MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641072131 A) filed by Srinivas P; Poornima U Kotehal; and Dr. Lokesh G R on June 10, 2026, for Ai-Based Consumer Behaviour Prediction And Personalized Shopping Recommendation System.
Inventors include Srinivas P; Poornima U Kotehal; and Dr. Lokesh G R.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: AI-Based Consumer Behaviour Prediction and Personalized Shopping Recommendation System: Artificial Intelligence-based Consumer Behaviour Prediction and Personalized Shopping Recommendation System is an intelligent digital commerce framework designed to analyze, predict, and optimize customer purchasing behavior through advanced machine learning, behavioral analytics, and real-time recommendation techniques. The proposed invention integrates multiple data acquisition modules capable of collecting structured and unstructured consumer data including browsing history, purchase frequency, product preferences, search patterns, demographic attributes, social media interactions, feedback sentiments, seasonal buying trends, and real-time engagement metrics from online and offline retail platforms. The collected data is processed through a centralized analytical engine employing deep learning algorithms, collaborative filtering, content-based filtering, natural language processing, predictive analytics, and hybrid recommendation models to generate highly personalized shopping recommendations for individual consumers. The system further incorporates a dynamic consumer profiling mechanism that continuously updates behavioral patterns based on user interactions, enabling adaptive learning and improved prediction accuracy over time. The invention includes a real-time decision support module capable of identifying potential customer interests, predicting future purchases, estimating cart abandonment probability, detecting impulse buying tendencies, and forecasting product demand trends for retailers and e-commerce platforms. A contextual recommendation engine intelligently suggests products, services, discounts, and bundled offers according to customer preferences, location, budget, emotional sentiment, and historical buying behavior, thereby enhancing customer satisfaction and increasing conversion rates. The proposed system also features an automated feedback optimization layer that evaluates recommendation effectiveness and retrains the prediction model to maintain accuracy under changing market conditions. Additionally, the invention supports multilingual interfaces, cloud-based deployment, cross-platform integration, secure transaction processing, and privacy-preserving data handling mechanisms to ensure scalability and compliance with data protection standards.
Disclaimer: Curated by HT Syndication.
