Intellectual Property India Publishes Patent Application for A Dynamic Algorithmic Optimization Method For High-Speed Data Processing And Decision Making

MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641118159 A) filed by Mrs. K. U. V. Padma; Dr. P. Yasodai; Dr. K. Balamurugan; Dr. E. Rameshkumar; Dr. Nellore Manoj Kumar; Dr. D. Srinivasa Kumar; and Dr. K. Manimekalai on October 01, 2026, for A Dynamic Algorithmic Optimization Method For High-Speed Data Processing And Decision Making.

Inventors include Mrs. K. U. V. Padma; Dr. P. Yasodai; Dr. K. Balamurugan; Dr. E. Rameshkumar; Dr. Nellore Manoj Kumar; Dr. D. Srinivasa Kumar; and Dr. K. Manimekalai.

The application for the patent was published on October 09, 2026, under issue no. 41/2026.

Abstract: A dynamic algorithmic optimization method, and a system embodying it, for high-speed data processing and decision making are disclosed. A processing pipeline performs a data processing and decision task, such as processing streaming or large-scale data and deciding upon it. A monitoring component monitors at runtime the characteristics of the data being processed, volume, rate, distribution, and structure, and the performance of the processing, throughput and latency. An optimization component dynamically optimizes the processing at runtime, selecting from among available processing algorithms the one best suited to the monitored data, configuring parameters, and allocating computational resources, in dependence upon the monitored data, performance, and the system's objectives and constraints, such as maximizing throughput and minimizing latency within resource limits, and adapts them continually as the data and conditions change, applying them without disrupting ongoing processing. The optimization uses a rule-based, heuristic, model-based, or machine-learning method, the last learning from outcomes, in embodiments by reinforcement learning, to improve over time. The system thereby maintains high throughput and low latency across changing data and conditions, producing a concrete technical effect where static fixed-algorithm processing degrades.

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