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USPTO Patent Grant: Iterative Attention Neural Network Training

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Published March 24th, 2026
Detected March 25th, 2026
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Summary

The USPTO has granted patent US12586001B1 for an iterative attention-based neural network training and processing method. This patent covers a system that refines its focus of attention on syntactical elements to improve probability generation and update representations, with potential for user-provided feedback to further train the network.

What changed

The United States Patent and Trademark Office (USPTO) has issued patent US12586001B1, titled 'Iterative attention neural network training and processing method and system.' The patent describes a novel method for training neural networks that iteratively applies and refines a focus of attention on syntactical elements. This process generates updated probabilities that inform subsequent attention, leading to improved representations of data. The system also allows for user feedback to further train the network.

This patent grant is primarily of interest to technology companies and researchers in the fields of artificial intelligence and machine learning. While it does not impose direct compliance obligations on regulated entities, it establishes intellectual property rights for the inventors and assignee. Companies developing or utilizing similar iterative attention-based neural network training methods should be aware of this patent to avoid potential infringement. No specific compliance deadlines or penalties are associated with this patent grant itself, as it pertains to intellectual property rights rather than regulatory mandates.

Source document (simplified)

← USPTO Patent Grants

Iterative attention-based neural network training and processing

Grant US12586001B1 Kind: B1 Mar 24, 2026

Assignee

Steven D. Flinn

Inventors

Steven Dennis Flinn, Naomi Felina Moneypenny

Abstract

An iterative attention-based neural network training and processing method and system iteratively applies a focus of attention of a trained neural network on syntactical elements and generates probabilities associated with representations of the syntactical elements, which in turn inform a subsequent focus of attention of the neural network, resulting in updated probabilities. The updated probabilities are then applied to generate syntactical elements for delivery to a user. The user may respond to the delivered syntactical elements, providing additional training information to the trained neural network.

CPC Classifications

G06N 20/00 G06N 5/048 G06N 3/02 G06F 40/211 G06F 40/216 G06F 40/30

Filing Date

2025-01-16

Application No.

19024655

Claims

84

View original document →

Named provisions

Iterative attention-based neural network training and processing

Classification

Agency
USPTO
Published
March 24th, 2026
Instrument
Rule
Legal weight
Non-binding
Stage
Final
Change scope
Minor
Document ID
US12586001B1

Who this affects

Applies to
Technology companies
Industry sector
5112 Software & Technology
Activity scope
AI Model Training Machine Learning Development
Geographic scope
United States US

Taxonomy

Primary area
Intellectual Property
Operational domain
Legal
Topics
Artificial Intelligence Machine Learning

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