MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202611060038 A) filed by Kassem Al-Attabi; Prof. Narendra Kumar Joshi; and Arpit Jain on May 12, 2026, for System And Method For Explainable Artificial Intelligence Using Causal Attribution And Feature Relevance Scoring.
Inventors include Kassem Al-Attabi; Prof. Narendra Kumar Joshi; and Arpit Jain.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: A system and method for explainable artificial intelligence using causal attribution and feature relevance scoring is disclosed. The system comprises a feature acquisition controller configured to receive multidimensional operational data from one or more sensing structures, industrial monitoring devices, image acquisition assemblies, or autonomous machine interfaces and to generate normalized feature vectors through signal conditioning and temporal synchronization operations. An inference processing unit generates predictive inference outputs through layered computational propagation operations. A causal attribution processor constructs weighted causal interaction matrices representing probabilistic influence pathways among feature variables and generated predictive outputs using perturbation-response evaluation and dependency propagation operations. A feature relevance scoring processor generates feature contribution distributions through recursive relevance propagation, activation sensitivity estimation, entropy variation analysis, and suppression influence evaluation.
Disclaimer: Curated by HT Syndication.
