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US Patent 12107742 Machine learning to enhance satellite terminal performance

Patent 12107742 was granted and assigned to Hughes Network Systems on October, 2024 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Hughes Network Systems
Hughes Network Systems
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Current Assignee
Hughes Network Systems
Hughes Network Systems
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
121077420
Patent Inventor Names
Kartik Seshadri0
Sairaj Amberkar0
Namrata Walanj0
Amit Arora0
Sonia Thakur0
Date of Patent
October 1, 2024
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Patent Application Number
179006760
Date Filed
August 31, 2022
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Patent Citations
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US Patent 9349001 Methods and systems for minimizing latency of behavioral analysis
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US Patent 9449279 Network server arrangements for processing non-parametric, multi-dimensional, spatial and temporal human behavior or technical observations measured pervasively, and related methods for the same
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US Patent 9489627 Hybrid clustering for data analytics
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US Patent 9531867 Methods and systems for determining a voice quality score for a mobile telephone
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US Patent 9558056 Organizing network performance metrics into historical anomaly dependency data
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US Patent 9602345 Performance optimizations for wireless access points
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US Patent 9633315 Method and system for distributed machine learning
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US Patent 8706025 Link performance difference metrics and link adaptation for mesh satellite networks
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...
Patent Primary Examiner
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Shirley X Zhang
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CPC Code
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G06N 3/02
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G06N 20/00
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H04L 43/02
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H04L 43/04
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H04L 41/16
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H04L 43/062
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Patent abstract

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for using machine learning to detect and correct satellite terminal performance limitations. In some implementations, a system retrieves data indicating labels for clusters of network performance anomalies. The system generates a set of training data to train a machine learning model, the set of training data being generated by assigning the labels for the clusters to sets of performance indicators used to generate the clusters. The system trains a machine learning model to predict classifications for communication devices based on input of performance indicators for the communication devices. The system determines a classification for the communication device based on output that the trained machine learning model generates.

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