MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641099837 A) filed by Ankam Jagan on August 18, 2026, for Incident-Driven Data Quality Rule Discovery And Forward Propagation During Phased Erp Migration..
Inventor includes Ankam Jagan.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: ABSTRACT [505] The incident-driven data quality framework introduces an innovative rule-discovery architecture for phased enterprise resource planning (ERP) migration environments that integrates real-time incident-analysis protocols with adaptive forward-propagation mechanisms, facilitating continuous data validation, dynamic rule generation, and robust migration confirmation while maintaining seamless integration across distributed enterprise data environments and operational accuracy for consistent phased-migration applications. [510] The comprehensive data quality framework employs adaptive rule-inference algorithms and intuitive incident-classification protocols, utilizing embedded computational reasoning arrays and latency-efficient validation systems to ensure timely data-defect identification, enhanced migration understanding, and optimal rule-propagation reliability while maintaining continuous data-quality monitoring capabilities across heterogeneous ERP modules. [515] The integrated methodology combines multi-phase migration-tracking techniques with artificial intelligence-driven pattern recognition systems, leveraging variable-precision incident signals and multi-factor data-quality indicators to optimize rule-discovery procedures and propagation workflows for maximum data-integrity accuracy and minimal recurrence uncertainty during critical migration applications. [520] The novel responsive rule-discovery architecture features engineered high-precision incident-analysis components with specialized data-lineage fingerprinting protocols, enabling complex multi-stage migration-phase verification while ensuring rule consistency and performance optimization across various ERP instruments and enterprise data domains without compromising system reliability. [525] The innovative design incorporates strategic validation mechanisms for enhanced incident identification and data-quality security, utilizing optimized multi-function rule-discovery systems and adaptive propagation technology to ensure legitimate rule enforcement while maintaining functionality across diverse enterprise migration environments and phased rollout scenarios. [530] Implementation methodology emphasizes scalable migration integration and efficient rule-propagation sequences, implementing interactive monitoring measures and pattern recognition algorithms to achieve superior defect determination, enhanced incident identification, and recurrence prevention while ensuring technological simplicity during phased migration oversight. [535] The system demonstrates exceptional adaptability through comprehensive integration of data-quality identification protocols and intelligent rule-propagation technologies, validating its effectiveness across various multi-phase ERP migration configurations and enterprise scenarios while maintaining consistent monitoring performance and operational efficiency under diverse conditions. [540] The developed framework enables sustainable and reliable execution of phased ERP migrations through streamlined, AI-powered rule-discovery systems, providing significant advantages over traditional data- quality approaches through variable validation mechanisms, adaptive propagation protocols, and improved forward rule-application while maintaining superior data-integrity accuracy during critical migration procedures.
Disclaimer: Curated by HT Syndication.
