SYSTEMS AND METHODS FOR MODEL TRAINING BASED ON FEATURE FUSION OF MULTIPLE DATA TYPES

SYSTEMS AND METHODS FOR MODEL TRAINING BASED ON FEATURE FUSION OF MULTIPLE DATA TYPES

Systems, methods, and computer readable storage media that may be used to train a model based on merged common features of two or more different data types. One method includes receiving a plurality of first data elements of a first data type and a plurality of second data elements of a second data...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Narlikar, Girija, Zeng, Yemao, Sethi, Abhishek, Chanda, Raghuveer
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 Narlikar, Girija
Zeng, Yemao
Sethi, Abhishek
Chanda, Raghuveer
description Systems, methods, and computer readable storage media that may be used to train a model based on merged common features of two or more different data types. One method includes receiving a plurality of first data elements of a first data type and a plurality of second data elements of a second data type, identifying first features of each of the plurality of first data elements, identifying second features of each of the plurality of second data elements, generating merged features by combining a first feature of the first features of each of the plurality of first data elements with a second feature of the second features of one of the plurality of second data elements, wherein the first feature and the second feature each represent a common feature, and training a model based on the merged features and at least a portion of the first features and the second features.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2023334328A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2023334328A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2023334328A13</originalsourceid><addsrcrecordid>eNqNyr8KwjAQgPEsDqK-w4GzoImD62kuNpA_pXcZOpUicRIt1PdHBx_A6eMH31IV7lkoMmCyEEmabBlc7iBmSwGkQ598usIZmSzkBI5QSkfgCvsvs4NYgvg2EFgUBOlb4rVa3MfHXDe_rtTWkVyaXZ1eQ52n8Vaf9T0U1nttjDkafcKD-e_6AAxlMSs</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>SYSTEMS AND METHODS FOR MODEL TRAINING BASED ON FEATURE FUSION OF MULTIPLE DATA TYPES</title><source>esp@cenet</source><creator>Narlikar, Girija ; Zeng, Yemao ; Sethi, Abhishek ; Chanda, Raghuveer</creator><creatorcontrib>Narlikar, Girija ; Zeng, Yemao ; Sethi, Abhishek ; Chanda, Raghuveer</creatorcontrib><description>Systems, methods, and computer readable storage media that may be used to train a model based on merged common features of two or more different data types. One method includes receiving a plurality of first data elements of a first data type and a plurality of second data elements of a second data type, identifying first features of each of the plurality of first data elements, identifying second features of each of the plurality of second data elements, generating merged features by combining a first feature of the first features of each of the plurality of first data elements with a second feature of the second features of one of the plurality of second data elements, wherein the first feature and the second feature each represent a common feature, and training a model based on the merged features and at least a portion of the first features and the second features.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2023</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&amp;date=20231019&amp;DB=EPODOC&amp;CC=US&amp;NR=2023334328A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20231019&amp;DB=EPODOC&amp;CC=US&amp;NR=2023334328A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Narlikar, Girija</creatorcontrib><creatorcontrib>Zeng, Yemao</creatorcontrib><creatorcontrib>Sethi, Abhishek</creatorcontrib><creatorcontrib>Chanda, Raghuveer</creatorcontrib><title>SYSTEMS AND METHODS FOR MODEL TRAINING BASED ON FEATURE FUSION OF MULTIPLE DATA TYPES</title><description>Systems, methods, and computer readable storage media that may be used to train a model based on merged common features of two or more different data types. One method includes receiving a plurality of first data elements of a first data type and a plurality of second data elements of a second data type, identifying first features of each of the plurality of first data elements, identifying second features of each of the plurality of second data elements, generating merged features by combining a first feature of the first features of each of the plurality of first data elements with a second feature of the second features of one of the plurality of second data elements, wherein the first feature and the second feature each represent a common feature, and training a model based on the merged features and at least a portion of the first features and the second features.</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>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyr8KwjAQgPEsDqK-w4GzoImD62kuNpA_pXcZOpUicRIt1PdHBx_A6eMH31IV7lkoMmCyEEmabBlc7iBmSwGkQ598usIZmSzkBI5QSkfgCvsvs4NYgvg2EFgUBOlb4rVa3MfHXDe_rtTWkVyaXZ1eQ52n8Vaf9T0U1nttjDkafcKD-e_6AAxlMSs</recordid><startdate>20231019</startdate><enddate>20231019</enddate><creator>Narlikar, Girija</creator><creator>Zeng, Yemao</creator><creator>Sethi, Abhishek</creator><creator>Chanda, Raghuveer</creator><scope>EVB</scope></search><sort><creationdate>20231019</creationdate><title>SYSTEMS AND METHODS FOR MODEL TRAINING BASED ON FEATURE FUSION OF MULTIPLE DATA TYPES</title><author>Narlikar, Girija ; Zeng, Yemao ; Sethi, Abhishek ; Chanda, Raghuveer</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2023334328A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</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>Narlikar, Girija</creatorcontrib><creatorcontrib>Zeng, Yemao</creatorcontrib><creatorcontrib>Sethi, Abhishek</creatorcontrib><creatorcontrib>Chanda, Raghuveer</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Narlikar, Girija</au><au>Zeng, Yemao</au><au>Sethi, Abhishek</au><au>Chanda, Raghuveer</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>SYSTEMS AND METHODS FOR MODEL TRAINING BASED ON FEATURE FUSION OF MULTIPLE DATA TYPES</title><date>2023-10-19</date><risdate>2023</risdate><abstract>Systems, methods, and computer readable storage media that may be used to train a model based on merged common features of two or more different data types. One method includes receiving a plurality of first data elements of a first data type and a plurality of second data elements of a second data type, identifying first features of each of the plurality of first data elements, identifying second features of each of the plurality of second data elements, generating merged features by combining a first feature of the first features of each of the plurality of first data elements with a second feature of the second features of one of the plurality of second data elements, wherein the first feature and the second feature each represent a common feature, and training a model based on the merged features and at least a portion of the first features and the second features.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_US2023334328A1
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
title SYSTEMS AND METHODS FOR MODEL TRAINING BASED ON FEATURE FUSION OF MULTIPLE DATA TYPES
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T10%3A30%3A16IST&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=Narlikar,%20Girija&rft.date=2023-10-19&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2023334328A1%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