MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123875 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy College Of Engineering And Technology; Malla Reddy Vishwavidyapeeth; Malla Reddy University; and Malla Reddy Engineering College For Women, Medchal-Malkajgiri, Telangana, on Dec. 9, 2025, for 'ai based consumer feedback analyzer for product development teams.'
Inventor(s) include Dr. Mandala Sreenivas; Ms. K. Manasa; Mrs. T. Pavani Prabha; Mr. N. Vasishta; and Dr. P. Girija Sri.
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: "The present invention discloses a specialized artificial intelligence system configured to aggregate, interpret, and convert unstructured consumer feedback into actionable technical specifications for product development and engineering teams. In the current digital commerce landscape, user feedback is generated in massive volumes across disparate platforms such as e-commerce reviews, social media threads, customer support tickets, and app store ratings. Product development teams currently struggle to manually sift through this noise to identify genuine design flaws or feature requests, often relying on filtered summaries from marketing departments that lack technical nuance. The invention overcomes this limitation by deploying a unified data ingestion engine coupled with a deep-learning natural language processing (NLP) model. The system utilizes an Aspect-Based Sentiment Analysis (ABSA) framework designed to identify specific product components within user text. Unlike generic sentiment analysis which merely labels a review as "positive" or "negative," the disclosed invention parses complex sentences to attribute sentiment to specific engineering attributes. For example, in the sentence "The phone looks great, but the battery drains in two hours," the system isolates "battery" as the component and "drain" as the defect, while ignoring the positive sentiment regarding aesthetics which is irrelevant to the thermal engineering team. This data is then mapped directly to the product's Bill of Materials (BOM) or feature list. Furthermore, the invention integrates a dynamic prioritization matrix that weighs feedback based on the credibility of the source, the frequency of the reported issue, and the severity of the sentiment. This processed intelligence is visualized on a real-time dashboard that integrates with standard Product Lifecycle Management (PLM) tools. This allows engineers to see a "heat map" of the product, identifying exactly which subsystems are causing consumer dissatisfaction, thereby reducing the time required for root cause analysis and accelerating the iteration cycle for the next product version."
Disclaimer: Curated by HT Syndication.
