MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202611024041 A) filed by Dr. Puneet Khanna; Prof. Varsha Dattatraya Mhaske; Dr. Deepak Kumar Singh; Pragya Sharma; Dr. Farah Deeba; Dr Minal Bafna; Rashmi Singh Chauha; Dr. Shivraj Singh Gangoliya; Dr. Pradeep Kumar Singh; and Dr. Sunil Kumar, Moradabad, Uttar Pradesh, on Feb. 28, for 'a system and a method to perform real-time context-aware analysis, predictive optimization, and reproducibility certification of multi-modal sensor data in physical experiments.'

Inventor(s) include Dr. Puneet Khanna; Prof. Varsha Dattatraya Mhaske; Dr. Deepak Kumar Singh; Pragya Sharma; Dr. Farah Deeba; Dr Minal Bafna; Rashmi Singh Chauha; Dr. Shivraj Singh Gangoliya; Dr. Pradeep Kumar Singh; and Dr. Sunil Kumar.

The application for the patent was published on May 1, under issue no. 18/2026.

According to the abstract released by the Intellectual Property India: "The present disclosure relates to a system and method for executing real-time context-aware analysis, predictive drift detection, adaptive optimization, and reproducibility certification of multi-modal sensor data in physical experiments. The system consists of a synchronized multi-modal sensor interface, a signal conditioning and digitization unit, a processor connected to memory, and built-in functional modules such as a sensor fusion engine, a contextual intelligence module, a predictive drift analytics module, an adaptive parameter optimization controller, a cross-modal correlation engine, and a reproducibility certification engine. The processor is set up to combine data from different sensors, use AI models to figure out the experimental conditions, find calibration errors before measurement failure, and send closed-loop control signals to dynamically control the experimental actuators. The system also calculates reproducibility indices using statistical and AI-based analysis of experimental data. It then stores improved datasets in a self-learning knowledge repository. The invention improves the accuracy of measurements, reduces the need for human intervention, makes experiments more reliable, and offers a technically advanced way to monitor and control experiments intelligently."

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