MUMBAI, India, Feb. 6 -- Intellectual Property India has published a patent application (202631008210 A) filed by Dr. Vidya Jha, Ranchi, Jharkhand, on Jan. 28, for 'predictive analytics system for employee performance and retention.'
Inventor(s) include Dr. Priyanka Pandey; Dr. Gagan Deep Chadha; Dr. Ashok Kumar Asthana; and Dr. Abhishek Chauhan.
The application for the patent was published on Feb. 6, under issue no. 06/2026.
According to the abstract released by the Intellectual Property India: "The current innovation is a Predictive Analytics System for Staff Performance and Retention. This system makes use of multi-dimensional workforce data and machine learning algorithms in order to evaluate staff productivity and anticipate issues with employee turnover. Performance measurements, attendance records, behavioral indications, feedback patterns, and HR records from the past are all included in the full data warehouse that the solution provides. A number of powerful prediction models, including sentiment analysis, clustering, classification, and regression, are utilized by the system in order to supply you with the most recent information, risk levels, and performance evaluations. A decision-support dashboard that presents these facts includes a variety of components, including interactive charts, trends, alarms, and suggestions, as well as individualized advice. Once this is accomplished, managers of human resources are able to make decisions that are both proactive and analytical based on the data. Through the use of this approach, continual staff development is facilitated, retention methods are improved, and evaluation bias is significantly mitigated. As a result of the solution's ability to translate raw HR data into exact predictive intelligence, both efficiency and production have been significantly improved. Keywords: Hybrid AI Model, Flood Forecasting, Physics-Informed Neural Network (PINN), Convolutional Vision Transformer (CViT), Shallow Water Equations (SWE), Real-Time Simulation, Hydrodynamic Modelling, Spatio-Temporal Prediction."
Disclaimer: Curated by HT Syndication.
