MUMBAI, India, Oct. 26 -- Intellectual Property India has published a patent application (202611112078 A) filed by Integral University on September 18, 2026, for A Computer-Implemented System And Method For Calibrating A Multi-Tier Structural Predictive Model From Workforce Survey Data And Simulating Actionable Intervention Targets.
Inventors include Dr Orooj Siddiqui; and Ms Anamta Ali.
The application for the patent was published on October 23, 2026, under issue no. 43/2026.
Abstract: The present invention discloses a computer-implemented system and method for calibrating a multi-tier structural predictive model using workforce survey data and converting the resulting analysis into actionable intervention targets for organizational improvement. The system brings together a data input interface, calibration processor, validation module, and simulation engine within an integrated computational framework. The system collects indicator-level responses from workforce surveys that represent multiple latent organizational constructs. These responses are processed using an iterative variance-based Partial Least Squares (PLS) estimation approach to establish the relationships between observed indicators and underlying constructs, as well as the structural relationships among the constructs. During this process, the calibration processor calculates composite scores for the relevant constructs, estimates the direction and strength of relationships between them, and allows the model to be recalibrated as additional workforce data becomes available. To ensure the reliability of the calibrated model, the validation module evaluates the consistency and statistical significance of the results. This includes the use of bootstrap resampling procedures and multicollinearity diagnostics to assess the stability and robustness of the estimated relationships. The simulation engine then uses the calibrated structural model to examine the potential effects of targeted changes in upstream organizational factors. By applying controlled perturbations to selected constructs and tracing their effects through the model's dependency structure, the system can estimate the level of intervention required to achieve predefined organizational outcomes. Based on these simulated results, potential intervention areas can be prioritized according to their expected organizational impact, resource efficiency, and potential contribution to reducing workforce turnover risk. Unlike conventional workforce analytics systems that primarily provide descriptive statistics or identify correlations, the disclosed invention provides a mechanism for translating workforce survey data into quantified and evidence-based intervention recommendations. In certain implementations, the system may further convert these recommendations into resource-allocation, workforce-planning, or scheduling instructions for integration with downstream enterprise resource planning (ERP) or related organizational management systems. The invention therefore provides a scalable, systematic, and reproducible framework for organizational analytics and predictive workforce management. By combining model calibration, statistical validation, scenario simulation, and intervention prioritization within a single computational architecture, the system can support evidence-based organizational decision-making across different enterprise environments.
Disclaimer: Curated by HT Syndication.
