Intellectual Property India Publishes Patent Application for Machine Learning-Based Economic Intelligence System For Real-Time Market Analysis And Forecasting

MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641081294 A) filed by Dr. S. Ramasamy; Prof. Dr. Durgadas Digambarrao Choudhari; Dr. V. Ramidha; Bejawada Neelima; Thalluri Meghana; Dr. Lakshmanpriya C; Dr. S. Velayutham; Dr. J. Suresh Kumar; Dr. D. Muniswamy; Urlam Pranita; Aditi Sharma; and Y. Rani on July 01, 2026, for Machine Learning-Based Economic Intelligence System For Real-Time Market Analysis And Forecasting.

Inventors include Dr. S. Ramasamy; Prof. Dr. Durgadas Digambarrao Choudhari; Dr. V. Ramidha; Bejawada Neelima; Thalluri Meghana; Dr. Lakshmanpriya C; Dr. S. Velayutham; Dr. J. Suresh Kumar; Dr. D. Muniswamy; Urlam Pranita; Aditi Sharma; and Y. Rani.

The application for the patent was published on July 03, 2026, under issue no. 27/2026.

Abstract: MACHINE LEARNING-BASED ECONOMIC INTELLIGENCE SYSTEM FOR REAL-TIME MARKET ANALYSIS AND FORECASTING The current innovation reveals a Machine Learning-Based Economic Intelligence System for Real-Time Market Analysis and Forecasting, which offers an intelligent and automated framework for assessing economic conditions, forecasting market trends, and facilitating strategic decision-making. The system incorporates Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, Natural Language Processing (NLP), and Cloud Computing technologies to gather, process, and analyse economic and financial data from various heterogeneous sources. Real-time data is obtained via stock exchanges, commodities markets, banking systems, governmental databases, financial reports, news outlets, and social media platforms. The gathered data is subjected to preprocessing, feature extraction, and analytical processing to uncover concealed patterns and market trends. Advanced machine learning algorithms, including Deep Neural Networks (DNN), Long Short-Term Memory (LSTM) networks, Random Forest, and Support Vector Machines (SVM), are employed to predict economic indicators such as stock prices, commodity values, inflation rates, GDP growth, exchange rates, and investment prospects. The system additionally includes a Natural Language Processing module to assess financial news and social media information for sentiment analysis and market perception evaluation. A risk assessment module perpetually observes market activity to identify anomalies, financial hazards, and possible economic disturbances. A sophisticated decision-support system produces actionable suggestions for investors, enterprises, financial institutions, and governments. The invention encompasses a cloud-based infrastructure for scalable data storage and real-time processing, as well as a continuous learning mechanism that enhances forecasting accuracy via periodic model upgrades. The suggested system improves the development of economic intelligence, market forecasting, risk management, and decision-making abilities, thereby offering a dependable and efficient solution for real-time economic research and forecasting across diverse financial and economic sectors. FIG.1.

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