MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641088661 A) filed by Malla Reddy Engineering College For Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be University; and Malla Reddy Vishwavidyapeeth Deemed To Be University on July 21, 2026, for Resume – Driven Skill Growth And Role Suggestions.
Inventors include Dr. Y. Madhaveelatha; Mr. D Rama Krishna; Dr. Venkata Konda Reddy Gajjala; Ms P B Maheswari; Ms. Nampally Sushma; Dr. Subhakaran Singh Rajaputra; Ms. Gnaneswari Bodana; and Mr. Santi Satyanarayana.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: The present invention relates to an intelligent resume analysis and career guidance system that integrates natural language processing and machine learning techniques to enable automated evaluation of resumes and generation of personalized skill growth recommendations and job role suggestions. Traditional recruitment and career guidance systems rely on manual resume screening or basic keyword matching techniques that lack contextual understanding, personalization, and real-time feedback. These limitations reduce efficiency in identifying suitable career paths and prevent individuals from understanding the gap between their current skills and industry requirements. The proposed Resume Driven Skill Growth and Role Suggestion System introduces a unified platform that combines advanced resume parsing capabilities with intelligent recommendation mechanisms. The system processes resume data, extracts relevant information such as skills, education, and experience, and evaluates the user profile using machine learning models. A recommendation module then analyzes the extracted data, identifies skill gaps, and generates personalized suggestions for improvement along with suitable job roles aligned to the user’s profile. The system architecture includes resume input processing, natural language-based information extraction, machine learning-based role prediction, skill gap analysis, and a user interface dashboard for displaying recommendations. The platform supports multi-role prediction, adaptive learning suggestions, and continuous profile evaluation based on evolving industry standards.. Furthermore, the system enhances career decision-making, reduces manual effort in resume evaluation, and supports deployment across educational platforms, recruitment systems, and professional development environments. The scalable architecture is designed to integrate with online learning platforms, job portals, and cloud-based systems for efficient real-world implementation.
Disclaimer: Curated by HT Syndication.
