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:
Apr. 21, 2026

Filed:

Sep. 20, 2022
Applicant:

Tektronix, Inc., Beaverton, OR (US);

Inventors:
Assignee:

Tektronix, Inc., Beaverton, OR (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01R 31/28 (2006.01);
U.S. Cl.
CPC ...
G01R 31/2834 (2013.01);
Abstract

A test and measurement device has an interface, one or more connectors, each connector to allow the test and measurement device to connect to a test and measurement instrument, and one or more processors, the one or more processors configured to execute code to cause the one or more processors to: receive one or more user inputs through the interface identifying one or more tests to perform on a device under test (DUT); form a connection through one of the one or more connectors to the DUT to perform the one or more tests and receive test result data; apply one or more machine learning models to the test result data to identify potentially anomalous test results; and generate and present a representation of the test result data and the potentially anomalous test results. A method of analyzing test data includes receiving one or more user inputs through an interface identifying one or more test to perform on a device under test (DUT), forming a connection to at least one test and measurement instrument, directing the test and measurement instrument to perform one or more tests on the DUT and receive test result data, applying one or more machine learning models to the test result data to identify potentially anomalous test results, and generating and presenting a representation of the test result data and the potentially anomalous test results.


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