MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641086816 A) filed by Dr. M. Jaganathan; G Santhoshi; Dr. Vijayalakshmi J; Dr. Shailendra Yadav; Dr. Sandip Subrao Kanase; Dr. A. Jafersadhiq; Dr. Pendurthy Anthony Sunny Dayal; Marydayana A; Dr. Joydeep Banerjee; Mr. Ajay Kumar Baghel; S. Swarnalatha; and Goli Kiranmai on July 15, 2026, for Machine Learning-Based Context-Aware Consumer Purchase Intention Prediction And Sales Optimization System.
Inventors include Dr. M. Jaganathan; G Santhoshi; Dr. Vijayalakshmi J; Dr. Shailendra Yadav; Dr. Sandip Subrao Kanase; Dr. A. Jafersadhiq; Dr. Pendurthy Anthony Sunny Dayal; Marydayana A; Dr. Joydeep Banerjee; Mr. Ajay Kumar Baghel; S. Swarnalatha; and Goli Kiranmai.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: The unprecedented boom of e-commerce, m-commerce, and digital market spaces has altered the manner in which consumers learn about products and decide to make purchases. Every business now has huge customer data from websites, mobile applications, loyalty programs & digital payment systems to analyze the shopping behavior. Though many consumer analytics and recommendation systems exist, most current solutions are primarily built on purchase history, demographic data or only direct browsing statistics. Such methods often ignore the bigger picture in which customers make decisions, including browsing patterns, product comparisons, levels of engagement, session behavior and devices used to access a site, which can change customer behaviours. Consequently, enterprises might get limited visibility on data hence may predict poor purchase decisions, unsuccessful marketing campaigns, lost sales and ineffective inventory planning. How other systems require manual updates over and over again to stay relevant as consumers behaviours move over time. The present invention relates to a Machine Learning-Based Context-Aware Consumer Purchase Intention Prediction and Sales Optimization System that integrates intelligent data collection, behavioural feature engineering, machine learning, and business decision support into a cohesive framework. It continuously collects the customer interactions from various retail channels and converts them into actionable indices like Consumer Purchase Intention Score (CPIS) for all customers, Customer Engagement Index (CEI) for selected customers, Product Interest Level PIL across future products, Shopping Behavior Consistency Factor (SBCF) as an indicator of brand loyalty and Dynamic Purchase Probability Index DPPI which indicates the likelihood of purchasing a product against a defined target. By means of a machine learning prediction engine, these signals are scanned to find out purchase intention, identify changing buying behavior and develop personalized recommendations and sales optimization tactics. It also adds cloud-based monitoring, enables business intelligence dashboards and has a self-learning process that builds on past interactions from customers and improves prediction accuracy over time. The current invention would allow companies to have a better understanding of the customer intention & help them carry out better marketing campaigns, sustained sales conversion, optimal inventory planning, note down the nature of customer engagement and support sound business decision across modern retail and e-commerce scenarios.
Disclaimer: Curated by HT Syndication.
