MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641107403 A) filed by Sr University on September 07, 2026, for Ai Powered Personalized Employee Well Being And Job Satisfaction Recommendation System.
Inventors include Ms. Sowmya Muppidi; and Dr Geetha Manoharan.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: ABSTRACT AI Powered Personalized Employee Well Being and Job Satisfaction Recommendation System The present invention relates to an Artificial Intelligence (AI) powered personalized employee well-being and job satisfaction recommendation system designed to continuously assess, analyze, predict, and improve the overall psychological, professional, and workplace well-being of employees. Modern organizations increasingly face challenges related to occupational stress, burnout, low job satisfaction, reduced productivity, employee disengagement, absenteeism, and employee turnover. Conventional employee satisfaction surveys are generally conducted periodically and provide generalized results that may not accurately identify individual employee concerns or provide timely personalized interventions. The proposed invention addresses these limitations through an intelligent system that collects and processes multidimensional employee-related data, including self-reported well- being information, job satisfaction feedback, workload characteristics, work schedules, performance indicators, attendance patterns, engagement levels, workplace interactions, and organizational environmental factors. An AI-based analytical engine applies machine learning, natural language processing, predictive analytics, and recommendation algorithms to identify patterns associated with stress, dissatisfaction, burnout risk, disengagement, and reduced workplace well-being. The system generates individualized well-being scores and job satisfaction profiles while maintaining privacy through secure data processing and access control mechanisms. Based on the identified patterns, the recommendation engine provides personalized suggestions, such as workload adjustment, flexible scheduling, skill development, work- life balance strategies, communication improvement, recognition opportunities, wellness activities, and professional support recommendations. The system may also provide organizational-level anonymized analytics to assist management in identifying systemic workplace concerns. A feedback mechanism continuously evaluates the effectiveness of recommendations and improves future suggestions using adaptive learning algorithms. Thus, the proposed invention provides an intelligent, proactive, privacy-aware, and personalized technological platform for enhancing employee well-being, job satisfaction, engagement, productivity, and organizational sustainability.
Disclaimer: Curated by HT Syndication.
