MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202641062252 A) filed by Sr University, Warangal, Telangana, on May 16, for 'an uncertainty-calibrated explainable artificial intelligence system and method for reliable healthcare decision support.'
Inventor(s) include Kamal Kumar Sethi; and Dr. Rupesh Kumar Mishra.
The application for the patent was published on May 29, under issue no. 22/2026.
According to the abstract released by the Intellectual Property India: "The present invention discloses an artificial intelligence-based early software quality prediction system that utilizes requirement and design complexity metrics to assess software quality during the initial phases of the software development lifecycle. The system is designed to enable proactive quality assurance by analysing requirement specifications and design artifacts before implementation, thereby identifying potential defects, risks, and maintainability issues at an early stage. The invention includes a data acquisition module that collects requirement documents and design models from engineering tools and repositories. A feature extraction module processes these artifacts using natural language processing and structural analysis techniques to derive complexity metrics such as requirement ambiguity, interdependency, volatility, coupling, cohesion, and modularity. These metrics are then pre-processed and provided to a machine learning engine trained on historical data to predict software quality outcomes. The system generates outputs such as defect probability, risk classification, and maintainability scores, which are delivered through a feedback interface to assist stakeholders in decision-making. It provides actionable recommendations for improving requirement clarity and design structure. Additionally, the system incorporates adaptive learning mechanisms to enhance prediction accuracy over time based on new data and feedback. By enabling early detection of quality issues, the invention reduces development costs, minimizes rework, and improves overall software reliability, offering a scalable and intelligent solution for modern software engineering environments."
Disclaimer: Curated by HT Syndication.
