MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641082792 A) filed by Dr. Karthik Reddy; Dr. Basavaraj S Kudachimath; and Ashwini V Hiremath on July 06, 2026, for Artificial Intelligence-Based Employee Attrition Prediction And Retention System.
Inventors include Dr. Karthik Reddy; Dr. Basavaraj S Kudachimath; and Ashwini V Hiremath.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: ABSTRACT Artificial Intelligence-Based Employee Attrition Prediction and Retention System The present invention discloses an Artificial Intelligence-Based Employee Attrition Prediction and Retention System for accurately identifying employees at risk of leaving an organization and recommending personalized interventions to improve employee retention. The system integrates employee demographic information, attendance records, payroll information, performance evaluations, promotion history, training participation, project assignments, leave records, engagement surveys, communication behavior, managerial feedback, organizational culture indicators, and other workforce-related datasets obtained from multiple enterprise information systems. The collected data are preprocessed through cleansing, normalization, feature engineering, and transformation before being analyzed using one or more machine learning models trained on historical employee data. The predictive engine generates an attrition probability score for each employee and classifies employees according to predefined risk levels. An explainable artificial intelligence module identifies the key factors responsible for each prediction, thereby improving model transparency and assisting human resource professionals in understanding employee turnover drivers. A recommendation engine subsequently generates personalized retention strategies based on the identified risk factors, including career development opportunities, skill enhancement programs, compensation reviews, workload optimization, mentoring support, flexible work arrangements, recognition initiatives, employee wellness measures, and leadership engagement activities. The invention further incorporates continuous learning mechanisms that update prediction models using newly available organizational data to improve long-term predictive performance. Interactive dashboards provide real-time workforce analytics, departmental attrition trends, organizational risk visualization, intervention effectiveness monitoring, and strategic reporting for human resource decision-makers. The proposed system supports cloud-based, on-premise, and hybrid deployment environments and integrates with existing Human Resource Management Systems and Enterprise Resource Planning platforms. By combining predictive analytics, explainable artificial intelligence, behavioral analysis.
Disclaimer: Curated by HT Syndication.
