Machine Learning Model for Construction Litigation Risk Prediction
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Solution Overview
Problem
Current systems lack a computer-intelligent method to detect early signs of potential disputes and litigation risks in construction and engineering projects from electronic correspondences, which are often identified too late to enable proactive interventions.
Innovation Solution
A machine learning-based monitoring system that uses semantic natural language understanding to predict litigation risks in electronic communications by training a model with known litigious and non-litigious language, generating alerts in near-real time, and allowing user feedback for improved accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional manual review methods are used to identify litigation risks in electronic correspondences, then detection accuracy may be maintained through human expertise, but the detection time is too slow and cannot provide early warnings
Solution Approach 1:
The patent replaces manual human review of electronic correspondences with an automated machine learning system that uses natural language processing to analyze communication patterns. This substitution enables near-real-time detection of litigation risks while maintaining detection accuracy through trained algorithms that identify subtle indicators of potential disputes that humans might miss in fast-moving communication streams.
2Reliability
If no automated system is implemented, then system complexity remains low, but early detection capability is lost and litigation risks cannot be predicted
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between electronic correspondences and risk detection. This intermediary automatically processes and analyzes communication patterns, extracting meaningful signals that indicate potential litigation risks. The system complexity is managed through modular architecture where the ML model serves as a specialized component that bridges raw data and actionable insights without requiring complete system redesign.
Solution Approach 2:
The system implements self-service through automated training and feedback mechanisms. The machine learning model continuously learns from labeled data and user feedback, automatically improving its detection capabilities without requiring manual reconfiguration. This self-improving nature enhances reliability over time while the initial system complexity is paid off through automated operations that reduce ongoing manual intervention.
3Measurement precision
If comprehensive analysis of all electronic correspondences is performed, then detection accuracy improves, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the most relevant features and patterns from electronic correspondences that are indicative of litigation risks, rather than analyzing every aspect of all communications. The machine learning model identifies and focuses on key indicators such as specific language patterns, communication frequencies, and contextual signals that strongly correlate with potential disputes. This selective extraction maintains high detection accuracy while dramatically reducing processing time and computational resource requirements.
Data Source
AI summary
Systems, methods, and other embodiments associated with a machine learning system that monitors and detects risk in electronic correspondence related to a construction project are described. In one embodiment, a method includes monitoring email communications over a network to identify an email; tokenizing text from the email into a plurality of words and initiating a machine learning classifier configured to identify construction terminology and to classify text with a risk as being litigious or non-litigious. The machine learning classifier processes the words from the email by at least corresponding the words to a set of defined litigious vocabulary and defined non-litigious vocabulary. The email is labeled as litigious or non-litigious. An electronic notice is generated and transmitted to a remote device in response to the email being labeled as being litigious to provide an alert in near-real time in relation to receiving the email over the network.