MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541122636 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy University; Malla Reddy Engineering College For Women; Malla Reddy College Of Engineering And Technology; and Malla Reddy Vishwavidyapeeth, Medchal-Malkajgiri, Telangana, on Dec. 5, 2025, for 'employee productivity analyzer using behavioural analytics framework.'
Inventor(s) include Dr. K. Maddileti; Dr. G. Mohan Ram; Mr. Votte Rajashekhar; Dr A Nagaraju; and Dr. Kunchala Little Flower.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The current product reveals a statistical model of examining worker productivity by the consequences of behavioral signs based on computer-activity, workplace engagement, and performance statistics. Formal forms of evaluation are based on judgemental methods of evaluation and fixed KPIs which become inappropriate and produce patchy over biased evaluation that cannot measure real time reflection of behavioural effects. The system proposed combines structured and unstructured data patterns, such as timelines of work on a project, trade habits regarding task accomplishment, frequency of communication, focus time intervals, collaboration indices, and so forth, and tries to form a dynamic productivity portrait of an individual employee. Machine-learning predictions measure the characteristics of behavior in terms of engagement, effectiveness, responsiveness, and collaboration. Correlation engine identifies the most effective drivers of the productivity in certain organizational settings through a linking of behavioral signs with performance outcomes. Predictive modules predict performance trend in the future and identify the early signs of a burn out, disengagement or overload. The invention can assist organizations by offering an insight into workforce effectiveness in a real-time manner through explainable and ongoing analytics without statistical unfairness, lack of transparency, or defiance to rules regarding data-privacy regulations."
Disclaimer: Curated by HT Syndication.
