MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541125025 A) filed by Sr University, Warangal, Telangana, on Dec. 11, 2025, for 'adaptive twin neural network framework for user-opinion-driven content recommendation.'
Inventor(s) include Dr. Lakshmikanth Paleti; and Dr. Balajee Maram.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "Modern digital platforms face a significant challenge in generating accurate and meaningful content recommendations due to the rapidly growing volume of user interactions and the complexity of multimodal data involved. Traditional recommendation systems often treat all interactions uniformly and fail to recognize the true intent behind user opinions, resulting in content that is misaligned with user preferences and sentiment. This problem becomes more critical as global social media usage continues to rise, with more than 4.8 billion active users worldwide generating over 300 million comments, reactions, and posts every hour, and metropolitan regions such as Amsterdam alone producing an estimated 2.5-3.2 million daily interactions, of which nearly 68% contain explicit or implicit opinion signals. Despite this abundance of meaningful data, conventional models typically exploit less than 40% of opinion-rich signals due to limitations in text understanding, multimodal alignment, and adaptability to preference changes. Existing recommendation architectures rely on single-branch or shallow models that cannot effectively integrate sentiment-bearing inputs with high-dimensional content features. To address these challenges, the present invention introduces an Adaptive Twin Neural Network Framework that processes user opinions and content data through two coordinated neural branches and fuses them through cross-attention and domain-adaptive mechanisms. This framework enhances opinion utilization, ensures sentiment-consistent recommendations, and delivers more accurate, adaptive, and context-aware content rankings across digital platforms."
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