ML Engine for Contract Attribute Prediction
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Solution Overview
Problem
Organizations face challenges in predicting the time and effort required to establish complex contracts and assessing the likelihood of contract performance due to unclear contract characteristics, such as parties, political conditions, and geographic locale, which hinders effective mitigation planning.
Innovation Solution
A machine learning engine is employed to analyze documents, determine attributes, and train models to predict attribute values, ranking candidate models based on accuracy scores to improve efficiency and accuracy in contract analysis and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of contract characteristics is used, then organizations can review contract details, but it requires significant time and effort to predict contract performance and establishment time
Solution Approach 1:
The patent replaces manual mechanical analysis of contract characteristics with an automated machine learning system. The ML engine processes contract documents, extracts attributes, and generates predictions automatically, eliminating the need for human reviewers to manually analyze each contract's parties, political conditions, geographic locale, and other characteristics.
Solution Approach 2:
The system enables contracts to be analyzed and evaluated autonomously without human intervention. The ML models self-service by automatically ingesting contract data, determining attributes, generating predictions about establishment time and performance likelihood, and providing results without requiring manual effort from organization personnel.
2Reliability
If comprehensive contract characteristics are analyzed, then prediction accuracy improves, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: document ingestion, attribute determination, model selection, and prediction generation. The system divides contract characteristics into separate attributes (parties, political conditions, geographic locale, subject matter, value/cost) that can be processed independently by specialized ML models, making the overall system more manageable despite comprehensive analysis.
Solution Approach 2:
The ML engine serves multiple functions within a single system: it ingests various contract types, determines diverse attributes, selects appropriate models based on contract characteristics, and generates multiple predictions (establishment time, performance likelihood, outlier detection). This multi-functional approach consolidates complexity into a universal platform rather than requiring separate systems for each analysis task.
3Reliability
If multiple contract attributes are evaluated, then mitigation planning improves, but the effort to establish contracts increases
Solution Approach 1:
The system performs preliminary analysis of contract characteristics and generates predictions about potential risks and establishment timelines before contracts are fully established. By evaluating attributes like political conditions, geographic locale, and subject matter in advance, the system enables organizations to plan mitigations proactively rather than reactively, improving reliability without adding post-establishment effort.
Solution Approach 2:
The system provides feedback to organizations about contract characteristics that may impact performance or establishment time. By analyzing multiple attributes and comparing them against learned patterns from training data, the system generates actionable insights that help organizations make informed decisions during contract establishment, improving mitigation planning while maintaining efficiency through automated rather than manual evaluation.
Data Source
AI summary
Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.