MUMBAI, India, Oct. 26 -- Intellectual Property India has published a patent application (202621105405 A) filed by Gunjan Shegade on September 01, 2026, for Digital Twin-Based Application Performance Analysis And Predictive Optimization.
Inventor includes Gunjan Shegade.
The application for the patent was published on October 23, 2026, under issue no. 43/2026.
Abstract: ABSTRACT [505] The advanced digital twin-based application performance analysis system introduces an innovative virtual-replication framework for comprehensive performance characterization that integrates computational simulation protocols with adaptive optimization-determination mechanisms, facilitating real-time performance calculations, dynamic behavioral modeling, and robust optimization-confirmation processes while maintaining seamless enterprise integration and operational accuracy for consistent application performance management. [510] The comprehensive digital twin framework employs adaptive machine-learning algorithms and intuitive simulation protocols, utilizing embedded computational processing arrays and resource-efficient calculation systems to ensure timely performance-deviation identification, enhanced application understanding, and optimal optimization reliability while maintaining continuous synchronization between the virtual replica and the live application environment. [515] The integrated methodology combines multi-dimensional simulation techniques with artificial intelligence-driven pattern-recognition systems, leveraging variable-precision behavioral signals and multi-factor performance indicators to optimize replication procedures and predictive workflows for maximum modeling accuracy and minimal deviation uncertainty during critical enterprise application scenarios. [520] The novel responsive digital twin architecture features engineered high-precision simulation components with specialized behavioral-fingerprinting protocols, enabling complex multi-stage optimization verification while ensuring modeling consistency and predictive-analysis optimization across various application instances without compromising system reliability. [525] The innovative design incorporates strategic validation mechanisms for enhanced performance identification and optimization security, utilizing optimized multi-function systems and adaptive simulation technology to ensure legitimate optimization assignment while maintaining functionality across diverse enterprise environments and deployment scenarios. [530] Implementation methodology emphasizes scalable digital twin integration and efficient simulation sequences, implementing interactive modeling measures and pattern-recognition algorithms to achieve superior performance-deviation determination, enhanced anomaly identification, and unauthorized-modification prevention while ensuring technological simplicity during operational monitoring. [535] The system demonstrates exceptional adaptability through comprehensive integration of performance-identification protocols and intelligent simulation technologies, validating its effectiveness across various multifunctional application configurations and enterprise scenarios while maintaining consistent modeling performance and operational efficiency under diverse workload conditions. [540] The developed framework enables sustainable and reliable optimization of application performance through streamlined, AI-powered digital twin systems, providing significant advantages over traditional reactive-monitoring approaches through variable validation mechanisms, adaptive optimization protocols, and improved performance-deviation assignment while maintaining superior modeling accuracy during critical enterprise performance-management procedures.
Disclaimer: Curated by HT Syndication.
