MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202611049757 A) filed by Vikas Kamra; Kumar Omkar; Farhan Ahmad; Sayyad Mohd Hasan; and Himanshu Singh Yadav, Greater Noida, Uttar Pradesh, on April 19, for 'unified ai-driven predictive framework for multi-domain behavioral anomaly detection and agricultural yield forecasting.'
Inventor(s) include Vikas Kamra; Kumar Omkar; Farhan Ahmad; Sayyad Mohd Hasan; and Himanshu Singh Yadav.
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 presents a Unified AI-Driven Predictive Framework (UAIPF). This framework integrates two domain-specific machine learning systems: an Agricultural Yield Estimator and a Network Traffic Anomaly Analyzer. It operates within a single modular software architecture exposed through a Flask-based REST API. The Agricultural Yield Estimator takes twelve agronomic and environmental parameters (crop type, country, rainfall, temperature, season, climate zone, soil quality, field area, irrigation, fertilizer rate, pH, humidity) and applies trained ensemble models-a Random Forest regressor (100 trees, max depth 15) and an XGBoost regressor (100 rounds, learning rate 0.1). Both models are trained on over 5,000 records from FAO and World Bank data. Predictions are presented in hectograms per hectare along with a confidence interval (85%-115%) and ranked feature importance scores, indicating that average rainfall (28.5%), temperature (22.1%), crop type (18.3%), soil quality (12.4%), and pesticide usage (8.7%) are the main contributors. The Network Traffic Anomaly Analyzer processes network flow records (duration, protocol type, service, flag, source/destination bytes, host statistics) from benchmark datasets (NSL-KDD, CICIDS) or live streams. An Isolation Forest model provides primary anomaly detection, while a K-Means clustering model offers additional distance-based anomaly indicators. A hybrid anomaly score, which is a weighted combination of the normalized Isolation Forest score and normalized cluster centroid distance, is compared against either a static or a dynamically adaptive threshold to classify each record as NORMAL or ANOMALY. The unifying concept of both modules is that diverse behavioural feature vectors, whether from field conditions or network packets, are processed through a shared preprocessing pipeline. These vectors are routed to domain-specific ensemble models via a JSON header in a single API endpoint, and results are returned as explainable, scored outputs. This unified architecture allows organizations to deploy one integrated AI system that addresses both food security and information security challenges."
Disclaimer: Curated by HT Syndication.
