MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202541112326 A) filed by Christ University, Bangalore, Karnataka, on Nov. 17, 2025, for 'integrated q-commerce optimization engine (iqoe) for real-time assortment, demand forcasting, custom.'
Inventor(s) include Kiran Hemanthraj Muloor; and Lakshmi Shankar Iyer.
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: "Quick Commerce (Q-commerce) has transformed urban retail by enabling near-instant delivery, yet most platforms struggle to balance speed, personalisation, and profitability. The challenges of managing localised product assortments, forecasting volatile demand, predicting Average Order Value (AOV), and retaining customers remain largely unsolved due to fragmented analytical systems. Existing tools operate in silos-focusing on either forecasting, segmentation, or pricing- without integrating these functions into a single, adaptive decision framework. The Integrated Q- commerce Optimisation Engine (IQOE) addresses this gap by introducing a unified Al-driven decision intelligence framework that fuses predictive modelling, optimisation, and reinforcement learning into one continuous system. IQOE employs ensemble learning to identify key drivers of AOV and association rule mining to optimise product assortments across micro-fulfilment centres based on geographic and behavioural patterns. Through RFM-based clustering, it segments customers into loyal, high-value, and at-risk categories, recommending personalised retention strategies to boost repeat purchases and lifetime value. The framework integrates time-series forecasting and deep learning models to predict category-level demand, enabling proactive inventory control and minimising wastage of perishable goods. Its intelligent orchestration layer continuously refines predictions through feedback loops, learning from new data to maintain realtime responsiveness and operational agility. Unlike conventional approaches that isolate forecasting, assortment, and personalisation, IQOE synchronises these tasks into a single selflearning analytical pipeline. This integration allows Q-commerce operators to dynamically optimise inventory, pricing, and engagement strategies, ensuring higher order accuracy, reduced churn, and sustainable profitability. By embedding IQOE into existing delivery ecosystems, Q-commerce firms can achieve measurable improvements in delivery efficiency, assortment precision, and customer loyalty across diverse geographic markets."
Disclaimer: Curated by HT Syndication.
