US Patent Issued to HiddenLayer on July 14 for "Agentic-based approach for reducing false positives in machine learning model output classifications" (California Inventor)

ALEXANDRIA, Va., July 14 -- United States Patent no. 12,682,249, issued on July 14, was assigned to HiddenLayer Inc. (Austin, Texas).

"Agentic-based approach for reducing false positives in machine learning model output classifications" was invented by Julian Collado Umana (Irvine, Calif.).

According to the abstract* released by the U.S. Patent & Trademark Office: "Techniques are provided for adjudicating suspected-malicious samples using in-context gating and an appeals stage to reduce false positives. A first-stage classifier generates a benign-or-malicious verdict. When malicious is indicated, a context-gating model determines whether the sample is in-context for a target application domain. If out-of-context, the initial verdict is fi...