Machine Learning Patent Application for Item Identification

ChangeBridge: Patent Apps - Business Methods (G06Q)

Detected March 26th, 2026

Summary

The USPTO has published a new patent application (US20260087476A1) detailing methods and devices utilizing machine learning for item identification. The application describes a process for capturing an image, classifying an item using a machine learning model, and updating a database based on the selected item.

View original document View source feed page

What changed

This document is a publication of a new patent application filed with the USPTO, specifically application US20260087476A1, titled "Devices and Methods Utilizing Machine Learning for Item Identification." The application, filed on September 25, 2024, by inventors Justin F. Barish, Jack Kraszewski, and Matthew V. Avallone, outlines a method that uses machine learning to identify items from captured images. Key steps include classifying the item, generating candidate items with scores, retrieving data, modifying scores based on this data, selecting the highest-scoring candidate, and updating a database.

As this is a patent application publication, it does not impose immediate regulatory obligations or compliance deadlines on businesses. However, it signals potential future technological advancements and intellectual property developments in the area of AI-driven item identification. Companies operating in sectors that utilize machine learning for product recognition, inventory management, or data processing should be aware of this application as it may influence future product development, competitive landscapes, and patentability considerations in this domain.

Source document (simplified)

← USPTO Patent Applications

Devices and Methods Utilizing Machine Learning for Item Identification

Application US20260087476A1 Kind: A1 Mar 26, 2026

Inventors

Justin F. Barish, Jack Kraszewski, Matthew V. Avallone

Abstract

Methods and devices utilizing machine learning for item identification are disclosed herein. The method captures an image of an item present in a region. The method classifies, based on the image, the item utilizing a machine learning model, and generates, utilizing the machine learning model, one or more candidate items and a score for each candidate item based on the classified item. The method determines whether a score of a candidate item among the one or more candidate items exceeds a threshold. If a score of a candidate item among the one or more candidate items does not exceed the threshold, the method retrieves data associated with each candidate item. The method modifies a score of each candidate item based on the retrieved data and selects a candidate item having a highest modified score. The method updates the database based on the selected candidate item and displays the selected candidate item.