MUMBAI, India, Oct. 26 -- Intellectual Property India has published a patent application (202641122496 A) filed by Sns College Of Technology on October 13, 2026, for Ai-Based Provider-Agnostic Memory System And Method For Adaptive Global Logistics Intelligence..
Inventors include Ms. Priyadharshini S; Dr. P. Sumathi; Siva Ashwanth Raj B; Santhosh Kumar S; Tharun T; Siddhaarth Ps; Srinivasan C; Prithiv Narayanan K; Shamil Hussain M; and Selva Methileash K.
The application for the patent was published on October 23, 2026, under issue no. 43/2026.
Abstract: The invention provides an artificial-intelligence-based, provider-agnostic memory system for adaptive global logistics intelligence. The system maintains persistent operational memory containing logistics information such as shipment and route data, carrier performance, inventory conditions, disruption events, prior artificial- intelligence outputs, operational decisions, and corresponding real-world outcomes. For a current logistics operation, a context-aware retrieval mechanism identifies and retrieves operational memory relevant to the current circumstances. A provider abstraction layer combines the retrieved memory with current logistics information and converts the resulting information into a context format compatible with a selected artificial-intelligence model. The selected model may be a cloud-based model, a locally deployed model, or another compatible artificial-intelligence system. The selected artificial-intelligence model generates predictions, recommendations, classifications, alerts, or other logistics-related outputs. Following execution of a corresponding logistics operation, actual operational outcomes are collected and evaluated against the associated artificial-intelligence outputs and, where applicable, operational decisions or actions. The resulting feedback is used to update, reinforce, supersede, archive, or otherwise modify associated memory records, including their relevance, confidence, or lifecycle status. The updated operational memory can subsequently be retrieved and used by the same or a different artificial-intelligence model. In this manner, the system provides a persistent operational knowledge layer that can retain and reuse logistics knowledge across different artificial-intelligence models and providers without requiring the knowledge to remain within a particular provider's internal memory or infrastructure.
Disclaimer: Curated by HT Syndication.
