MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641127181 A) filed by Dr Rvr Nri Institute Of Technology Deemed To Be University; Ch. Kiran Babu; Ch. Naga Swapna; H. Sai Yugesh; K. Akhila; P. Mounika; and S. V. Siva Reddy on October 26, 2026, for Vyaptiq Iq: Data-Driven Retail Intelligence For Revenue Growth.
Inventors include Ch. Kiran Babu; Ch. Naga Swapna; H. Sai Yugesh; K. Akhila; P. Mounika; and S. V. Siva Reddy.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: ABSTRACT [0030] A web-based retail intelligence system is disclosed for analyzing structured retail transaction data and presenting evidence-linked business recommendations. The system receives transaction data in a comma-separated values format, validates the input, and calculates key performance indicators including revenue, profit margin percentage, promotional impact and average revenue per user, according to available fields and configured definitions. A statistical anomaly detection module applies a Z-score-based method to identify unusual transaction values for review. A rule-based recommendation module evaluates the calculated indicators and anomaly results, generates prioritized recommendations and presents supporting evidence through a dashboard. A role-based workflow allows business analysts to upload and analyze datasets and decision-makers to review findings and update action status. The system provides a unified workflow for converting transaction records into retail performance indicators, anomaly flags and decision-support information. This project presents Vyaptiq IQ, a web-based retail intelligence platform designed to support data-driven review of retail performance. The application uses a React and Vite interface, Tailwind CSS styling, and Supabase with PostgreSQL for backend and data services. In the reported sample evaluation, an apparel dataset of 1,800 transactions produced a reported margin value of 22.94%, five high-revenue outlier transactions and three prioritized recommendations. These results describe the stated example dataset and do not establish universal accuracy or guaranteed financial improvement. Predictive analytics and real-time data integration are potential future extensions and are not represented as completed functions of the current implementation.
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