MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641077984 A) filed by Ms. Drakshayini; Ms. T. B Inderakala; and Dr. Santhosh Kumar V on June 24, 2026, for A Machine Learning-Based System For Identifying Skill Gaps And Personalized Upskilling Pathways.
Inventors include Ms. Drakshayini; Ms. T. B Inderakala; and Dr. Santhosh Kumar V.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: A Machine Learning-Based System for Identifying Skill Gaps and Personalized Upskilling Pathways The present invention relates to a machine learning-based system and method for identifying skill gaps and generating personalized upskilling pathways for individuals across educational, professional, and organizational environments. The invention utilizes advanced artificial intelligence, machine learning algorithms, data analytics, and competency mapping techniques to continuously evaluate an individual's existing knowledge, technical capabilities, behavioral competencies, performance metrics, educational background, job requirements, industry trends, and career aspirations. The system collects and processes data from multiple sources, including learning management systems, professional profiles, assessment platforms, workplace performance records, certification databases, and labor market intelligence repositories. Through predictive analytics and intelligent pattern recognition, the invention identifies current and future skill deficiencies by comparing individual competency profiles against dynamic industry benchmarks and role-specific requirements. A skill gap analysis engine generates quantitative and qualitative insights regarding proficiency levels, emerging competency needs, and potential career advancement opportunities. Based on the identified gaps, a recommendation engine constructs personalized upskilling pathways tailored to the user's learning preferences, experience level, career objectives, available time, and organizational requirements. The pathway generation module employs adaptive learning models, reinforcement learning techniques, and recommendation algorithms to suggest optimized sequences of courses, certifications, projects, mentorship opportunities, practical exercises, and experiential learning activities. The system continuously monitors user progress through real-time performance tracking, assessment outcomes, engagement metrics, and feedback mechanisms, enabling dynamic adjustment of learning pathways to maximize skill acquisition efficiency and knowledge retention. Furthermore, the invention incorporates workforce demand forecasting models that analyze market trends, technological advancements, and employment patterns to recommend future-ready competencies and proactively address emerging skill shortages. The system may provide interactive dashboards, competency heat maps, predictive career progression insights, and organizational talent analytics to support decision-making by learners, employers, educational institutions, and workforce development agencies. By integrating automated skill assessment, intelligent recommendation generation, adaptive learning optimization, and predictive workforce intelligence within a unified platform, the invention significantly improves the accuracy, scalability, and effectiveness of skill development initiatives. The proposed system enables data-driven talent enhancement, reduces skill mismatches, accelerates professional growth, and supports continuous lifelong learning in rapidly evolving educational and employment ecosystems.
Disclaimer: Curated by HT Syndication.
