MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641069502 A) filed by Peri College Of Arts And Science on June 03, 2026, for Ai Driven Contextual Decision Intelligence System With Predictive Learning.
Inventors include Mohanraj S; Mohan U; Srinick R; Ragavi D; Denish Kumar T; and Ashwin M.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: ABSTRACT The present invention relates to an intelligent and adaptive computational framework titled "Context-Aware Smart Decision Support System Using Deep Learning", designed to provide real-time, personalized, and autonomous decision assistance across multiple application domains including healthcare, smart manufacturing, education, transportation, industrial automation, agriculture, and enterprise management. The proposed system integrates advanced deep learning architectures, contextual intelligence, multimodal data fusion, and predictive analytics to enhance the accuracy, efficiency, and reliability of decision-making processes under dynamic and uncertain environments. The invention introduces a novel context-awareness engine capable of continuously capturing and interpreting environmental, behavioral, operational, temporal, and user-specific contextual parameters from heterogeneous data sources such as loT sensors, wearable devices, cloud platforms, surveillance systems, mobile devices, enterprise databases, and real-time streaming networks. The acquired contextual data is processed using a hybrid deep neural framework incorporating transformer networks, recurrent neural networks (RNN), convolutional neural networks (CNN), reinforcement learning modules, and attention-based learning mechanisms to generate adaptive recommendations and predictive insights. A key novelty of the invention lies in its self-evolving cognitive decision architecture, which dynamically updates its inference models based on changing contextual patterns, historical decisions, risk probabilities, and user interaction feedback without requiring complete retraining. The system further incorporates an explainable artificial intelligence (XAI) layer that provides transparent reasoning and confidence scoring for generated recommendations, thereby improving trustworthiness and interpretability of AI-driven decisions. The proposed invention additionally employs a federated and privacy-preserving learning framework enabling secure decentralized model training without exposing sensitive user data. An intelligent priority optimization module evaluates multiple decision alternatives using real-time constraints, resource availability, urgency factors, and predictive outcomes to provide optimal decision strategies. The system also supports anomaly detection and proactive alert generation through continuous behavioral monitoring and predictive risk assessment. The invention significantly improves contextual understanding, autonomous adaptation, predictive decision accuracy, and operational efficiency compared to conventional rule-based or static decision support systems. The proposed smart decision support platform can be deployed in cloud, edge, or hybrid computing environments, making it highly scalable, secure, and suitable for next-generation intelligent automation ecosystems. surveillance
Disclaimer: Curated by HT Syndication.
