MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641127078 A) filed by Dr. Srinivasu Polinati; Dr. Sharanamma M Hugar; Mrs. Shilpa C; Dr. N. Rahul Pal; Dr. K. Emily Esther Rani; Mr. D. Rambabu; Mrs. I Domilin Shyni; and Mrs. D. Kalaiselvi on October 26, 2026, for Machine Learning-Based Predictive Business Intelligence And Decision Management System.
Inventors include Dr. Srinivasu Polinati; Dr. Sharanamma M Hugar; Mrs. Shilpa C; Dr. N. Rahul Pal; Dr. K. Emily Esther Rani; Mr. D. Rambabu; Mrs. I Domilin Shyni; and Mrs. D. Kalaiselvi.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: Business operations are becoming complex and market conditions are changing quickly, requiring intelligent systems with the ability to process diverse business data and generate precise forecasts and decisions. Traditional BI solutions focus on past trends, fixed dashboards and pre-defined analytical frameworks and models, thereby failing to uncover new trends, predict operational threats and facilitate adaptive decision-making. The overall idea of this research is to design a novel machine learning- based predictive business intelligence and decision management system that provides a unified analytical approach to integrate predictive analytics, automated data processing, explainable AI and optimization-based decision support. The system to be implemented gathers and preprocesses structured and unstructured data from sales transactions, customer interactions, financial records, inventory systems and operational databases to build an integrated business intelligence data set. Machine learning algorithms are used to forecast sales performance, customer demand, financial trends, resource utilization, and potential business risks. The adaptive model selection mechanism detects the appropriate predictive model based on data characteristics and monitors the model's suitability over time, given the evolving business context. In addition, it has an explainable AI module that provides more visibility and management confidence on the business drivers that influenced a specific prediction. The multi-objective decision optimization engine uses the predictive outputs to inform a set of recommendations based on profitability, operational cost, resource limitations, customer satisfaction, and risk exposure. A feedback-driven learning mechanism uses real business outcomes to improve the accuracy of predictions and incrementally adjust the decision strategy. The system is equipped with interactive dashboards, live performance monitoring, scenario simulation, and automatic alerts for proactive and evidence-based management. Its performance can be measured in terms of prediction accuracy, root mean square error, decision response time, resource utilization and business-oriented indicators in comparison with traditional business intelligence techniques. The suggested framework is designed to fill the gap between descriptive analytics and intelligent decision execution, helping organizations make smarter decisions in strategic planning, optimizing operations, handling risks and business environments, and gaining a competitive edge with data.
Disclaimer: Curated by HT Syndication.
