for improving the efficiency of renovation construction estimate work, and more particularly to a business method for real estate businesses, real estate brokerage businesses, and construction businesses to improve the efficiency of renovation construction estimate work using a computer system equipped with generative AI and solve their respective business challenges. [Background technology]

[0002] In recent years, with the revitalization of the used housing market, businesses that renovate existing real estate properties to increase their value have become widespread.Such businesses involve multiple businesses, including real estate businesses that purchase properties, renovate them, and resell them (purchasing and reselling businesses), real estate brokerage businesses that introduce properties to customers who wish to renovate, and construction businesses that carry out the actual construction.

[0003] Traditionally, when real estate businesses considered purchasing a used property, accurately understanding the construction costs required for renovation was essential to assessing the business's profitability. To do this, the real estate business would first ask a construction company to create a renovation plan and provide an estimate. The construction company would then conduct a site survey and take measurements, create a plan and a rough estimate, and submit them to the real estate business. The real estate business would then verify the contents and request further adjustments to the price and plan, resulting in multiple exchanges. In reality, this entire process typically took anywhere from a few days to two weeks.

[0004] Furthermore, in the case of real estate agents, many consumers consider second-hand properties, which are more affordable than new properties, but if they do purchase, they want the room to be in good condition. However, the number of renovated properties available on the market is limited. Therefore, even if agents try to propose post-purchase renovations for unrenovated properties, they cannot calculate the feasibility or cost of changing the floor plan on the spot. Furthermore, formally requesting an estimate from an external construction company for a project where the outcome is uncertain is not realistic in terms of time and cost.

[0005] As a result of this revitalization of the used housing market, various systems related to the renovation of real estate properties have been proposed. For example, Patent Document 1 discloses a system in which a user interactively determines a renovation plan and the corresponding price by selecting from a menu on an information terminal the size of the room, the layout of plumbing fixtures, and interior materials for the floor, walls, and ceiling.

[0006] Patent document 2 also discloses a system for inputting information such as the purchase price of a home, the renovation budget, the areas to be renovated, and estimates obtained from contractors, item by item, and for centrally managing and displaying information on the entire renovation plan. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2020-061008 [Patent Document 2] Utility Model Registration No. 3243825 Summary of the Invention [Problem to be solved by the invention]

[0008] However, the technology described in Patent Document 1 is a method of assembling a plan from a set of pre-defined options, and therefore cannot solve the problem required in on-site work, which is to directly read the wide variety of existing floor plans in various formats that real estate businesses and brokerage businesses obtain on a daily basis, and instantly calculate the optimal plan and estimate for the property.

[0009] Furthermore, the technology described in Patent Document 2 is based on the premise that the estimated cost of renovation work must be manually entered into the system, which means that the calculation of the estimate itself takes several days to several weeks, and incurs significant communication costs between businesses, which is a fundamental bottleneck in renovation work.

[0010] The present invention was made taking these circumstances into consideration, and aims to provide a method for improving the efficiency of renovation work estimates, which can solve the issues related to renovation work estimates that real estate businesses, real estate agents, and construction businesses face in their respective business processes via a renovation support server that utilizes generative AI, thereby improving the productivity of the entire business and creating new business opportunities. [Means for solving the problem]

[0011] In order to solve the above-mentioned problems, the present invention provides a method for improving the efficiency of renovation work estimates, which is executed by a real estate business using a computer, and which is executed by a renovation support server that analyzes structural information including room dimensions and wall thickness from floor plan data, selects a renovation plan based on the structural information using a generation AI that takes into account furniture placement and daily activity lines, and automatically calculates a construction estimate from the necessary building materials and labor costs based on the renovation plan, and includes the steps of: transmitting floor plan data of an existing real estate property that the real estate business is considering purchasing from a terminal of the real estate business to the renovation support server; and receiving the selected renovation plan and the renovation work estimate calculated based on the renovation plan from the renovation support server at the terminal of the real estate business. a step of calculating an expected selling price based on the received construction estimate, the planned purchase price of the existing real estate property, and business profit; a step of evaluating whether the calculated expected selling price is reasonable in the market and determining whether to purchase the existing real estate property based on the evaluation; The present invention is characterized by comprising:

[0012] In order to solve the above-mentioned problems, the present invention provides a method for improving the efficiency of renovation work estimates, which is executed by a real estate agent using a computer, and which is executed by a renovation support server that analyzes structural information including room dimensions and wall thickness from floor plan data, selects a renovation plan based on the structural information using a generation AI that takes into account furniture placement and daily activity lines, and automatically calculates a construction estimate from the necessary building materials and labor costs based on the renovation plan, and includes the steps of: transmitting floor plan data of an existing real estate property that is not yet renovated and is to be introduced to a customer from a terminal of the real estate agent to the renovation support server; and receiving the selected renovation plan and the estimated cost of the renovation work calculated based on the renovation plan from the renovation support server at the terminal of the real estate agent. a step of calculating a total amount by adding up the received estimate amount and the purchase price of the existing real estate property in an unrenovated state, and presenting the total amount to the customer; a step of further receiving a CG perspective drawing corresponding to the renovation plan from the renovation support server, and presenting the CG perspective drawing together with the total amount to the customer; and a step of receiving matching information regarding candidate construction companies that can carry out the renovation work from the renovation support server. The present invention is characterized by comprising:

[0013] Furthermore, in order to solve the above-mentioned problems, the present invention provides a method for improving the efficiency of renovation work estimates, which is executed by a construction company using a computer, and which is executed by a renovation support server that analyzes structural information including room dimensions and wall thickness from floor plan data, selects a renovation plan based on the structural information using a generation AI that takes into account furniture placement and daily activity lines, and automatically calculates a construction estimate from the necessary building materials and labor costs based on the renovation plan, and includes the steps of: transmitting floor plan data of an existing real estate property to be renovated, which is provided by a client, from a terminal of the construction company to the renovation support server; and receiving, at the terminal of the construction company from the renovation support server, renovation work estimate data including the selected renovation plan and the estimated amount of the renovation work calculated based on the renovation plan. receiving matching information from the renovation support server regarding candidates for construction companies that can carry out the construction work included in the renovation work; The present invention is characterized by comprising: [Effects of the Invention]

[0014] According to the present invention, through a renovation support server utilizing generative AI, it is possible to solve the issues related to estimating renovation work that real estate businesses, real estate brokerage businesses, and construction businesses face in their respective business processes, thereby improving productivity across the business and creating new business opportunities. [Brief explanation of the drawings]

[0015] [Figure 1] 10 is a flowchart illustrating a processing flow of a real estate business operator according to an embodiment of the present invention. [Figure 2] 10 is a flowchart illustrating a processing flow of a real estate brokerage business according to an embodiment of the present invention. [Figure 3] 10 is a flowchart for explaining a processing flow of a construction company according to an embodiment of the present invention. [Figure 4] 1 is a block diagram showing the configuration of a renovation support system according to one embodiment of the present invention. [Figure 5] FIG. 2 is a block diagram showing a renovation support server according to one embodiment of the present invention. [Figure 6] 10 is a flowchart showing the overall processing procedure of a renovation support server according to an embodiment of the present invention. [Figure 7] 10 is a flowchart for explaining details of the analysis process (step S200) by the image analysis unit of the renovation support server according to one embodiment of the present invention. [Figure 8] 10 is a flowchart for explaining details of the renovation plan generation process (step S300) by the plan generation unit of the renovation support server according to one embodiment of the present invention. [Figure 9] 10 is a flowchart for explaining details of a construction estimate generation process (step S400) performed by an estimating unit of a renovation support server according to an embodiment of the present invention. [Figure 10] 10 is a flowchart for explaining details of the matching process (step S500) performed by a matching unit of the renovation support server according to one embodiment of the present invention. [Figure 11] 10 is a flowchart for explaining details of the CG perspective generation process (step S600) by the CG generation unit of the renovation support server according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In all the drawings, like components are designated by like reference numerals, and the description thereof will be omitted as appropriate. In the following description, each component of each device or system represents a functional block, not a hardware configuration, unless otherwise specified. Each component of each device or system is realized by any combination of hardware and software, centered around the CPU of any computer, memory, a program loaded into the memory, a storage medium such as a hard disk that stores the program, and a network connection interface. There are many variations in the realization methods and devices.

[0017] The method for improving the efficiency of estimate work for renovation work according to an embodiment of the present invention is executed by a system that is mainly composed of a renovation support server (hereinafter, sometimes simply referred to as a "server") connected via a network and a user terminal operated by a business operator. The business operators that operate the user terminal include real estate businesses, real estate brokerage businesses, and construction businesses.

[0018] [Renovation support server functions] In this embodiment, the server that receives floor plan data of an existing real estate property transmitted from a user terminal automatically and immediately executes the following processes as its core function. First, the server analyzes the input floor plan image and extracts structural information necessary for the renovation design, such as room dimensions and wall location and thickness. Next, based on this structural information and, in some cases, user-specified conditions (such as family composition or desired style), the server uses generative AI to generate multiple optimal renovation plans that take into account furniture placement and daily living patterns, and then selects the plan with the highest rating. Finally, the server calculates the construction material and labor costs required to realize the selected renovation plan based on a pre-stored database, etc., and calculates a construction estimate. This series of processes dramatically streamlines the process of creating plans and calculating estimates, which previously required a lot of time from experts.

[0019] Therefore, the business methods described in the following embodiments are realized by the renovation support system 1 described below in [4. Configuration of the Renovation Support System 1], and in particular by the renovation support server (renovation support device) 100 that forms the core of the system. By using this renovation support server 100, this embodiment provides a new business method for solving issues faced in the business processes of different businesses involved in renovation projects, namely real estate businesses, real estate brokerage businesses, and construction businesses, and for improving business efficiency. Below, embodiments focusing on each business are described in detail with reference to drawings (flowcharts).

[0020] [1. Real estate business (purchase and resale) model] The real estate business model is a mode in which the method of the present invention is used by real estate businesses that purchase used properties, renovate them, and resell them. Conventionally, in this business model, before deciding to purchase a property, it was necessary to accurately understand the renovation costs to determine the profitability of the business. However, obtaining an estimate required requesting a construction company and then multiple adjustments to the plan and price, which usually took several days to two weeks. This process, which required time and human costs, was a major obstacle to quick business decision-making.

[0021] FIG. 1 is a flowchart illustrating the processing flow of a real estate business according to one embodiment of the present invention. As shown in FIG. 1, the real estate business first transmits floor plan data of a property they are considering purchasing from a user terminal to a renovation support server (step S101). The renovation support server uses the floor plan data as input information, analyzes the structural information internally, selects an optimal renovation plan using a generation AI, and performs cost estimation processing based on the plan. The user terminal then receives the renovation plan and construction estimate from the server in a short time (from nearly real time to a few seconds) (step S102). This series of steps forms the core of the present invention, dramatically shortening the estimate acquisition process, which previously required several days or more, and improving business efficiency.

[0022] Next, the real estate business operator can use the construction estimate efficiently obtained in step S102 to further develop a business plan. That is, the user terminal combines the received construction estimate with the business operator's "planned property purchase price" and "business profit" to calculate the "estimated sales price" after renovation (step S103). This step is an important process for concretely quantifying the business's exit strategy.

[0023] The real estate business then evaluates the appropriateness of the estimated selling price calculated in step S103 by comparing it with the surrounding market price, sales examples, etc. (step S104). If this evaluation determines that the estimated selling price is sufficiently competitive in the market (YES in step S104), the real estate business ultimately purchases the property (step S105). On the other hand, if the real estate business determines that the estimated selling price is too high compared to the market price (NO in step S104), for example, the business makes a decision not to purchase the property with the renovation plan because it is not profitable (step S106). In this way, the entire process of calculating the estimated selling price based on data, evaluating its appropriateness, and finally making the purchase decision is the core part of business decisions in this business model.

[0024] [2. Real estate brokerage business model] The real estate brokerage model is a mode in which the method of the present invention is used by real estate brokerages that mediate the sale and purchase of real estate. Traditionally, many consumers desire beautiful, renovated used properties, but the supply of such properties on the market has been extremely limited. Therefore, even if real estate brokerages tried to propose post-purchase renovations for the many unrenovated properties on the market, they were unable to calculate the feasibility or cost of the plans on the spot, and it was unrealistic to spend time and money requesting formal estimates from outside for deals where the outcome was uncertain. This supply-demand gap resulted in significant missed opportunities for real estate brokerages.

[0025] FIG. 2 is a flowchart for explaining the flow of processing by a real estate brokerage business according to one embodiment of the present invention. As shown in Figure 2, the basic business efficiency improvement method for the real estate brokerage model begins with the real estate broker sending floor plan data of an unrenovated property to be introduced to a customer from the user's terminal to the renovation support server (step S111). Next, the user's terminal receives from the server a renovation plan and an estimate for the renovation work that is automatically and instantly calculated based on the plan (step S112). This series of steps forms the basis for enabling a new business model of "renovation proposals" for any unrenovated property, something that was previously impossible.

[0026] Next, the real estate brokerage business adds up the construction estimate received in step S112 and the purchase price of the introduced property to calculate the "total" of expenses to be borne by the customer (step S113). The business then presents this calculated "total" to the customer (step S114). This process allows the customer to accurately grasp the overall picture of the financial plan that combines the property purchase and renovation, making it easier for them to make a purchase decision.

[0027] Furthermore, in this embodiment, the real estate agent can make proposals with higher added value. In step S112, the real estate agent receives from the server, in addition to the estimated price, a "CG perspective drawing" that is a rendering of the completed renovation plan, and "matching information on potential construction companies" that can carry out the work. Then, in step S114, this information is presented to the customer along with the calculated "total price." The step of presenting "CG perspective drawings" allows customers to intuitively imagine the space after renovation, dramatically increasing the appeal of the proposal. In addition, the step of presenting "matching information" allows real estate agents to go beyond simply introducing properties and provide one-stop proposals that support the realization of the renovation.

[0028] [3. Construction business model] The construction company model is a mode in which a construction company that undertakes renovation work uses the method of the present invention. Conventionally, construction companies have had to manually select materials and work items based on floor plans and calculate costs every time they receive a quote request from a customer (a client or another business). This work not only requires specialized knowledge but also takes a lot of time, and the effort placed a heavy burden on the business, especially for projects that did not result in an order.

[0029] FIG. 3 is a flowchart for explaining the flow of processing by a construction company according to one embodiment of the present invention. As shown in Figure 3, the basic business efficiency improvement method of the construction company model begins with the construction company sending property floor plan data provided by the client from the user terminal to the renovation support server (step S121). Next, the user terminal automatically and instantly receives construction estimate data, including the renovation plan and construction estimate, from the renovation support server (step S122). This series of steps is an innovative business efficiency improvement method that replaces the time-consuming cost estimation work that construction companies previously performed internally with the transmission and reception of information with the server.

[0030] Next, the construction company outputs the construction estimate data received in step S122 as information to be submitted to the client (step S123). This enables the company to submit a quick and accurate estimate in response to an inquiry, leading to improved customer satisfaction and increased opportunities to receive orders.

[0031] It is preferable that the business efficiency improvement method for each business model described above be provided to businesses in the form of SaaS (Software as a Service), for example, based on a subscription contract for a fixed monthly fee, etc. This allows real estate businesses, real estate brokerage businesses, and construction businesses to continuously use this method with the latest functions without requiring a large initial investment.

[0032] Furthermore, it is preferable that the existing real estate property targeted in the business efficiency improvement methods for each of the above business models is a single dwelling unit in an apartment building. This is because by specializing in apartment buildings, which have the most transactions in the renovation market and where standardization of structure and specifications can be expected to a certain extent, the accuracy of plan generation and estimate calculation by the generation AI can be maximized.

[0033] [4. Configuration of Renovation Support System 1] Figure 4 is a block diagram showing the configuration of a renovation support system 1 according to one embodiment of the present invention. The renovation support system 1 shown in Figure 4 is an information processing system in which a renovation support server (renovation support device) 100 uses generated AI 400 to analyze floor plan information of an existing dwelling unit to be renovated based on input operations by a user from a user terminal 200, and automatically and comprehensively supports a series of renovation tasks, such as design proposals, estimate creation, construction company recommendation, and output of a rendering of the completed unit. The "dwelling unit to be renovated" here refers to the dwelling space that is treated as the target of design and construction in the renovation support system 1, and is a concept that includes the interior space of a single dwelling unit in an apartment building or a detached house.

[0034] As shown in Figure 4, in the renovation support system 1 of this embodiment, a renovation support server 100 analyzes floor plan information of existing dwelling units and provides a series of renovation support services such as design proposals, creating estimates, recommending construction companies, and outputting renderings of completed units, and is configured so that a user terminal 200, a group of databases (DBs) 300, and a generation AI 400 can be connected to each other via a network N such as the Internet.

[0035] In this embodiment, in the renovation support system 1, the user terminals 200 connected to the renovation support server (renovation support device) 100 via the network N are terminals operated by real estate agents, real estate brokerage agents, and construction companies. The details of the renovation support system 1 and its configuration and operation will be described later.

[0036] [4.1. Configuration and Functions of the Renovation Support Server 100] The renovation support server 100 is a device that provides services such as providing renovation plans to users of the renovation support system 1, for example in SaaS format, and is a computer or server that generates renovation plans, etc. using generation AI 400 based on a prompt, which is text entered by the user on the user terminal 200.

[0037] The renovation support server 100 receives floor plan images as input from the user terminal 200 and incorporates multiple processing means for consistently executing processes such as extracting structural information, generating renovation proposals, creating estimates, matching potential construction companies, and outputting CG perspective drawings. These processes are shared among various modules (various means) implemented within the system, and are configured to be processed automatically while coordinating with each other. Here, the renovation support server 100 mainly includes the following functional blocks.

[0038] [4.1.1. Configuration and Function of Input / Output Unit 101] The input / output unit 101 has an interface function for transmitting and receiving information between the user terminal 200 and the renovation support server 100 . Specifically, the input / output unit 101 receives floor plan image data uploaded from the user terminal 200 and renovation conditions specified by the user (desired floor plan, family composition, preferred equipment, etc.), and supplies the data to internal processing modules such as the image analysis unit 102 and the plan generation unit 103.

[0039] In addition, the input / output unit 101 transmits structural information, floor plan proposals, construction estimates, matching candidate lists, CG perspective drawings of completed projects, etc., generated as a result of internal processing to the user terminal 200, and provides them in a format that can be displayed or downloaded through a specified user interface screen. Here, the input / output unit 101 may be configured to present the status of ongoing processing and completion notifications to the user in real time depending on the overall system usage and processing status, and may include control logic that enables interactive dialogue with the system. In this way, the input / output unit 101 is a component that serves as the starting point and the ending point of the entire processing flow as a contact point with the user, and also functions as a hub for data exchange in cooperation with other internal processing means.

[0040] [4.1.2. Configuration and Function of Image Analysis Unit 102] The image analysis unit 102 is a processing module that acquires as input from the user terminal 200, for example, a floor plan image corresponding to a single unit in an apartment building, and generates "structural information" for quantitatively grasping the spatial configuration of the unit.

[0041] The image analysis unit 102 first automatically detects elements such as walls, rooms, doors, windows, and storage areas contained in the floor plan image using image processing and image recognition algorithms, and models the spatial configuration by analyzing the geometric shapes, positional relationships, and connection relationships of these elements. Next, the image analysis unit 102 calculates dimensional attributes such as the area of ​​each room, the length of walls, the placement of openings, and the width of corridors based on the detected components, and converts these into numerical data as structural information. The structural information obtained here is used by the plan generation unit 103 to generate floor plan proposals and evaluate furniture placement.

[0042] Furthermore, the image analysis unit 102 can extract at least one of secondary information contained in the floor plan image, namely, annotation information (e.g., room name, room number, direction symbol, etc.), scale description (scale line, numerical scale, etc.), and room name (living room, Japanese-style room, bedroom, etc.), and use this to perform the following correction or complementation processing: Drawing scaling based on scale description (pixel-based dimensions converted to actual size) Room classification based on room name (labeling of space use based on functional classification) - Consistency check using information obtained from annotations (preventing false positives and complementing room attributes) In other words, these processes function as "pre-processing complementation functions" and constitute technical means for scaling structural information, complementing room attributes, and aligning dimensional values ​​using the extracted secondary information. Note that structural information includes the positional relationships, sizes, and layout coordinates of each component, as well as room dimensions (length, width, area, etc.), which are used as important input information in subsequent design and cost estimation processes.

[0043] In this way, the image analysis unit 102 accurately reads the spatial configuration of the dwelling unit from the pixel information of the drawing image and outputs it as structural information expressed quantitatively and structurally. As a result, it plays an important role as the starting point for the renovation plan proposal process in the renovation support system 1 according to this embodiment, supporting the quality and accuracy of subsequent processes.

[0044] [4.1.3. Configuration and Function of Plan Generator 103] The plan generation unit 103 is a core processing module that receives the structural information generated by the image analysis unit 102 as input and designs, evaluates, and selects renovation proposals based on the structural information and user-specified conditions.

[0045] The plan generation unit 103 first creates a prompt based on the input structural information and conditions specified by the user (e.g., desired floor plan type, family composition, furniture type, preferred flow line, etc.), and inputs the prompt to the generation AI 400. Based on this prompt, the generation AI 400 outputs multiple renovation floor plan proposals including furniture type, position, dimensions, arrangement pattern, etc. The plan generation unit 103 evaluates each output floor plan proposal in light of predetermined design constraints and flow line evaluation criteria. For example, the design constraints include preventing furniture from interfering with each other, ensuring a minimum amount of movement space, and adhering to functional zoning, while the flow line evaluation criteria include the length of the route between rooms, the number of intersections, and smooth circulation.

[0046] The plan generating unit 103 calculates an evaluation value for each floor plan based on the constraints and evaluation criteria described above. This evaluation value may be a quantitative score, or may be a weighted and integrated evaluation value of multiple evaluation indexes. The plan generation unit 103 then selects the most suitable floor plan based on the calculated evaluation values. This optimal plan is passed on to subsequent processes such as estimation, matching, and CG generation, and is used as the basis for proposing a final renovation plan to the user.

[0047] In this way, the plan generation unit 103 flexibly creates floor plan proposals by utilizing the generation AI 400 in response to user requests, and also has the function of objectively judging the merits or demerits of the proposals through a predetermined evaluation logic, thereby forming the core of the intelligent space proposals of the present invention.

[0048] [4.1.4. Configuration and Function of Integration Unit 104] The estimating unit 104 has a function of calculating the construction costs required to realize the renovation floor plan selected by the plan generating unit 103, and outputting the calculation results as a construction estimate.

[0049] First, the estimation unit 104 identifies the type of construction material according to the components included in the proposed floor plan and calculates the required quantity for each. For example, the specifications, area, and quantity of materials required for construction are automatically calculated for each element, such as wall area, floor area, ceiling finish, fixtures, storage equipment, and housing equipment (kitchen, bathroom, toilet, etc.) to be newly constructed or removed. Next, the estimating unit 104 references unit price information for each material from the material database 320 and acquires labor cost information for each type of work. Based on this information, the estimating unit 104 calculates the cost (= unit price × quantity + labor cost) for each material and work item, and adds these up to calculate the construction cost (construction estimate) for the entire renovation. Furthermore, the estimating unit 104 organizes the cost information calculated above by item and automatically generates a construction estimate including the quantity, unit price, subtotal, total amount, etc. of each component. This construction estimate is output to the user terminal 200, and is configured so that the user can immediately grasp the budget corresponding to the renovation content.

[0050] The estimation process in the estimation unit 104 can also be used to automatically estimate rough costs at the design stage, and is also effective as a decision-making support before preparing detailed drawings or requesting estimates. In this way, the estimation unit 104 can quickly and precisely calculate material and labor costs based on structural information and design proposals, and performs an important function of enhancing the practicality of the entire renovation support system 1 and the completeness of automated processing.

[0051] [4.1.5. Configuration and Function of Matching Unit 105] The matching unit 105 is a component that is responsible for extracting construction companies or real estate companies as recommended candidates based on the renovation floor plan selected by the plan generation unit 103 and the conditions specified by the user, and presenting them to the user.

[0052] First, the matching unit 105 receives as input the components included in the proposed floor plan (e.g., new wall construction, kitchen replacement, toilet relocation, etc.) and user-specified conditions (e.g., location, budget, desired construction period, contractor type, etc.), and based on this information, creates a prompt for the generation AI 400. The prompt is a semantic integration of contractor search conditions, and is generated in natural language or structured data format. Here, the generation AI 400 uses the prompt as input to expand or complement the internal conditions (search targets) for extracting suitable construction companies or real estate companies, and outputs more accurate recommended candidate conditions. This utilizes information recorded in the company database 330, namely, feature quantities such as each company's past construction performance, available areas, areas of expertise, customer evaluation scores, price range trends, and response speed.

[0053] The matching unit 105 uses this information to score the candidate companies and generate a list with a recommendation ranking based on the evaluation value. The final list of recommended candidates is presented to the user via the user terminal 200, allowing the user to select a desired company from the list and directly request a quote or make an inquiry as desired. In this way, the matching unit 105 constitutes an important feature of the present invention in that it can assist in the selection of a construction company, which previously required manual searching and inquiries, by linking it with design information and presenting a list of candidates optimized in line with the user's requests and design intentions.

[0054] [4.1.6. Configuration and Function of CG Generation Unit 106] The CG generation unit 106 is responsible for the process of visualizing in three dimensions the completed image of the renovated residential space based on the renovation floor plan selected by the plan generation unit 103, and generating image data (a rendering of the completed space, so-called CG perspective) to present to the user. Specifically, the CG generation unit 106 acquires spatial structure information about the components included in the selected renovation floor plan, namely, walls, floors, ceilings, doors, windows, storage, and equipment, as well as furniture layout information, such as the type, placement position, dimensions, and orientation of the furniture, and then uses this information to virtually construct the interior of the apartment in three-dimensional space.

[0055] The CG generation unit 106 executes the following processes in stages on the constructed three-dimensional model: (1) Material application process Material information such as texture, color, and pattern is assigned to each component (flooring, wallpaper, fixtures, furniture, etc.) to create an appearance that faithfully reproduces the actual finishing materials. (2) Lighting setting processing Lighting conditions are set based on natural light and the placement of lighting fixtures, and the brightness, shadows, and degree of reflection of the entire scene are adjusted, making it possible to create a sense of realism and atmosphere as an indoor space. (3) Rendering process Based on the above 3D model, viewpoint information, materials, and lighting settings, a CG perspective is output as 2D image data through computer graphics processing. It may be generated from multiple viewpoints depending on the user's viewpoint and shooting angle.

[0056] The generated CG perspective is provided to users to intuitively grasp the completed image after renovation, and is used for plan consideration, decision-making, customer proposals, etc. It can also be linked to other systems, such as by converting it into AR / VR format as needed. In this way, the CG generation unit 106 generates a realistic rendering of the completed design from drawing-based design information, thereby matching the design intent with the impression of the space, and dramatically improving the user's satisfaction and visibility.

[0057] [4.1.7. Configuration and Function of Storage Unit 107] The memory unit 107 is an information storage device for temporarily or permanently storing data handled by each of the above-mentioned processing units within the renovation support system 1, and is a fundamental component that supports the stability and reproducibility of the overall information processing of the present invention.

[0058] The storage unit 107 stores, for example, the following information: Input information such as floor plan images, user-specified conditions, desired specifications, desired construction area, etc. acquired from the user terminal 400 via the input / output unit 101. Structural information generated by the image analysis unit 102. A plurality of renovation floor plan proposals generated by the plan generating unit 103 and their evaluation values. The material quantity, unit price, labor cost, estimated amount, and construction estimate calculated by the estimation unit 104. Construction company information and matching results extracted by the matching unit 105. A three-dimensional model and a CG perspective drawing of the completed model generated by the CG generation unit 106.

[0059] The storage unit 107 can organize, classify and record this data by user or by case, and is also used for database reference for suspending and resuming processing, tracking history and reusing. The storage unit 107 is configured by a hard disk device, semiconductor memory, cloud storage, or a combination of these, and provides a flexible and scalable data processing environment for the entire renovation support system 1.

[0060] [4.1.8. Example of hardware configuration of renovation support server 100] 5 is a diagram showing an example of the hardware configuration of a renovation support server 100 according to an embodiment of the present invention. The renovation support server 100 includes a bus 1010, a processor 1020, a memory 1030, a storage device 1040, an input / output interface 1050, and a network interface 1060.

[0061] The bus 1010 is a data transmission path for transmitting and receiving data among the processor 1020, memory 1030, storage device 1040, input / output interface 1050, and network interface 1060. However, the method of connecting the processor 1020 and the like to each other is not limited to bus connection.

[0062] The processor 1020 is implemented by a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or the like.

[0063] The memory 1030 is a main storage device realized by a RAM (Random Access Memory) or the like.

[0064] The storage device 1040 is an auxiliary storage device realized by removable media such as a hard disk drive (HDD), a solid state drive (SSD), or a memory card, or a read-only memory (ROM), and has a recording medium. The recording medium of the storage device 1040 stores program modules that realize each function of the renovation support server 100. The processor 1020 loads each of these program modules into the memory 1030 and executes them, thereby realizing each function corresponding to the program module.

[0065] The input / output interface 1050 is an interface for connecting the renovation support server 100 to various input / output devices (not shown).

[0066] The network interface 1060 is an interface for connecting the renovation support server 100 to the network N. The method for connecting the network interface 1060 to the network N may be wireless connection or wired connection. The renovation support server 100 may communicate with a user terminal 200, etc. via the network interface 1060.

[0067] [4.2. User terminal configuration and functions] The user terminal 200 is an external device connected to the renovation support server 100 via the network N, and is an information processing terminal operated by a user such as a real estate company representative or a sales or design staff member of a construction company.

[0068] The user terminal 200 provides the following functions to support the renovation work for one of the apartment units in the housing complex that the user is in charge of: Select and upload floor plan images - Input of user-specified conditions such as area information, desired specifications, budget, construction period, etc. Receiving and displaying various processing results provided by the renovation support server 100

[0069] Specifically, the user terminal 200 can be a smartphone, tablet, laptop, desktop, or web browser-compatible business terminal. These terminals are equipped with Internet communication functions, a GUI-based user interface, image display functions, and data transmission and reception functions.

[0070] Through the user terminal 200, the user selects and transmits a floor plan image corresponding to the target dwelling unit in the apartment complex, and inputs the area conditions, desired floor plan, interior design preferences, priorities, etc. This information is imported into the renovation support server 100 via the input / output unit 101 and is used in each stage of the image analysis process, plan generation process, estimate creation process, contractor matching process, and CG generation process.

[0071] In addition, the renovation proposals, construction estimates, list of recommended construction companies, CG perspective drawings of the completed project, etc. generated as a result of the processing are displayed on the user terminal 200, and the user can refer to them to carry out tasks such as examining proposals, presenting them to clients, sharing them internally, and applying for approval. In this way, the user terminal 200 constitutes the starting point and the ending point of input and output in this system, and functions as an interface terminal that mediates between user operations and information presentation.

[0072] [4.3. Configuration and Function of Database Group 300] The database group 300 is a group of storage devices for systematically storing and providing information necessary for various processes in the renovation support system 1, and is composed of multiple databases divided by purpose, such as a plan database 310, a materials database 320, and a company database 330. These databases 300 are connected to the renovation support server 100 via a network N, and are configured so that various processing units can refer to these data in real time as necessary.

[0073] The plan database 310 stores a plurality of renovation floor plan proposals generated by the plan generating unit 103, their evaluation values, selection results, user input conditions, etc., in chronological order or on a case-by-case basis. The materials database 320 stores information about the construction materials used by the estimation unit 104, such as material type, dimensions, unit price, finishing specifications, and construction classification. The company database 330 stores feature quantities such as company name, location, service area, specialty, price range, past construction performance, and customer evaluations for the matching unit 105 to select a construction company.

[0074] These databases may be stored in the memory unit 107 provided inside the renovation support server 100, or may be configured to be installed on cloud storage or an external server, and may be configured to be distributed, expandable, and synchronized as needed. In this way, the database group 300 functions as an information infrastructure that supports the accuracy and consistency of various design, estimation, and recommendation processes, and is an essential component for ensuring the reliability and scalability of the automated series of processes of the present invention.

[0075] [4.4. Configuration and Function of Generation AI 400] The generation AI 400 is an intelligent processing module in the renovation support system 1 that assists in understanding the floor plan structure, analyzing the spatial composition, and automatically generating new renovation plans based on a floor plan image of a unit in an apartment building.

[0076] The generation AI 400 receives as input in the plan generation unit 103 a prompt constructed based on the structural information output from the image analysis unit 102 and user-specified conditions, and outputs multiple renovation floor plan proposals including furniture types, placement patterns, dimensions, functional zoning, etc. The generation AI 400 also evaluates the consistency of each output plan in light of predetermined design constraints and flow line evaluation criteria, and assists the plan generation unit 103 in selecting the optimal plan.

[0077] The generation AI 400 according to this embodiment can use a large-scale language model (LLM) such as GPT-4, GPT-4 Turbo, or GPT-4o, which are associated with OpenAI's "GPT (registered trademark)," or Claude 3, Claude 3 Haiku, Claude 3 Sonnet, or Claude 3 Opus, which are associated with Anthropic's "CLAUDE (registered trademark)." Furthermore, it is also possible to apply a multi-modal large-scale language model (Multi-Modal LLM: MMLLM), which is capable of integrated processing of different modalities such as text, drawings, images, and voice, to the generation AI 400. This enables applications such as converting drawing-based input instructions and configuration information read from images into natural language or structured data, and directly generating floor plan proposals and layout drawings that maintain spatial and configuration consistency. In this way, the generation AI 400 is the technical core of the present invention in terms of both understanding the floor plan of the dwelling unit and generating new designs, and is the main means for realizing intelligent automation of spatial proposals.

[0078] In this way, in the renovation support system 1 of this embodiment, the renovation support server 100 works in cooperation with the user terminal 200, database group 300, and generation AI 400 connected via the network N to comprehensively and automatically realize the process of creating a floor plan based on user input and a renovation plan based on various specified conditions. This allows for automated processing of everything from structural analysis of floor plans to design, estimates, and recommendations of construction candidates, streamlining renovation work all at once.

[0079] More specifically, the renovation support system 1 of this embodiment uses a generation AI to generate, evaluate, and select multiple new floor plan proposals that take into consideration furniture placement and daily activity patterns based on floor plan images of an existing dwelling unit, and is capable of automatically and comprehensively executing a series of renovation support processes based on the proposed floor plans, including creating estimates, outputting renderings of the completed building (CG perspective drawings), and recommending construction companies. This allows users to perform everything from structural analysis of floor plans to design, estimates, and selection of construction candidates in one go with minimal operations, resulting in the entire renovation work workflow, which was previously separated, being integrated and processed quickly with high consistency.

[0080] [5. Example of Renovation Support System 1 in Action] 6 is a flowchart showing the overall processing procedure of the renovation support server 100 according to one embodiment of the present invention. The input / output unit 101 of the renovation support server 100 acquires (receives / inputs) user-specified conditions such as a floor plan image and area information corresponding to the property to be renovated, which are transmitted from the user terminal 200 (step S100).

[0081] Here, regional information refers to information about the location of the dwelling unit designated by the user as the property to be renovated, including, for example, the postal code, city, ward, town, village name, nearest station, prefecture, and the building's floor and building number. More detailed conditions can also be included, including information about the desired construction area, local building regulations, and the surrounding environment (commercial facilities, public transportation, sunlight conditions, etc.). This regional information is treated as an important decision parameter because it influences the recommendation of construction companies, price range adjustments, and design constraints (height restrictions, securing evacuation routes, etc.) in the subsequent matching process.

[0082] When the input / output unit 101 acquires these data, the image analysis unit 102 performs image analysis processing to detect walls, rooms, doors, etc. from the floor plan image and generate floor plan structure data (step S200). Figure 7 is a flowchart for explaining the details of the analysis processing (step S200) by the image analysis unit 102 of the renovation support server 100 according to one embodiment of the present invention.

[0083] As shown in FIG. 7, the image analysis unit 102 performs preprocessing such as tilt correction, noise removal, and resolution conversion on the input floor plan image (step S201).

[0084] Next, the image analysis unit 102 extracts elements such as walls, rooms, doors, windows, storage areas, etc. from the pre-processed image using an image processing algorithm or the like (step S202).

[0085] Next, the image analysis unit 102 estimates room attributes such as the name and use of the room (for example, living room, bedroom, kitchen, etc.) from notes, layout, etc. (step S203).

[0086] Next, the image analysis unit 102 digitizes information such as the area of ​​the extracted components, the length of the walls, the positions of the openings, and the orientation of the doors, and converts it into quantitative data of the spatial configuration (step S204).

[0087] Furthermore, the image analysis unit 102 generates floor plan structure data including room IDs, components, dimensional information, etc., based on the digitized structural attribute information (step S205).

[0088] Then, the image analysis unit 102 outputs the generated floor plan structure data to the plan generation unit 103, which is in charge of subsequent processing (step S206). This floor plan structure data becomes the basic information for the renovation plan created by the plan generation unit 103 in the subsequent steps.

[0089] Returning to the flowchart of Fig. 6, after the analysis process (step S200) by the image analysis unit 102, the plan generation unit 103 generates a renovation plan based on the floor plan structure data and user-specified conditions (step S300). Fig. 8 is a flowchart for explaining the details of the renovation plan generation process (step S300) by the plan generation unit 103 of the renovation support server 100 according to one embodiment of the present invention.

[0090] As shown in FIG. 8, the plan generation unit 103 acquires floor plan structure data from the image analysis unit 102 and user-specified conditions from the input / output unit 101 (step S301), and then configures this information as a prompt and inputs it to the generation AI 400.

[0091] The plan generation unit 103 automatically generates a plurality of renovation plans that take into consideration room configuration, traffic flow, functional zoning, furniture arrangement, etc., through processing in cooperation with the generation AI 400 (step S302).

[0092] In the next step S303, each generated plan is verified based on predetermined design constraints (furniture interference prevention, spatial balance, storage rate, etc.) and traffic flow evaluation criteria (movement efficiency, circulation of traffic flow, safety, etc.).

[0093] As a result, for plans that are determined to be problem-free in step S303, the plan generation unit 103 maps furniture placement information (furniture type, size, orientation, coordinates, etc.) to floor plan structure data, and ultimately constructs one or more renovation plan data (step S304). On the other hand, if a plan is determined to have a problem in step S303, it is discarded, or if necessary, the process returns to step S302 to generate a new renovation plan.

[0094] After one or more renovation plan data are constructed in step S304, the renovation plan with the highest evaluation value is selected from among them, and that plan is output to the user terminal 200 via the input / output unit 101 and stored (saved) in the plan database 310 (step S305). In this way, the plan generation unit 103 utilizes the generation AI 400 to create, evaluate, and select flexible and logical floor plan proposals, and can provide the user with a consistent renovation plan.

[0095] Returning to the flowchart of Fig. 6, in parallel with the renovation plan generation process (step S300) by the plan generation unit 103, or after the renovation plan has been generated, the estimating unit 104 generates a construction estimate for the generated renovation plan (step S400). Fig. 9 is a flowchart for explaining the details of the construction estimate generation process (step S400) by the estimating unit 104 of the renovation support server 100 according to one embodiment of the present invention.

[0096] The estimating unit 104 acquires information on the components of the renovation plan, that is, the wall area, floor area, number of doors and windows, and arrangement of household equipment, from the plan generating unit 103 (step S401).

[0097] Next, the estimating unit 104 refers to the material database 320 to acquire unit price information corresponding to the building materials (finishing materials, structural materials, equipment, etc.) required for each component (step S402). At this time, the labor unit price of the workers required for the renovation work is also acquired at the same time from any of the databases in the database group 300 (including databases not shown), or from user-specified conditions, etc.

[0098] Furthermore, the estimating unit 104 calculates the cost for each component based on the acquired quantity of building materials and their respective unit price information, adds labor costs as necessary, and then estimates the cost for each construction item (step S403).

[0099] Furthermore, the estimating unit 104 automatically generates a construction estimate in a format including the cost breakdown by material, unit price, quantity, subtotal, and total amount based on the estimation result in step S403 (step S404).

[0100] The construction estimate created by the estimating unit 104 is then output from the input / output unit 101 to the user terminal 200 (step S405). This allows the user to immediately refer to the estimate information. In this way, the estimating unit 104 can quickly and accurately calculate related material information and labor costs based on the contents of the renovation plan, and automatically generate a construction estimate. This allows the user to conduct more specific considerations while grasping a realistic budget that corresponds to the design intent of the renovation proposal.

[0101] Returning to the flowchart of Figure 6, in parallel with the renovation plan generation process by the plan generation unit 103 (step S300) and the construction estimate generation process by the estimating unit 104 (step S400), or after the renovation plan and construction estimate have been generated, the matching unit 105 executes matching with a construction company that will carry out the renovation according to the generated renovation plan (step S500). Figure 10 is a flowchart for explaining the details of the matching process (step S500) by the matching unit 105 of the renovation support server 100 according to one embodiment of the present invention.

[0102] As shown in FIG. 10, the matching unit 105 first acquires a proposed renovation plan (a data structure including room configuration, construction items, estimated budget, etc.) acquired from the plan generation unit 103 or the storage unit 107 (step S501).

[0103] Next, the matching unit 105 acquires the area information and user-specified conditions (for example, desired price range, construction period, service area, etc.) acquired from the user terminal 200 or the storage unit 107 (step S502).

[0104] Furthermore, based on the acquired information, the matching unit 105 performs formatting processes such as tagging the plan components (e.g., replacing plumbing, changing partitions, etc.), clarifying the specified conditions, and converting the generated AI 400 into a prompt format (step S503).

[0105] Furthermore, the matching unit 105, in cooperation with the generation AI 400, extracts conditions of construction companies that are suitable in terms of regional response, construction track record, areas of expertise, customer evaluations, price range, etc., and generates candidate matching conditions (step S504).

[0106] Furthermore, the matching unit 105 queries the company database 330 to acquire company information that matches the output of the generation AI 400 (step S505). Here, detailed information such as the company name, location, service area, specialty construction type, price trend, etc. are also acquired.

[0107] Furthermore, the matching unit 105 performs a scoring process on each candidate company based on a plurality of evaluation indices (trust score, past evaluation, regional suitability, etc.) (step S506).

[0108] Then, the matching unit 105 formats and outputs the scoring-processed matching results (each company candidate) as a list sorted according to recommendation order (step S507). In this way, the matching unit 105 can dynamically and automatically present construction company candidates that are most suitable for the user's wishes and design content through advanced semantic processing using the generation AI 400.

[0109] Returning to the flowchart of Fig. 6, after the plan generation unit 103 generates a renovation plan (step S300), or after the estimating unit 104 generates a construction estimate (step S400) and the matching unit 105 performs matching (step S500), the CG generation unit 106 generates a three-dimensional model based on the renovation plan, etc., and performs rendering to generate a CG perspective (a conceptual drawing of the completed building) (step S600). Fig. 11 is a flowchart for explaining the details of the CG perspective generation process (step S600) by the CG generation unit 106 of the renovation support server 100 according to one embodiment of the present invention.

[0110] The CG generation unit 106 acquires the renovation plan proposal and furniture arrangement information (types, arrangement positions, dimensions, orientations, etc. of furniture) from the plan generation unit 103 or the storage unit 107 (step S601).

[0111] Next, the CG generating unit 106 converts the acquired information in the two-dimensional coordinate system into a three-dimensional space format, and prepares it for placement in the virtual space (step S602).

[0112] Furthermore, the CG generation unit 106 specifically arranges furniture models in the three-dimensional space in addition to architectural structural elements such as walls, floors, ceilings, and fittings, to form a structure that is close to the real space (step S603).

[0113] Furthermore, the CG generation unit 106 performs a material application process for each of the formed components (step S604). This material application process includes applying material information such as the texture, color, pattern, and gloss of flooring, wallpaper, and furniture surface finishes. This allows the CG object to have an appearance that matches the real material.

[0114] Furthermore, the CG generation unit 106 executes lighting setting processing such as the arrangement of lighting (natural light, ceiling lighting, etc.), color temperature of the light source, brightness, and shadow expression, to create a sense of realism throughout the space (step S605).

[0115] Furthermore, the CG generation unit 106 sets a camera angle and perspective (bird's-eye view, eye level, dynamic viewpoint, etc.) that matches the user's viewpoint, and adjusts it so that the space is captured from a viewpoint that looks good (step S606).

[0116] Finally, the CG generation unit 106 performs rendering processing on the entire virtual space reflecting the above configuration and settings, and generates a CG perspective (a conceptual drawing of the completed work) as a two-dimensional image (step S607). This image is output from multiple viewpoints as needed, and is stored in the storage unit 107, and is also transmitted via the input / output unit 101 so that it can be displayed or saved on the user terminal 200. In this way, the CG generation unit 106 automatically generates highly accurate 3D visuals from blueprint-based plan information, enabling the user to intuitively and visually visualize the completed space. This ensures consistency between the design intent and the impression of the space, and realizes renovation support with a high level of proposal power and persuasiveness.

[0117] As described above, the renovation support system 1 of this embodiment can automatically execute each process from structural analysis of floor plan, plan generation, estimate creation, recommendation of construction candidates, and CG generation, starting from input from the user, in accordance with the various processing flows shown in Figures 6 to 11, with each module coordinating with each other.

[0118] 1 to 3 and 6 to 11 are merely examples of the present invention. Within the scope of the present invention, processes other than those described in the flowcharts may be included, some of the processes described in the flowcharts may be omitted, or the order of the processes may be changed.

[0119] Although the embodiments of the present invention have been described above, the embodiments disclosed herein are illustrative in all respects and should not be considered limiting. The scope of the present invention is defined by the claims rather than the above description, and it is intended to include meanings equivalent to the claims and all modifications within the scope of the claims. [Explanation of symbols]

[0120] 1. Renovation support system 100 Renovation support server (device) 101 Input / output section 102 Image analysis unit 103 Plan Generation Unit 104 Integration Unit 105 Matching Department 106 CG generation department 107 Storage section 200 user terminals 300 databases (DB) 310 Plan Database 320 Materials Database 330 company database 400 Generation AI 1010 Bus 1020 processor 1030 memory 1040 Storage Device 1050 Input / Output Interface 1060 Network Interface N Internet

Claims

1. A method for improving the efficiency of renovation construction estimate work, which is carried out by a real estate business operator using a computer, The method is executed using a renovation support server that analyzes structural information including room dimensions and wall thickness from floor plan data, selects a renovation plan based on the structural information using a generation AI that takes into account furniture placement and daily activity lines, and automatically calculates a construction estimate from the necessary building materials and labor costs based on the renovation plan, A step of transmitting floor plan data of an existing real estate property being considered for purchase from the terminal of the real estate business operator to the renovation support server; receiving, from the renovation support server, the selected renovation plan and a renovation construction estimate calculated based on the renovation plan, at a terminal of the real estate business; Calculating an estimated selling price based on the received construction estimate, the planned purchase price of the existing real estate property, and the business profit; a step of evaluating whether the calculated expected selling price is reasonable in the market and determining whether to purchase the existing real estate property based on the evaluation; A method for improving the efficiency of estimate work for renovation work, comprising:

2. 2. The method for improving the efficiency of quotation work according to claim 1, A method for improving the efficiency of quotation work, characterized by further comprising a step of receiving a CG perspective drawing corresponding to the renovation plan from the renovation support server and presenting the CG perspective drawing to the customer.

3. A method for improving the efficiency of renovation work estimates, which is carried out by a real estate brokerage business using a computer, comprising: The method is executed using a renovation support server that analyzes structural information including room dimensions and wall thickness from floor plan data, selects a renovation plan based on the structural information using a generation AI that takes into account furniture placement and daily activity lines, and automatically calculates a construction estimate from the necessary building materials and labor costs based on the renovation plan, A step of transmitting floor plan data of an existing real estate property that has not yet been renovated to be introduced to a customer from a terminal of the real estate brokerage business to the renovation support server; receiving, from the renovation support server, the selected renovation plan and an estimated cost of the renovation work calculated based on the renovation plan, at a terminal of the real estate brokerage business; a step of calculating a total amount by adding up the received estimate amount and the purchase price of the existing real estate property in an unrenovated state, and presenting the total amount to the customer; receiving a CG perspective drawing corresponding to the renovation plan from the renovation support server, and presenting the CG perspective drawing to the customer together with the total price; receiving matching information regarding candidates for construction companies capable of carrying out the renovation work from the renovation support server; A method for improving the efficiency of estimate work for renovation work, comprising:

4. A method for improving the efficiency of renovation construction estimate work, which is carried out by a construction company using a computer, comprising: The method is executed using a renovation support server that analyzes structural information including room dimensions and wall thickness from floor plan data, selects a renovation plan based on the structural information using a generation AI that takes into account furniture placement and daily activity lines, and automatically calculates a construction estimate from the necessary building materials and labor costs based on the renovation plan, A step of transmitting floor plan data of the existing real estate property to be renovated, provided by the client, from the terminal of the construction company to the renovation support server; receiving, from the renovation support server, the selected renovation plan and renovation work estimate data including an estimate for the renovation work calculated based on the renovation plan, at the terminal of the construction company; receiving matching information from the renovation support server regarding candidates for construction companies that can carry out the construction work included in the renovation work; A method for improving the efficiency of estimate work for renovation work, comprising:

5. 5. The method for improving the efficiency of quotation work according to claim 4, A method for improving the efficiency of quotation work, further comprising a step of outputting the received renovation work quotation data as information to be submitted to the client.

6. 5. The method for improving the efficiency of quotation work according to claim 4, A method for improving the efficiency of quotation work, characterized by further comprising a step of receiving a CG perspective drawing corresponding to the renovation plan from the renovation support server and presenting the CG perspective drawing to the customer.

7. 7. The method for improving the efficiency of quotation work according to claim 1, The method is a method for improving the efficiency of quotation work, characterized in that it is provided in a SaaS (Software as a Service) format based on a subscription contract such as a fixed monthly fee.

8. In the method for improving the efficiency of quotation work according to any one of claims 1 to 6, A method for improving the efficiency of quotation work, wherein the existing real estate property is a single dwelling unit in an apartment building.

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