Intellectual Property India Publishes Patent Application for 'System And Method For Intelligent Disease Prediction And Diagnosis Using Deep Learning-Based Patient Data Analytics' Filed by Dr M. G. Dinesh; J. Oburadha; Dr. B. Sharmila; Negalurmath Vinayakaswami; Dr Ebby Darney P; Sincy Elezebeth K; S. Sathya; Kirubhanandan CG; G. D. Binola; and G. Akila

MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202641064271 A) filed by Dr M. G. Dinesh; J. Oburadha; Dr. B. Sharmila; Negalurmath Vinayakaswami; Dr Ebby Darney P; Sincy Elezebeth K; S. Sathya; Kirubhanandan Cg; G. D. Binola; and G. Akila, Kandhe Gounden Chavadi, Tamil Nadu, on May 21, for 'system and method for intelligent disease prediction and diagnosis using deep learning-based patient data analytics.'

Inventor(s) include Dr M. G. Dinesh; J. Oburadha; Dr. B. Sharmila; Negalurmath Vinayakaswami; Dr Ebby Darney P; Sincy Elezebeth K; S. Sathya; Kirubhanandan CG; G. D. Binola; and G. Akila.

The application for the patent was published on May 29, under issue no. 22/2026.

According to the abstract released by the Intellectual Property India: "The invention discloses a system and method for intelligent disease prediction and diagnosis using deep learning-based patient data analytics. The system integrates multiple modules, including data acquisition, preprocessing, deep learning, diagnostic inference, and user interface dashboard. Patient data from diverse sources such as electronic health records, laboratory tests, wearable sensors, and genomic repositories is securely ingested and standardized. Advanced deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer models, analyze the data to identify disease patterns and risk factors. Diagnostic inference aligns predictive outputs with clinical guidelines, ensuring interpretability and trustworthiness. Results are presented through a user interface dashboard, enabling clinicians to access risk scores, probable diagnoses, and suggested actions. The system incorporates adaptive learning mechanisms for continuous improvement and employs robust security protocols to protect sensitive healthcare data. This invention addresses limitations of prior art by providing a scalable, accurate, and secure framework for predictive healthcare and diagnostic decision support."

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