AI Machine Learning Patent for Detecting Trade Spoofing Patterns
Summary
The USPTO has published patent application US20260099711A1 filed by Trading Technologies International, Inc. covering artificial intelligence and machine learning techniques for processing trading data to detect trade spoofing patterns. The application describes using semi-supervised machine learning with positively labeled and unlabeled training data to develop classification models distinguishing spoofing behavior from legitimate trading. Clustering techniques segment trading activity into assessable bursts for potential spoofing status. The application was filed on October 10, 2025, under application number 19355773.
What changed
The USPTO published a patent application covering AI and machine learning technology for detecting trade spoofing in financial trading. The technology employs semi-supervised machine learning applied to labeled and unlabeled training data to create classification models that distinguish trading behavior likely qualifying as trade spoofing from legitimate trading activity. Clustering techniques segment large datasets into bursts of trading activity for individual spoofing assessment. The application covers CPC classifications G06N 3/08 and G06N 3/004.
Trading technology firms, financial software developers, and exchanges developing anti-spoofing surveillance systems should monitor this application for potential licensing implications once granted. Competitors in trading technology may need to evaluate whether their spoofing detection systems could implicate these patent claims. This application signals growing intersection between AI/machine learning and market surveillance compliance technologies.
What to do next
- Monitor for patent grant or office action
Archived snapshot
Apr 11, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Applied Artificial Intelligence Technology for Processing Trade Data to Detect Patterns Indicative of Potential Trade Spoofing
Application US20260099711A1 Kind: A1 Apr 09, 2026
Assignee
Trading Technologies International, Inc.
Inventors
David I. Widerhorn, Paul Giedratis, Melanie Rubino, Carolyn Phillips
Abstract
Various techniques are described for using machine-learning artificial intelligence to improve how trading data can be processed to detect improper trading behaviors such as trade spoofing. In an example embodiment, semi-supervised machine learning is applied to positively labeled and unlabeled training data to develop a classification model that distinguishes between trading behavior likely to qualify as trade spoofing and trading behavior not likely to qualify as trade spoofing. Also, clustering techniques can be employed to segment larger sets of training data and trading data into bursts of trading activities that are to be assessed for potential trade spoofing status.
CPC Classifications
G06N 3/08 G06N 3/004
Filing Date
2025-10-10
Application No.
19355773
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