MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641078367 A) filed by Cmr Engineering College, Kandlakoyav, Medchal Road, Hyderabad, Medchal Malkajgiri, Telangana-, India. on June 25, 2026, for Context-Aware Artificial Intelligence Framework For Personalized Healthcare Monitoring.
Inventors include Dr. Rajesh Tiwari, Professor, Computer Science And Engineering, Cmr Engineering College, Kandlakoya, Hydeabad-; Mr. Naveen Kumar Mamidala, Assistant Professor, Computer; Science And Engineering Data Science, Cmr Engineering College, Kandlakoya, Hydeabad-; Mrs. M. Srikala, Assistant Professor, Computer Science And; Dr. A. Vijendhar, Associate Professor, Computer Science And; Mrs. T. Bhavya, Assistant Professor, Computer Science And; Mr. Ch. Aravind, Assistant Professor, Computer Science And; and Mr. K. Sri Rekha, Assistant Professor, Electronics And Communication Engineering, Cmr Engineering College, Kandlakoya, Hydeabad-.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention discloses a Context-Aware Artificial Intelligence Framework for Personalized Healthcare Monitoring for intelligent and adaptive healthcare services. The framework integrates wearable devices, Internet of Medical Things (IoMT) sensors, cloud computing, and artificial intelligence technologies to continuously monitor patient health conditions and provide personalized healthcare recommendations. Physiological data including heart rate, blood pressure, body temperature, oxygen saturation, glucose levels, sleep patterns, and physical activity are collected from wearable sensors and medical devices. The acquired data undergo preprocessing operations such as cleaning, normalization, feature extraction, and integration to improve data quality and analytical performance. Contextual information including user activity, location, environmental conditions, lifestyle habits, medical history, and medication records is further incorporated to enable context-aware healthcare analysis. Machine learning algorithms including Random Forest, Support Vector Machine (SVM), XGBoost, Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) networks are employed for disease risk prediction, anomaly detection, and health assessment. Based on prediction results, the system generates personalized health alerts, medication reminders, dietary recommendations, and exercise suggestions. Healthcare professionals can remotely monitor patients through real-time dashboards and emergency notification systems. The framework supports cloud and edge computing architectures for scalable and low-latency deployment. The proposed invention enhances healthcare quality, enables early disease detection, supports remote patient care, improves clinical decision-making, and promotes personalized healthcare services in hospitals, smart homes, telemedicine platforms, and elderly care environments.
Disclaimer: Curated by HT Syndication.
