Intellectual Property India Publishes Patent Application for System And Methods For Economic Performance Prediction Based On Biomolecular And Biological Metrics

MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202621085042 A) filed by Dr. Pankaj Onkarsingh Baisthakur; Ms. Priyanka Bhagwan Dhopate; Mr. Vinayak Bapurao Alse; Dr. Santosh Balaji Patil; Mr. Nilesh Sudam Gaikwad; Mr. Dipak Pravinrao Kale; Mr. Rajesh Madhavrao Achegawe; Dr. Sanjay Uttamrao Chavan; Mr. Dipak Gopalbuwa Puri; and Ms. Shubhangi Som Bharti on July 10, 2026, for System And Methods For Economic Performance Prediction Based On Biomolecular And Biological Metrics.

Inventors include Dr. Pankaj Onkarsingh Baisthakur; Ms. Priyanka Bhagwan Dhopate; Mr. Vinayak Bapurao Alse; Dr. Santosh Balaji Patil; Mr. Nilesh Sudam Gaikwad; Mr. Dipak Pravinrao Kale; Mr. Rajesh Madhavrao Achegawe; Dr. Sanjay Uttamrao Chavan; Mr. Dipak Gopalbuwa Puri; and Ms. Shubhangi Som Bharti.

The application for the patent was published on August 28, 2026, under issue no. 35/2026.

Abstract: ABSTRACT System and Methods for Economic Performance Prediction Based on Biomolecular and Biological Metrics The present disclosure relates to a system and method for prediction of economic performance based on multi-modal biomolecular, biological, environmental . The system collects biomolecular, physiological, laboratory, wearable, imaging, and environmental data from different heterogeneous sources and aggregates the collected data into a single biological dataset. A biological state reconstruction engine estimates hidden physiological conditions and an adaptive biological digital twin continuously models the evolving biological state of a monitored entity. The system also builds biomolecular interaction networks, conducts causal biological analysis, compensates for environmental variations, estimates prediction confidence and uncertainty, and self-corrects prediction drift to ensure long-term forecasting accuracy. A biological-to-economic performance translation engine translates reconstructed biological states into predictive economic indicators, while a feedback optimization engine continuously improves prediction models using observed biological and economic outcomes. The system also generates explainable prediction reports and optimized intervention recommendations, thus enabling adaptive, reliable, accurate and technically robust economic performance prediction across diverse biological and industrial applications.

Disclaimer: Curated by HT Syndication.