MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641074934 A) filed by Dr. G. Santhoshkumar; Dr. Anita M Patil-Nikam; Puspanjali Burada; Dr Jiju Mathew John; Dr. Gincy Jiju Mathew; Arulkumar Rv; Dr P Suresh; Shruti Sharma; Renuga K; Urlam Pranita; Boomika P; and Gayathri V on June 17, 2026, for Ai-Powered Personalized Recommendation And Sales Prediction Engine For E-Commerce Platforms.
Inventors include Dr. G. Santhoshkumar; Dr. Anita M Patil-Nikam; Puspanjali Burada; Dr Jiju Mathew John; Dr. Gincy Jiju Mathew; Arulkumar Rv; Dr P Suresh; Shruti Sharma; Renuga K; Urlam Pranita; Boomika P; and Gayathri V.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: AI-Powered Personalized Recommendation and Sales Prediction Engine for E-Commerce Platforms is the proposed invention. The proposed invention relates to a AI-powered personalized recommendation and sales prediction engine for e-commerce platforms utilizing a Transformer-based deep learning architecture. The system is designed to analyze large-scale customer interaction data, product information, transaction records, and market-related factors to deliver personalized product recommendations and accurate sales forecasts within a unified framework. The recommendation module employs self-attention mechanisms to capture complex relationships between customer preferences, browsing behavior, search patterns, and purchasing activities, enabling the generation of highly relevant product suggestions. Simultaneously, the sales prediction module processes historical sales data, seasonal trends, pricing variations, promotional activities, and demand fluctuations to forecast future sales performance with enhanced precision. The system incorporates a continuous learning mechanism that updates model parameters using real-time customer feedback and transaction outcomes, thereby improving recommendation relevance and forecasting accuracy over time. Additionally, the framework supports inventory optimization, demand planning, marketing strategy enhancement, and operational decision-making through data- driven insights.
Disclaimer: Curated by HT Syndication.
