MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641082521 A) filed by Jangili Srinivasa Rao; M. Saranyaa; G Sushmitha; Md Moyeed Abrar; Jagadhish V S; Senthilkumar C; Logeshwari Dhavamani; Dr. Shaheda Niloufer; Dr. Devi S; Dr. Rahul Soni; Dr D J Samatha Naidu; and P Joel Josephson on July 04, 2026, for Machine Learning-Assisted Bismuth Ferrite Platform For Smart Healthcare, Energy Harvesting, And Environmental Monitoring Applications.
Inventors include Jangili Srinivasa Rao; M. Saranyaa; G Sushmitha; Md Moyeed Abrar; Jagadhish V S; Senthilkumar C; Logeshwari Dhavamani; Dr. Shaheda Niloufer; Dr. Devi S; Dr. Rahul Soni; Dr D J Samatha Naidu; and P Joel Josephson.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: New developments in functional materials, smart sensing technologies, and intelligent monitoring systems have created new opportunities for healthcare, energy harvesting and environmental monitoring. Bismuth ferrite is one of the most promising multiferroics materials due to their unique electrical, magnetic, piezoelectric and sensing properties which widely used in many applications. Yet, for the most part existing solutions still treat these application areas as independent. Physiological health sensing systems are mainly oriented towards the sensors, energy harvesting platforms focus on electrical output efficiencies, and environmental monitoring systems are only designed to collect data. Furthermore, numerous existing solutions rely heavily on raw outputs of sensors and traditional statistical analysis techniques, making it hard to gain more meaningful insights or anticipate future dynamics. Hence, the information users get is often fragmented and prones them to reactive decision-making than rather continuous intelligent assistance. The present invention provides a Machine Learning-Assisted Bismuth Ferrite Platform for Smart Healthcare, Energy Harvesting, and Environmental Monitoring Applications that integrates multifunctional sensing, intelligent feature generation/predictive analytics/decision support into a single framework. Administration The information produced by these sensing elements based on bismuth Ferrite, are continuously registered and interpreted in response to determine a number of indicators, including the exogenous Bio-Signal Response Index (BSRI), Energy Conversion Efficiency Score (ECES) and Environmental quality Indicator (EQI) and Material Performance Stability Factor (MPSF). These signals are processed by a machine learning prediction engine to detect operational patterns, apply future situational awareness and create application specific recommendations. This framework also includes features such as healthcare monitoring, energy optimization, environmental assessment, cloud-based visualization and adaptive feedback loops that improve system performance. The invention described here offers an integrated platform that combines multifunctional material capability along with smart learning and predictive analysis for the application domain while being able to facilitate accurate health monitoring, improved energy harvesting efficiency, reliable environmental assessment and data-driven decision-making. This enables our adaptive architecture to operate more efficiently, facilitating better resource allocation and scalability for future smart sensing and intelligent monitoring applications while ensuring early detection of changing conditions leading to prescriptive decision making leveraging the inherent context-aware optimization capabilities. FIG.1
Disclaimer: Curated by HT Syndication.
