MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202621043045 A) filed by Dr Meena Rupkumar Pagarwar; Prof. Anna Eric Brown; Prof. Shailesh Panchal; Dr. Sindhu. R; Balraj Sharma; Manisha Agrahari; Abhilasha Sunilrao Moon; Mrs. Pinky Sharma; Ms. Geetanjali Miglani; and Prof. Ginu Anie Joseph, Nagpur, Maharashtra, on April 3, for 'system and method for real-time nursing surveillance and predictive deterioration detection using multimodal adaptive intelligence framework.'

Inventor(s) include Dr Meena Rupkumar Pagarwar; Prof. Anna Eric Brown; Prof. Shailesh Panchal; Dr. Sindhu. R; Balraj Sharma; Manisha Agrahari; Abhilasha Sunilrao Moon; Mrs. Pinky Sharma; Ms. Geetanjali Miglani; and Prof. Ginu Anie Joseph.

The application for the patent was published on May 29, under issue no. 22/2026.

According to the abstract released by the Intellectual Property India: "System and Method for Real-Time Nursing Surveillance and Predictive Deterioration Detection Using Multimodal Adaptive Intelligence Framework The current disclosure pertains to a system and method for real-time nursing surveillance aimed at the early detection of patient deterioration. The system has a multimodal sensor acquisition unit that collects physiological, behavioral, and environmental data, and a data fusion and normalization engine that processes inputs that are not the same. A digital twin generation unit makes a moving virtual model of the patient to mimic physiological conditions. A predictive deterioration signature engine looks at patterns over time to find early signs of risk. A neuromorphic decision reasoning engine offers context-aware clinical interpretation, bolstered by a module that takes into account the patient's history and the environment. There is also an alert and escalation management unit and a cognitive nursing assistance interface that gives useful information. Federated learning also lets you improve models while keeping your privacy, and blockchain-based systems make sure that data is kept safe. This makes it possible to proactively, intelligently, and on a large scale monitor patients more closely and intervene in a timely manner."

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