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US Patent 11923895 Optical transmitter tuning using machine learning and reference parameters

Patent 11923895 was granted and assigned to Tektronix on March, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Tektronix
Tektronix
0
Current Assignee
Tektronix
Tektronix
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119238950
Patent Inventor Names
Heike Tritschler0
Kan Tan0
Evan Douglas Smith0
John J. Pickerd0
Date of Patent
March 5, 2024
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Patent Application Number
177011860
Date Filed
March 22, 2022
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Patent Citations
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US Patent 9548858 Skew management for PAM communication systems
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US Patent 9699009 Dual-mode non-return-to-zero (NRZ)/ four-level pulse amplitude modulation (PAM4) receiver with digitally enhanced NRZ sensitivity
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US Patent 10171161 Machine learning for link parameter identification in an optical communications system
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US Patent 10236982 Fiber parameter identification
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US Patent 10270527 Method for testing optical transmitters
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US Patent 10396897 Systems and methods for predicting defects in optical transceiver devices
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US Patent 10585121 Recommending measurements based on detected waveform type
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US Patent 10727973 Apparatus and method for self-learning and predictive DWDM network
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Patent Primary Examiner
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Hibret A Woldekidan
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CPC Code
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G06N 3/08
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H04B 10/0779
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H04B 10/40
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H04B 10/0777
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H04B 10/0775
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H04B 10/0773
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H04B 10/43
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H04B 10/50
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Patent abstract

A test and measurement system includes a test and measurement device, a connection to allow the test and measurement device to connect to an optical transceiver, and one or more processors, configured to execute code that causes the one or more processors to: set operating parameters for the optical transceiver to reference operating parameters; acquire a waveform from the optical transceiver; repeatedly execute the code to cause the one or more processors to set operating parameters and acquire a waveform, for each of a predetermined number of sets of reference operating parameters; build one or more tensors from the acquired waveforms; send the one or more tensors to a machine learning system to obtain a set of predicted operating parameters; set the operating parameters for the optical transceiver to the predicted operating parameters; and test the optical transceiver using the predicted operating parameters.

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