MUMBAI, India, Nov. 21 -- Intellectual Property India has published a patent application (202541096482 A) filed by Saveetha Engineering College, Chennai, Tamil Nadu, on Oct. 7, for 'adaptive sampling methodology for large-scale behavioral research studies.'
Inventor(s) include Dr. M. Ganesan Alias Kanagaraj.
The application for the patent was published on Nov. 21, under issue no. 47/2025.
According to the abstract released by the Intellectual Property India: "The invention discloses an Adaptive Sampling Methodology specifically designed for largescale behavioral research studies, where traditional static sampling frameworks are often insufficient to capture diverse and evolving participant dynamics. Unlike conventional methods that rely on fixed quotas or random selection, this invention introduces a dynamic, feedback-driven framework that recalibrates sampling stqltegies in real time based on participant engagement, demographic representation, and data quality 'indicators. The methodology operates through a three-tier adaptive framework consisting uf (i) an Initialization Module, which establishes baseline quotas and inclusion criteria; (ii) a RealTime Monitoring and Feedback Module, which tracks engagement levels, dropout risks, and demographic balance; and (iii) an Adaptive Recalibration Module, which adjusts recruitment strategies, redistributes sampling weights, and introduces corrective interventions to maintain . ' representativeness. These modules work together in iterative cycles, ensuring that data collection processes remain inclusive, bias-resistant, and resilient against attrition. Additional features include an Engagement Monitoring System that minimizes response fatigue, a Bias Reduction Engine that corrects sampling distortions, and a Predictive Response Analyzer that forecasts potential under-representation or dropout risks using historical and real-time data. The methodology is fully interoperable with digital survey platforms, mobile applications, sensor-based systems, apd traditional field research environments, making it scalable across both localized and global studies. By incorporating real-time adaptation, predictive analytics, and ethical safeguards such as participant anonymization and transparent recalibration logs, the invention provides a costefficient, scalable, and ethically sound solution for behavioral researchers. This adaptive approach ensures balanced demographic representation, reduces systematic bias, improves data reliability, and enables more authentic insights into human behavior across diverse and rapidly evolving populations."
Disclaimer: Curated by HT Syndication.
