Intellectual Property India Publishes Patent Application for Campus Placement Prediction System:employing Multi Dimensional Weighted Employability Scoring With Skill Gap Impact Estimation And Resume - Job Description Decomposition

MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641050040 A) filed by Ananya Y; Dr. Patcha Bhujanga Rao; Zoya Fatema; Eklavya Singh Rathore; Mehul Raj Lunia; and Vignesh B on April 20, 2026, for Campus Placement Prediction System:employing Multi Dimensional Weighted Employability Scoring With Skill Gap Impact Estimation And Resume - Job Description Decomposition.

Inventors include Ananya Y; Dr. Patcha Bhujanga Rao; Zoya Fatema; Eklavya Singh Rathore; Mehul Raj Lunia; and Vignesh B.

The application for the patent was published on June 12, 2026, under issue no. 24/2026.

Abstract: 7. ABSTRACT OF THE INVENTION The present invention relates to a campus placement prediction system designed to evaluate student employability through a Multi-Dimensional Weighted Employability Scoring Engine (MW-ESE) that decomposes resume-job description matching into independently scored and weighted dimensions. The system comprises a resume parsing module, a job description parsing module, a Key BERT-based skill phrase extraction layer, a component scoring calculator operating across six dimensions (skills, projects, internships, work experience, academics, and achievements), a configurable weight aggregation module, a gap impact estimation engine, and an improvement simulation engine. The MW-ESE computes per-component scores using semantic embeddings generated by Sentence-BERT and applies domain-adaptive configurable weight profiles to produce a composite employability score. The gap impact estimation mechanism quantifies the weighted impact of each missing resume factor on the composite score and ranks gaps by their impact magnitude. The improvement simulation mechanism recalculates the composite score under hypothetical changes to resume components, generating ranked recommendations for improvements with the highest score impact. The invention provides fully interpretable, actionable employability assessment without requiring historical placement datasets, external APis, or web scraping.

Disclaimer: Curated by HT Syndication.