Unified Vulnerability Scoring via Covariate Vectors

The patent combines multiple vulnerability scoring systems (CVSS, CWE, and other taxonomies) into a unified machine learning model that processes diverse input features from different scoring systems. The model merges these heterogeneous data sources to generate a single, consistent risk score, eliminating terminology discrepancies while preserving the comprehensive assessment capabilities of individual systems.

The patent creates a universal vulnerability scoring framework that can handle multiple input formats and scoring systems through a single machine learning model. This multi-functional system accepts diverse vulnerability data (CVSS scores, CWE classifications, exploit availability, patch status) and produces standardized risk assessments, making the system adaptable to various vulnerability types and scoring methodologies.

The patent implements a self-service vulnerability assessment system where the machine learning model automatically processes vulnerability data, generates risk scores, and prioritizes findings without requiring manual analyst intervention. The model trains on historical vulnerability data and autonomously applies learned patterns to new vulnerabilities, maintaining high accuracy while eliminating time-consuming manual review processes.

The patent replaces the mechanical process of manual vulnerability review with an automated machine learning system. Instead of human analysts manually evaluating each vulnerability, the system uses trained algorithms to automatically assess risk, prioritize findings, and generate remediation recommendations, dramatically reducing assessment time while maintaining or improving accuracy.