The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 11, 2022

Filed:

Sep. 20, 2019
Applicant:

Cable Television Laboratories, Inc., Louisville, CO (US);

Inventors:

Jingjie Zhu, Erie, CO (US);

Karthik Sundaresan, Boulder, CO (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06G 7/00 (2006.01); G06K 9/62 (2022.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); H04L 25/03 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6268 (2013.01); G06K 9/6256 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); H04L 25/03165 (2013.01); G06K 9/6265 (2013.01);
Abstract

The present disclosure generally relates to apparatus, software and methods for detecting and classifying anomalous features in one-dimensional data. The apparatus, software and methods disclosed herein use a YOLO-type algorithm on one-dimensional data. For example, the data can be any one-dimensional data or time series, such as but not limited to be power over time data, signal to noise ratio (SNR) over time data, modulation error ratio (MER) data, full band capture data, radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable anomalous features. In some embodiments, the features can be characterized or labeled as known phenomena and used to train a machine learning model via supervised learning to recognize those features in a new data series.


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