Brand Protection System Using Multi-Score Risk Analysis

The system enables automated self-monitoring and self-analysis of brand abuses through crawlers that automatically crawl web pages, extract content, and generate risk scores without requiring continuous manual intervention. The system serves itself by autonomously identifying and prioritizing abusive websites based on multiple criteria including relevance, popularity, investment level, and damage potential.

The patent replaces manual mechanical analysis with automated computational systems. crawlers automatically navigate and extract data from websites, algorithms compute relevance scores by comparing domain names and content against brand portfolios, and machine learning models generate risk assessments. This substitution of manual processes with automated mechanical systems resolves the contradiction between accuracy and time consumption.

The protection system is segmented into distinct functional modules: crawlers for data collection, relevance computation engines for scoring, priority determination algorithms for ranking, and automated response systems for mitigation. Each module handles a specific aspect of brand protection, allowing comprehensive analysis to be distributed across multiple specialized components rather than a single complex system.

The system employs universal crawlers that can analyze multiple types of websites across different domains, a relevance computation engine that handles various brand portfolios simultaneously, and a priority determination system that works with diverse abuse scenarios. This multi-functionality allows comprehensive brand protection without proportionally increasing system complexity, as the same core mechanisms serve multiple purposes.

The system incorporates feedback mechanisms where relevance scores, popularity metrics, and risk assessments are continuously refined based on automated analysis results. The crawler systematically evaluates websites using multiple criteria, and the system learns from patterns in the data to improve its ability to distinguish actual abuses from legitimate uses. This feedback loop maintains precision while enabling automated high-speed operation.

The system changes multiple parameters simultaneously to achieve both speed and precision: it computes relevance scores based on domain name similarity, analyzes content keywords, evaluates website popularity metrics, assesses investment levels, and determines damage potential. By changing and weighting multiple parameters rather than relying on a single criterion, the automated system maintains precision in identifying actual abuses while operating at high productivity.