Construction Software Usage Modeling for Project Performance Prediction
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Solution Overview
Problem
Existing construction management software applications lack the ability to evaluate a party's usage of their software features to predict the impact on project performance and provide recommendations for improvement, leading to suboptimal usage and varying project outcomes.
Innovation Solution
A computing platform that utilizes data science and machine-learning models to analyze usage metrics of construction management software tools, predicting performance impacts and generating insights and recommendations to enhance project outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If construction management software applications are used to manage projects, then project management capabilities are improved, but the ability to evaluate usage impact and predict performance deteriorates
Solution Approach 1:
The system implements feedback by continuously monitoring software usage metrics and feeding this information back through machine learning models to generate performance predictions and recommendations. Usage data from multiple sources (tool usage, project data, user interactions) is collected and processed to provide actionable insights that improve future project management decisions
Solution Approach 2:
The patent replaces manual evaluation of usage impact with automated machine learning models. Instead of relying on human analysis of software usage patterns, the system uses trained ML models to automatically process usage metrics and generate performance predictions, thereby capturing and utilizing information that would otherwise be lost
2Productivity
If software tools are used for construction management, then management efficiency is improved, but understanding of performance impact deteriorates
Solution Approach 1:
The system establishes feedback loops where software usage data is continuously monitored and fed back through machine learning models to generate performance impact assessments. This feedback mechanism ensures that as software usage increases productivity, the system simultaneously maintains understanding of performance impact through automated analysis and prediction
Solution Approach 2:
The patent introduces machine learning models as intermediaries between software usage and performance outcomes. These models act as mediators that process usage metrics and translate them into meaningful performance predictions, preventing the loss of information about performance impact while maintaining high management efficiency
3Measurement precision
If usage metrics are collected and analyzed, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct components: usage metric collection, data processing, machine learning model training, and prediction generation. By dividing the system into modular segments that handle specific functions, prediction accuracy improves through focused data analysis while overall system complexity is managed through clear separation of concerns
Solution Approach 2:
The patent employs machine learning models as intermediaries that handle the complexity of analyzing usage metrics. Instead of requiring complex custom analysis systems, standardized ML models process the metrics and generate predictions, thereby improving prediction accuracy while keeping the overall system architecture relatively simple and maintainable
Data Source
AI summary
A computing system is configured to: (i) create a data science model that is configured to (a) receive a value for a metric that provides insight regarding a party's usage of a software tool of a construction management software application on a construction project and (b) based on an evaluation of the received value for the metric, output a prediction of the party's performance on the construction project and, (ii) after creating the data science model, utilize the data science model to produce a prediction of a given party's performance on a given construction project by inputting a given value for the metric into the data science model and thereby causing the data science model to (a) evaluate the given value of the metric, and (b) based on the evaluation, output the prediction of performance on the given construction project.