Time Curve Graph for Contract Risk Identification
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Solution Overview
Problem
Manual tracking of contract version differences is time-consuming and resource-intensive, making it difficult for businesses to efficiently manage and understand the state of multiple contracts during negotiations.
Innovation Solution
A computer-implemented method that generates a time curve graph based on contract metadata to visually represent similarities and temporal ordering of contract documents, using geometric analysis to identify risk and notify administrators of potential issues, thereby automating the risk analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking of contract version differences is used, then employees can document modifications, but it results in exorbitant amounts of time and computing resources
Solution Approach 1:
The patent replaces the manual mechanical process of tracking contract versions with an automated computational system. The system uses natural language processing and machine learning algorithms to automatically generate time curve graphs from contract documents, eliminating the need for manual documentation while maintaining accurate tracking of all modifications across contract versions.
Solution Approach 2:
The system enables self-service automation where the contract analysis system automatically generates time curve graphs and identifies risks without requiring employee intervention. The machine learning model continuously monitors contract versions and autonomously produces visual representations of contract evolution, allowing the system to serve itself in tracking and analyzing contract changes.
2Measurement precision
If manual tracking of contract version differences is used, then employees can document modifications, but it requires exorbitant amounts of computing resources
Solution Approach 1:
The patent segments the contract analysis process into distinct functional modules: text extraction, natural language processing, time curve generation, and risk identification. Each module processes specific aspects of contract data independently, allowing for optimized resource allocation and efficient computation. This segmentation enables the system to handle multiple contracts simultaneously with reduced computing resource requirements.
Solution Approach 2:
The system dynamically adjusts processing parameters based on contract complexity and volume. The machine learning model adapts its computational intensity and processing depth according to the specific characteristics of each contract set, optimizing resource usage by applying appropriate levels of analysis rather than uniform high-computation processing to all contracts.
3Reliability
If automated time curve graph generation is implemented, then risk identification is improved, but the system complexity increases
Solution Approach 1:
The patent introduces time curve graphs as an intermediary visual representation between raw contract text and risk identification. The system generates standardized time curve graphs that visually encode contract evolution patterns, serving as a mediator that simplifies the complex task of risk analysis. This intermediary layer translates unstructured contract data into a standardized visual format that the machine learning model can efficiently interpret for risk identification.
Data Source
AI summary
In one embodiment, a method includes using a first computing device to access digital data representing a contract set of contract documents and digital contract metadata, weight differences between data field values, use the weighted differences to calculate distance scores, use the distance scores and a temporal ordering of the contract documents to generate and cause displaying a time curve graph comprising a geometric shape that comprises time curves and a spatial proximity at a second computing device, a measure of each time curve calculated to indicate an amount of time between a creation of two contract documents, the spatial proximity calculated to indicate a metric of similarity between the contract documents, in response to determining that the geometric shape indicates a lack of convergence over a threshold amount of time, generate and cause displaying a notification indicating that the contract set is at risk at the second computing device.