MUMBAI, India, Oct. 26 -- Intellectual Property India has published a patent application (202621105403 A) filed by Gunjan Shegade on September 01, 2026, for Predictive Detection Of Architectural Performance Bottlenecks Using Dependency Graphs And Runtime Telemetry.
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 predictive bottleneck-detection system introduces an innovative dependency-graph-based framework for comprehensive architectural performance characterization that integrates computational graph-analysis protocols with adaptive bottleneck-determination mechanisms, facilitating real-time dependency calculations, dynamic runtime-telemetry correlation, and robust bottleneck-confirmation processes while maintaining seamless enterprise integration and operational accuracy for consistent architectural performance management. [510] The comprehensive dependency-graph framework employs adaptive machine-learning algorithms and intuitive graph-traversal protocols, utilizing embedded computational processing arrays and resource-efficient calculation systems to ensure timely bottleneck-pattern identification, enhanced architectural understanding, and optimal detection reliability while maintaining continuous correlation between service- dependency structures and live runtime telemetry. [515] The integrated methodology combines multi-dimensional graph-analysis techniques with artificial intelligence- driven pattern-recognition systems, leveraging variable-precision telemetry signals and multi-factor dependency indicators to optimize detection procedures and predictive workflows for maximum bottleneck accuracy and minimal false-positive uncertainty during critical enterprise architectural scenarios. [520] The novel responsive dependency-graph architecture features engineered high-precision graph-traversal components with specialized bottleneck-fingerprinting protocols, enabling complex multi-stage detection verification while ensuring correlation consistency and predictive-analysis optimization across various service-topology instances without compromising system reliability. [525] The innovative design incorporates strategic validation mechanisms for enhanced bottleneck identification and detection security, utilizing optimized multi-function systems and adaptive graph-monitoring technology to ensure legitimate detection assignment while maintaining functionality across diverse enterprise environments and architectural topologies. [530] Implementation methodology emphasizes scalable dependency-graph integration and efficient traversal sequences, implementing interactive telemetry-correlation measures and pattern-recognition algorithms to achieve superior bottleneck-location determination, enhanced root-cause identification, and unauthorized-modification prevention while ensuring technological simplicity during architectural monitoring. [535] The system demonstrates exceptional adaptability through comprehensive integration of bottleneck-identification protocols and intelligent graph-analysis technologies, validating its effectiveness across various multifunctional architectural configurations and enterprise scenarios while maintaining consistent detection performance and operational efficiency under diverse topology conditions. [540] The developed framework enables sustainable and reliable detection of architectural bottlenecks through streamlined, AI-powered dependency-graph systems, providing significant advantages over traditional single- service monitoring approaches through variable validation mechanisms, adaptive detection protocols, and improved bottleneck-location assignment while maintaining superior correlation accuracy during critical enterprise architectural-analysis procedures.
Disclaimer: Curated by HT Syndication.
