Attribute-Specific Predictive Pipelines for Construction Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Construction management software applications lack functionality for verifying the completeness, accuracy, and format of project attribute data, leading to unreliable data that degrades search reliability and predictive insights.
Innovation Solution
Implementing attribute-specific predictive analytics pipelines that utilize pre-processing logic and AI models to accurately predict and populate project attribute values, integrating with construction management software to enhance data reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If construction management software stores project attribute data without verification, then data storage is simple and quick, but data completeness and accuracy deteriorate
Solution Approach 1:
The patent applies preliminary action by implementing predictive analytics pipelines that proactively predict and populate project attribute values before they are needed or before data completeness issues arise. The system pre-processes available data sources (drawings, specifications, RFIs, etc.) to generate predicted attribute values, ensuring data reliability is established in advance rather than reacting to missing or incorrect data later.
Solution Approach 2:
The patent introduces an intermediary layer between raw project data and stored project attributes. The predictive analytics pipeline acts as a mediator that transforms unstructured or semi-structured data from various sources into verified, complete project attribute values. This intermediary processing layer ensures data reliability without requiring fundamental changes to the core software architecture.
2Measurement precision
If manual verification of project attribute data is implemented, then data accuracy improves, but time consumption and labor requirements increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically verify and complete project attribute data without human intervention. The predictive analytics pipelines autonomously extract information from available project sources, predict missing attribute values, and populate the database. This automated self-verification process maintains high measurement precision while eliminating the time loss associated with manual verification.
Solution Approach 2:
The patent replaces manual verification mechanisms with automated computational systems. Instead of human operators manually checking and completing project attributes, the system uses AI models and pre-processing logic to automatically analyze project data sources and generate accurate attribute predictions. This substitution of mechanical (manual) verification with automated computational verification dramatically reduces time consumption while maintaining or improving accuracy.
3Reliability
If comprehensive data validation is performed on all project attributes, then data quality improves, but processing complexity and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the data validation process into attribute-specific predictive analytics pipelines. Each pipeline is dedicated to predicting and validating specific project attributes (e.g., project type, occupancy code, construction type) using tailored pre-processing logic and AI models. This segmentation allows comprehensive data quality validation without requiring a single monolithic complex validation system, as each pipeline can be independently optimized and maintained.
4Loss of information
If predictive analytics pipelines are implemented for all project attributes, then data completeness improves, but system complexity and development time increase
Solution Approach 1:
The patent implements universality by creating a standardized predictive analytics pipeline framework that can be applied to multiple different project attributes using the same fundamental architecture. The system uses common data sources (drawings, specifications, RFIs, daily logs) and uniform pre-processing approaches across different attribute types. This universal framework reduces system complexity compared to developing separate custom solutions for each attribute, as the same pipeline structure serves multiple functions while maintaining attribute-specific customization through configurable pre-processing logic and models.
Data Source
AI summary
An example computing platform is configured to: (i) detect a trigger event for determining a value of a given project attribute for a given construction project having a stored set of project attribute data; (ii) in response to detecting the trigger event, execute an attribute-specific set of one or more predictive analytics pipelines for predicting one or more values of the given project attribute based on respective sets of source data for the one or more predictive analytics pipelines; and (iii) update the stored set of project attribute data for the given construction project based on the one or more values of the given project attribute that are predicted for the given construction project.