MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641082642 A) filed by Malleswara Rao Mattam; Dr. K. T. Balaram Padal; Dr. K. Narayana Rao; and Dr. Y. Seetharama Rao on July 04, 2026, for Adaptive Digital Twin-Based Self-Learning Decision Intelligence Architecture For Objective Weight Generation And Reliability Assessment Under Uncertainty.
Inventors include Malleswara Rao Mattam; Dr. K. T. Balaram Padal; Dr. K. Narayana Rao; and Dr. Y. Seetharama Rao.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: The present invention relates to an adaptive digital twin-based self-learning decision intelligence architecture for generating objective criterion weights and assessing decision reliability in uncertain decision environments. The proposed architecture creates a digital twin of a decision matrix and employs a copula-based simulation mechanism to generate multiple realistic decision scenarios while preserving the underlying dependency relationships among decision criteria. These simulated environments enable comprehensive evaluation of the robustness and stability of the weighting process under varying operating conditions. The architecture incorporates a Progressive Evaluation of Criteria (PEC) engine that sequentially integrates Principal Component Analysis (PCA), Entropy weighting, and CRITIC weighting to derive reliable and data-driven criterion weights. To further improve decision quality, the architecture includes a dynamic method orchestration engine that adaptively coordinates multiple weighting methods according to the characteristics of the decision environment. An adaptive self-learning mechanism based on Twin Adaptation Gain (TAG) continuously refines the generated weights and improves the fidelity of the digital twin through iterative learning.
Disclaimer: Curated by HT Syndication.
