Patent Application: Feature Importance for Supply Chain Models

ChangeBridge: Patent Apps - Business Methods (G06Q)

Detected March 26th, 2026

Summary

The USPTO has published a patent application (US20260087448A1) detailing a feature importance system for supply chain models. The application, filed on December 4, 2025, describes methods for identifying relevant features to predict target variables within supply chain operations.

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What changed

This document is a published patent application (US20260087448A1) from the USPTO, filed on December 4, 2025. It describes a system and method for determining feature importance in supply chain models, utilizing probabilistic graphical models, feature pruning, and network graph modeling to identify relevant features for predicting target variables.

As this is a patent application, it does not impose direct regulatory obligations or compliance deadlines on entities. However, companies involved in supply chain analytics, data modeling, or technology development may find the described methodologies relevant to their intellectual property strategy or operational improvements.

Source document (simplified)

← USPTO Patent Applications

Class Level Feature Importance Using Lasso and Probabilistic Graphical Models

Application US20260087448A1 Kind: A1 Mar 26, 2026

Inventors

Deb Mohanty, Phani Mitra Bulusu, Rashid Puthiyapurayil, Vidhi Chugh

Abstract

Embodiments of the following disclosure provide a feature importance system and method to identify features relevant to determining whether a target variable will achieve a particular value. One example method includes generating a probabilistic graphical model to represent the performance of one or more entities in a supply chain and selecting or more target variables. The method further includes collating a list of features pertaining to the one or more selected target variables, pruning at least one of the one or more features from the list and generating one or more bins in which to distribute the one or more features in the list. The method further includes modeling a network graph incorporating the one or more features in the list and bins and determining one or more inferences pertaining to the one or more supply chain entity target variables.