Construction AI Query Interface for Specification Retrieval
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Solution Overview
Problem
Construction specifications are lengthy and difficult to navigate, leading to inefficient retrieval of critical project information, with data often siloed across various platforms, requiring labor-intensive searches to find relevant details.
Innovation Solution
A construction management system utilizing generative AI to allow users to ask natural language queries, integrating Retrieval-Augmented Generation and Generative Language Models to streamline information retrieval from specifications and project data, providing accurate and contextual responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If construction specifications are made detailed and comprehensive to cover all project requirements, then the completeness and reliability of project information is improved, but the time and effort required to retrieve specific information increases significantly
Solution Approach 1:
The patent introduces an AI-powered natural language processing intermediary that acts as a mediator between users and the comprehensive construction specification database. This intermediary translates user queries into structured search operations, automatically navigates through the detailed specifications, and retrieves relevant information without requiring users to manually search through the entire document, thus resolving the contradiction between having complete information and quick access to it
Solution Approach 2:
The patent replaces the mechanical manual search process with an automated AI-based information retrieval system. Instead of physically flipping through pages or manually searching through detailed specifications, users can ask natural language questions and the system automatically processes and retrieves the required information, significantly reducing retrieval time while maintaining access to comprehensive project data
2Ease of manufacture
If construction data is organized into separate siloed platforms for different project aspects, then the organization and manageability of specific data types is improved, but the ability to access integrated information across platforms deteriorates
Solution Approach 1:
The patent implements a universal AI-powered search interface that can access and integrate information across multiple previously siloed construction data platforms. This single interface performs multiple functions: it queries different data sources (specifications, project data, schedules, RFIs), integrates results from various platforms, and presents unified answers, thereby maintaining the organizational benefits of separate platforms while enabling easy access to integrated information
Solution Approach 2:
The patent merges the access to multiple separate construction data platforms through a unified AI-powered natural language interface. While the data platforms remain separately organized, the system combines their accessibility by allowing users to query all platforms simultaneously through natural language, integrating information from specifications, project data, schedules, and RFIs into comprehensive responses
3Device complexity
If manual searching methods are used to find specific information in construction specifications, then the simplicity and low cost of the system is maintained, but the productivity and efficiency of information retrieval deteriorates
Solution Approach 1:
The patent implements a self-service AI system that automatically processes user queries without requiring complex manual search operations. The system autonomously interprets natural language questions, navigates through construction specifications and project data, retrieves relevant information, and presents answers, thereby maintaining interface simplicity while dramatically improving retrieval efficiency through automated intelligent processing
Data Source
AI summary
A method and system provide the ability to process a construction domain query. A natural language user query is obtained within a construction software system. The user query is pre-processed to validate the query. Text from the query is embed into search vectors for a semantic search. A data source having multiple different sections is obtained. The semantic search is performed within each of the sections and identifies semantically relevant sections. The relevant sections are consolidated into a contextual data prompt that is input into an LLM. The LLM, which is trained based on construction data, generates a response that identifies the relevant sections. The response and an identification of the relevant sections is output.