Intellectual Property India Publishes Patent Application for Adaptive Edge-Based Offline Medical Diagnostic System For Resource-Constrained Healthcare Environments And Method Thereof

MUMBAI, India, Sept. 7 -- Intellectual Property India has published a patent application (202641100001 A) filed by Dayananda Sagar University on August 19, 2026, for Adaptive Edge-Based Offline Medical Diagnostic System For Resource-Constrained Healthcare Environments And Method Thereof.

Inventors include Mithaguru; and A. Vegi Fernando.

The application for the patent was published on September 04, 2026, under issue no. 36/2026.

Abstract: Title: Adaptive Edge-Based Offline Medical Diagnostic System for Resource-Constrained Healthcare Environments and Method Thereof The present invention relates to an adaptive edge-based offline medical diagnostic system (100) and method for artificial intelligence-assisted chest disease diagnosis in resource-constrained healthcare environments. The system comprises an image acquisition (101) and preprocessing module (102), an edge computing unit, an Autonomous Resource Orchestration Controller (AROC) (109), an adaptive inference engine (104), a confidence calibration module (105), an explainable artificial intelligence module (106), a diagnostic report generation module (107), a secure local storage framework (108), a Priority-Based Adaptive Synchronization Mechanism (PASM) (110) and a Rural-Aware Federated Aggregation Mechanism (RFAM) (111). The AROC dynamically optimizes computational resources and inference execution according to processor utilization, memory availability, battery status, storage capacity, thermal conditions and network quality. The system performs local diagnosis without continuous internet connectivity, generates confidence-aware and explainable diagnostic outputs and securely stores reports for offline access. Privacy-preserving federated learning employs resource-aware client selection, encrypted model updates, secure aggregation and adaptive model synchronization without transmitting raw patient data, thereby enabling scalable, communication-efficient and privacy-preserving collaborative diagnostic intelligence across distributed healthcare institutions.

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