MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202621093397 A) filed by Rk University on July 31, 2026, for Hybrid Deep Learning Framework For Predictive Respiratory Health Analytics And Workplace Exposure Assessment.
Inventors include Amit Lathigara; Nirav Bhatt; Paresh Tanna; Parvez Belim; and Chetan Shingadiya.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: Abstract HYBRID DEEP LEARNING FRAMEWORK FOR PREDICTIVE RESPIRATORY HEALTH ANALYTICS AND WORKPLACE EXPOSURE ASSESSMENT A system for exposure-correlated respiratory prediction includes a Physiological Sequence Encoder, a Exposure Sequence Encoder, a Time-Alignment Engine, a Hybrid Fusion Layer, and a Respiratory State Predictor. The system receives respiratory time-series data, repeated lung-function observations, workplace activity periods, and cumulative exposure histories. The acquired observations are checked for quality, source association, and timing before a model-ready representation is generated. The Time-Alignment Engine produces an analytical result and a confidence indication. The Hybrid Fusion Layer applies a stored response criterion and determines whether to release an output, request reacquisition, or require review. The Respiratory State Predictor provides a predicted respiratory trajectory and exposure-attribution result. The system particularly performs separately encoding physiological and exposure sequences and fusing the time-aligned representations for future-state prediction. An event record stores input identifiers, a processing version, the analytical result, and the delivered response to maintain traceability between acquisition, processing, and output. Fig. 1 Dated July 30, 2026 / Mungalpara Jigneshbhai Chhaganbhai IN/PA-2640 Agent for the Applicant
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