AI Sectioning of Construction Specifications for Faster Query Retrieval
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Solution Overview
Problem
Construction specifications are lengthy and complex, making it difficult for users to efficiently locate specific information, with existing systems failing to automate information retrieval and requiring manual, time-consuming navigation and search processes.
Innovation Solution
The invention employs AI-powered automation to section construction specifications into logically correct segments, using deep learning models to filter irrelevant sections and generate accurate query answers based on metadata and vector embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If construction specification documents are kept complete and detailed, then information completeness is improved, but search time and complexity increase
Solution Approach 1:
The patent divides construction specification documents into hierarchical sections and subsections using natural language processing and machine learning. The system automatically identifies section headers, organizes content into logical groups, and creates a structured table of contents. This segmentation allows users to navigate to specific sections rather than searching through entire documents, reducing search time while preserving complete information in the organized structure.
2Measurement precision
If construction specification documents are manually searched, then information accuracy can be verified, but productivity decreases
Solution Approach 1:
The patent replaces manual mechanical search processes with automated computational systems. Machine learning models process document text, identify relevant sections, and retrieve information automatically. The system uses natural language queries to search through structured specification data, providing accurate results without manual page-by-page review, thus maintaining accuracy while dramatically improving productivity.
3Ease of operation
If construction specification documents are organized into sections, then ease of operation improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary organization of construction specification documents into structured sections and subsections before users need to search. The system pre-processes documents using NLP and machine learning to create hierarchical structures, generate tables of contents, and index content. This preliminary action ensures that when users need information, the documents are already organized for easy navigation, without requiring complex real-time processing during user interaction.
Data Source
AI summary
A method and system process a construction domain query. A user query relating to a construction specification document is obtained. The construction specification document is obtained A Table of Content (ToC) page detection module autonomously classifies each page of the construction specification document as a table of content (ToC) or not. A text extraction module autonomously extracts text content from the construction specification document. A document sectioning module autonomously outputs section titles based on the ToC page classification results and the extracted text content, and sections the construction specification document into sections based on the section titles. The user query is processed based on the sectioned construction specification document.