MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641125227 A) filed by Dr. Manjunatha M; Dr. Vasanthakumar M; and Mrs. Soniya V on October 21, 2026, for An Ai-Enabled Library Visitor Flow Analysis And Behaviour-Based Resource Utilisation Monitoring System.
Inventors include Dr. Manjunatha M; Dr. Vasanthakumar M; and Mrs. Soniya V.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: ABSTRACT [505] The present invention introduces an advanced AI-enabled library visitor flow analysis and behaviour-based resource utilisation monitoring system that integrates real-time occupancy sensing with intelligent movement-pattern analysis and adaptive resource-usage determination mechanisms, facilitating dynamic visitor-flow calculations, behaviour-driven utilisation optimisation, and robust operational confirmation while maintaining seamless library-management integration and operational accuracy for consistent learning-resource applications. [510] The comprehensive monitoring framework employs adaptive machine-learning algorithms and intuitive sensing protocols, utilising embedded computational processing arrays and energy-efficient analytics systems to ensure timely occupancy identification, enhanced behavioural understanding, and optimal resource reliability while maintaining continuous flow-monitoring capabilities across library zones. [515] The integrated methodology combines multi-sensor flow-tracking techniques with artificial-intelligence-driven pattern recognition systems, leveraging variable-precision movement signals and multi-factor utilisation indicators to optimise resource-allocation procedures and service workflows for maximum operational accuracy and minimal utilisation uncertainty during critical library operations. [520] The novel responsive monitoring architecture features engineered high-precision sensing components with specialised behavioural-fingerprinting protocols, enabling complex multi-stage flow verification while ensuring measurement consistency and performance optimisation across various library configurations without compromising visitor privacy or system reliability. [525] The innovative design incorporates strategic validation mechanisms for enhanced behavioural identification and operational security, utilising optimised multi-function systems and adaptive analytics technology to ensure legitimate resource-usage attribution while maintaining functionality across diverse library environments and service scenarios. [530] Implementation methodology emphasises scalable analytics integration and efficient monitoring sequences, implementing interactive sensing measures and pattern- recognition algorithms to achieve superior utilisation determination, enhanced flow identification, and inefficiency prevention while ensuring technological simplicity during library monitoring. [535] The system demonstrates exceptional adaptability through comprehensive integration of behavioural identification protocols and intelligent monitoring technologies, validating its effectiveness across various library-service configurations and operational scenarios while maintaining consistent monitoring performance and operational efficiency under diverse conditions. [540] The developed framework enables sustainable and reliable management of library resources through streamlined, AI-powered monitoring systems, providing significant advantages over traditional manual approaches through adaptive validation mechanisms, behaviour-based identification protocols, and improved utilisation allocation while maintaining superior monitoring accuracy during critical learning- resource operations.
Disclaimer: Curated by HT Syndication.
