AI Pixel Analysis for Design Plan Data Extraction
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Solution Overview
Problem
Traditional methods for extracting comprehensive data from design plans, particularly in construction and architectural domains, are labor-intensive, prone to errors, and lack the necessary granularity and precision for accurate segment-specific analysis and cost estimation.
Innovation Solution
An AI-driven system that enables users to select segments within two-dimensional design plans, conducting pixel-level analysis to extract detailed data on segment-specific specifications, including material lists and labor costs, facilitating precise cost estimation and streamlined planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination and interpretation of design blueprints is used, then comprehensive data extraction is possible, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical examination of design blueprints with an automated AI-based image processing system. The system uses machine learning models to automatically analyze design plans, extract segment information, and generate cost estimates, eliminating the need for labor-intensive manual interpretation while maintaining or improving accuracy.
Solution Approach 2:
The patent introduces an AI-based image processing system as an intermediary between the design blueprint and the final data extraction. This intermediary automatically processes the visual information in design plans, identifies segments, and extracts relevant data, bridging the gap between raw design documentation and actionable project information without requiring manual intervention.
2Productivity
If conventional automation tools are used, then some automation is achieved, but granularity and precision for detailed segment-specific analysis remain insufficient
Solution Approach 1:
The patent applies segmentation by dividing the design plan into distinct segments or regions of interest. The AI system identifies and separates different components (e.g., walls, windows, doors, fixtures) within the design plan, allowing for detailed segment-specific analysis. This segmentation enables precise extraction of information for individual elements while maintaining overall productivity through automation.
Solution Approach 2:
The patent implements local quality by applying different analysis methods and levels of detail to different segments of the design plan. The AI system can focus computational resources on specific segments requiring detailed analysis, such as areas with complex features or high cost implications, while using more efficient processing for simpler segments, thereby achieving both precision and productivity.
3Extent of automation
If existing algorithmic approaches are used, then some automation is provided, but comprehensive data extraction at granular level is not achieved
Solution Approach 1:
The patent applies preliminary action by pre-training AI models on extensive datasets of design plans and associated cost information before deployment. The system performs preliminary processing of design plans to identify and classify segments, prepare extraction templates, and establish data structures in advance. This preliminary preparation enables comprehensive data extraction at granular levels during actual use without sacrificing automation efficiency.
Data Source
AI summary
Methods and systems employing artificial intelligence for efficient data extraction and analysis for a user-defined design plan segmentation. The method enables users to select specific segments within two-dimensional design plans, thereby triggering an AI engine to execute pixel-pattern analysis on the chosen segments. This process facilitates the extraction of comprehensive data related to these segments, encompassing detailed segment-specific information, aggregated counts of similar segments in the design plans, material lists, material costs, and labor expenses. Additionally, the invention includes methods for component searching within the design plans using symbols or polygon shapes selected by users, involving pixel-level analysis to identify matching dynamic components within the design plans. The resulting comprehensive data involves specific component details, quantities, associated costs, and possible automated suggestions for design alterations, enhancing user decision-making and enabling seamless planning aligned with users' budget and quality standards.