Intellectual Property India Publishes Patent Application for Ai-Driven Context-Aware Recommendation System Using Hybrid Deep Learning And Adaptive Knowledge Graph Models

MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641095042 A) filed by Mrs. B Sivasankari; Mrs. Surisetty Pushpa; Dr. B. Uma Maheswari; Mrs. Lavanya R; Mrs. P Deeplaxmi; Mrs. Nuvvula Nikhitha; and Dr. J K Periasamy on August 05, 2026, for Ai-Driven Context-Aware Recommendation System Using Hybrid Deep Learning And Adaptive Knowledge Graph Models.

Inventors include Mrs. B Sivasankari; Mrs. Surisetty Pushpa; Dr. B. Uma Maheswari; Mrs. Lavanya R; Mrs. P Deeplaxmi; Mrs. Nuvvula Nikhitha; and Dr. J K Periasamy.

The application for the patent was published on August 14, 2026, under issue no. 33/2026.

Abstract: The present invention relates to an AI-driven context-aware recommendation system employing hybrid deep learning and adaptive knowledge graph models for generating intelligent, personalized, explainable, and continuously optimized recommendations across diverse application domains. The system comprises a multimodal data acquisition module for collecting heterogeneous contextual information, a contextual intelligence engine for generating unified contextual representations, a hybrid feature learning engine incorporating transformer encoders, graph neural networks, deep neural networks, attention mechanisms, and self-supervised learning models, an adaptive knowledge graph reasoning module for semantic inference, a recommendation orchestration engine for adaptive recommendation generation, a reinforcement learning optimization engine for continuous policy improvement, an explainable artificial intelligence module for transparent recommendation reasoning, a privacy-preserving intelligence layer utilizing federated learning and differential privacy, and a continuous learning framework configured to detect contextual evolution and incrementally update recommendation models without complete retraining. The invention significantly improves recommendation accuracy, contextual awareness, scalability, explainability, semantic reasoning capability, privacy protection, computational efficiency, and long-term user satisfaction while enabling deployment across cloud, edge, enterprise, healthcare, education, finance, industrial automation, smart city, transportation, and e-commerce environments. Accompanied Drawing [FIGS. 1-2]

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