USPTO Patent Grant: Radio Signal Identification System Learning

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted patent US12585953B2 to Virginia Tech Intellectual Properties, Inc. for methods and systems related to machine-learned identification of radio frequency (RF) signals. The patent covers techniques for training and deploying AI models to classify RF signals based on their characteristics and environmental factors.

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What changed

Patent US12585953B2, granted on March 24, 2026, details methods, systems, and apparatus for training and deploying machine-learned identification of radio frequency (RF) signals. The patent, assigned to Virginia Tech Intellectual Properties, Inc., focuses on using machine-learning networks to process RF signals, classify them based on associated information, measure the distance between predicted and actual classifications, and update the network accordingly. The filing date was October 4, 2023, with application number 18376480.

This patent grant represents a new intellectual property asset in the field of AI-driven signal processing. While it does not impose direct compliance obligations on regulated entities, it may influence future product development and licensing strategies for companies operating in the telecommunications and technology sectors. Companies developing or utilizing AI for RF signal analysis should be aware of this patent's claims, particularly concerning the training and updating of machine learning models for signal classification.

Source document (simplified)

← USPTO Patent Grants

Radio signal identification, identification system learning, and identifier deployment

Grant US12585953B2 Kind: B2 Mar 24, 2026

Assignee

Virginia Tech Intellectual Properties, Inc.

Inventors

Timothy James O′Shea

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned identification of radio frequency (RF) signals. One of the methods includes: determining an RF signal configured to be transmitted through an RF band of a communication medium; determining first classification information that is associated with the RF signal, and that includes a representation of a characteristic of the RF signal or a characteristic of an environment in which the RF signal is communicated; using at least one machine-learning network to process the RF signal and generate second classification information as a prediction of the first classification information; calculating a measure of distance between (i) the second classification information that was generated by the at least one machine-learning network, and (ii) the first classification information associated with the RF signal; and updating the at least one machine-learning network based on the measure of distance.