MUMBAI, India, Feb. 27 -- Intellectual Property India has published a patent application (202641017667 A) filed by P Chitra; and Dr. P. Jayanthi, Chennai, Tamil Nadu, on Feb. 17, for 'a cognitive-aware software architecture for dynamically adaptive digital workflows based on real-time human cognitive state estimation.'
Inventor(s) include Dr. P. Chitra; and Dr. P. Jayanthi.
The application for the patent was published on Feb. 27, under issue no. 09/2026.
According to the abstract released by the Intellectual Property India: "The present invention discloses a cognitive-aware software architecture for dynamically adapting digital workflows based on real-time estimation of human cognitive states derived from user-system interaction signals. Conventional digital platforms typically rely on static or rule-based workflows that do not account for variations in user cognitive capacity, attention, or fatigue, often leading to increased errors, inefficiency, and reduced accessibility. The disclosed system addresses these limitations by enabling cognition-driven workflow adaptation through a technically implemented software architecture. The system includes an interaction signal capture module configured to collect real-time, implicit interaction signals such as keystroke timing patterns, mouse movement trajectories, click frequency, hesitation intervals, navigation depth, and backtracking behavior. A cognitive state inference engine processes these signals to generate a multidimensional cognitive state vector representing cognitive load, attention stability, and cognitive fatigue. The inference engine employs signal normalization, temporal aggregation, and adaptive weighting mechanisms to ensure continuous and reliable cognitive state estimation without the use of physiological or biometric sensors. Based on the inferred cognitive state vector, a workflow adaptation controller dynamically restructures digital workflows by modifying task ordering, interface element density, interaction complexity, and notification scheduling in real time without interrupting ongoing tasks. A cognitive feedback learning module further learns user-specific cognitive patterns over time and updates adaptation rules dynamically. An explainability and audit module logs cognitive state transitions and workflow adaptation decisions to support transparency and compliance. The invention integrates with existing software systems and produces measurable technical effects, including reduced cognitive overload, improved task accuracy, enhanced accessibility, and increased workflow efficiency."
Disclaimer: Curated by HT Syndication.
