MACHINE LEARNING TO MONITOR OPERATIONS OF A DEVICE

A multi-tier machine learning engine receives signal data characterizing a monitored signal of the computing platform. The machine learning engine can include a plurality of tiers that employ frequency domain analysis on the signal data to identify an application executing on the computing platform...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	ANDERSON, PETER D, RUBLE, MACEY C, PITRUZZELLO, ANN M, HAYES, CHARLES ETHAN, WELBORN, MATTHEW LEE
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page
container_issue
container_start_page
container_title
container_volume
creator	ANDERSON, PETER D RUBLE, MACEY C PITRUZZELLO, ANN M HAYES, CHARLES ETHAN WELBORN, MATTHEW LEE
description	A multi-tier machine learning engine receives signal data characterizing a monitored signal of the computing platform. The machine learning engine can include a plurality of tiers that employ frequency domain analysis on the signal data to identify an application executing on the computing platform and a module and/or loop of the identified application and employ time domain analysis on the signal data to identify timing of events within the identified module and/or loop of the identified application.
format	Patent
fullrecord	<record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2021374580A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2021374580A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2021374580A13</originalsourceid><addsrcrecordid>eNrjZDDydXT28PRzVfBxdQzy8_RzVwjxV_D19_MM8Q9S8A9wDXIM8fT3C1bwd1NwVHBxDfN0duVhYE1LzClO5YXS3AzKbq4hzh66qQX58anFBYnJqXmpJfGhwUYGRobG5iamFgaOhsbEqQIA_BUnhw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>MACHINE LEARNING TO MONITOR OPERATIONS OF A DEVICE</title><source>esp@cenet</source><creator>ANDERSON, PETER D ; RUBLE, MACEY C ; PITRUZZELLO, ANN M ; HAYES, CHARLES ETHAN ; WELBORN, MATTHEW LEE</creator><creatorcontrib>ANDERSON, PETER D ; RUBLE, MACEY C ; PITRUZZELLO, ANN M ; HAYES, CHARLES ETHAN ; WELBORN, MATTHEW LEE</creatorcontrib><description>A multi-tier machine learning engine receives signal data characterizing a monitored signal of the computing platform. The machine learning engine can include a plurality of tiers that employ frequency domain analysis on the signal data to identify an application executing on the computing platform and a module and/or loop of the identified application and employ time domain analysis on the signal data to identify timing of events within the identified module and/or loop of the identified application.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2021</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20211202&DB=EPODOC&CC=US&NR=2021374580A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20211202&DB=EPODOC&CC=US&NR=2021374580A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ANDERSON, PETER D</creatorcontrib><creatorcontrib>RUBLE, MACEY C</creatorcontrib><creatorcontrib>PITRUZZELLO, ANN M</creatorcontrib><creatorcontrib>HAYES, CHARLES ETHAN</creatorcontrib><creatorcontrib>WELBORN, MATTHEW LEE</creatorcontrib><title>MACHINE LEARNING TO MONITOR OPERATIONS OF A DEVICE</title><description>A multi-tier machine learning engine receives signal data characterizing a monitored signal of the computing platform. The machine learning engine can include a plurality of tiers that employ frequency domain analysis on the signal data to identify an application executing on the computing platform and a module and/or loop of the identified application and employ time domain analysis on the signal data to identify timing of events within the identified module and/or loop of the identified application.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDDydXT28PRzVfBxdQzy8_RzVwjxV_D19_MM8Q9S8A9wDXIM8fT3C1bwd1NwVHBxDfN0duVhYE1LzClO5YXS3AzKbq4hzh66qQX58anFBYnJqXmpJfGhwUYGRobG5iamFgaOhsbEqQIA_BUnhw</recordid><startdate>20211202</startdate><enddate>20211202</enddate><creator>ANDERSON, PETER D</creator><creator>RUBLE, MACEY C</creator><creator>PITRUZZELLO, ANN M</creator><creator>HAYES, CHARLES ETHAN</creator><creator>WELBORN, MATTHEW LEE</creator><scope>EVB</scope></search><sort><creationdate>20211202</creationdate><title>MACHINE LEARNING TO MONITOR OPERATIONS OF A DEVICE</title><author>ANDERSON, PETER D ; RUBLE, MACEY C ; PITRUZZELLO, ANN M ; HAYES, CHARLES ETHAN ; WELBORN, MATTHEW LEE</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2021374580A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2021</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>ANDERSON, PETER D</creatorcontrib><creatorcontrib>RUBLE, MACEY C</creatorcontrib><creatorcontrib>PITRUZZELLO, ANN M</creatorcontrib><creatorcontrib>HAYES, CHARLES ETHAN</creatorcontrib><creatorcontrib>WELBORN, MATTHEW LEE</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ANDERSON, PETER D</au><au>RUBLE, MACEY C</au><au>PITRUZZELLO, ANN M</au><au>HAYES, CHARLES ETHAN</au><au>WELBORN, MATTHEW LEE</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>MACHINE LEARNING TO MONITOR OPERATIONS OF A DEVICE</title><date>2021-12-02</date><risdate>2021</risdate><abstract>A multi-tier machine learning engine receives signal data characterizing a monitored signal of the computing platform. The machine learning engine can include a plurality of tiers that employ frequency domain analysis on the signal data to identify an application executing on the computing platform and a module and/or loop of the identified application and employ time domain analysis on the signal data to identify timing of events within the identified module and/or loop of the identified application.</abstract><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier
ispartof
issn
language	eng
recordid	cdi_epo_espacenet_US2021374580A1
source	esp@cenet
subjects	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS
title	MACHINE LEARNING TO MONITOR OPERATIONS OF A DEVICE
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T07%3A16%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=ANDERSON,%20PETER%20D&rft.date=2021-12-02&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2021374580A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true