Intellectual Property India Publishes Patent Application for 'Artificial Intelligence Approach To Estimating Brand Loyalty Through Consumer Behavior' Filed by Dr. Abhijiti Ratnakar Mancharkar; Dr. Manoj Sham Kulkarni; Dr. Arvinder Kour Mehta; Mr. P. Mohanraj; Dr. Suyog Upasani; Umayal AR; and Avinash Kumar Vivekanand Mishra

MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202521119086 A) filed by Dr. Abhijiti Ratnakar Mancharkar; Dr. Manoj Sham Kulkarni; Dr. Arvinder Kour Mehta; Mr. P. Mohanraj; Dr. Suyog Upasani; Umayal AR; and Avinash Kumar Vivekanand Mishra, Pune, Maharashtra, on Nov. 28, 2025, for 'artificial intelligence approach to estimating brand loyalty through consumer behavior.'

Inventor(s) include Dr. Abhijiti Ratnakar Mancharkar; Dr. Manoj Sham Kulkarni; Dr. Arvinder Kour Mehta; Mr. P. Mohanraj; Dr. Suyog Upasani; Umayal AR; and Avinash Kumar Vivekanand Mishra.

The application for the patent was published on Jan. 9, under issue no. 02/2026.

According to the abstract released by the Intellectual Property India: "Artificial Intelligence Approach to Estimating Brand Loyalty through Consumer Behavior Artificial Intelligence (AI)-based system for accurately estimating brand loyalty by analyzing multidimensional consumer behavior patterns across physical and digital channels. The system leverages machine learning, deep learning, behavioral analytics, and natural language processing to evaluate indicators such as purchase frequency, product preferences, engagement history, sentiment polarity, social media interactions, and psychographic attributes. Traditional brand loyalty measurement techniques rely heavily on surveys, limited datasets, and episodic observations, often resulting in biased or incomplete insights. The proposed AI framework provides a continuous, automated, and data-driven mechanism capable of detecting subtle behavioral signals that correlate with long-term brand commitment. It employs hybrid AI models combining supervised, unsupervised, and reinforcement learning to segment consumers, forecast loyalty trajectories, and assign dynamic loyalty scores. The system integrates multi-source data streams-including e-commerce logs, point-of-sale records, customer relationship management (CRM) data, mobile app usage, and online review text-into a unified analytics pipeline. By applying ensemble predictive models and explainable AI techniques, the invention ensures transparency in loyalty estimation while offering actionable insights for marketing optimization."

Disclaimer: Curated by HT Syndication.