Cognitive horizon surveillance
A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system accor...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | eng |
| Schlagworte: | |
| 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 | Narayan, Chandrasekhar Gruhl, Daniel Kato, Linda Ha Gentile, Anna Lisa Zubarev, Dmitry Park, Nathaniel H Alba, Alfredo DeLuca, Chad Eric Welch, Steven R Ristoski, Petar |
| description | A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US11663273B2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US11663273B2</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US11663273B23</originalsourceid><addsrcrecordid>eNrjZJBzzk_PyyzJLEtVyMgvyqzKz1MoLi0qS83MyUnMS07lYWBNS8wpTuWF0twMim6uIc4euqkF-fGpxQWJyal5qSXxocGGhmZmxkbmxk5GxsSoAQAy2CUx</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Cognitive horizon surveillance</title><source>esp@cenet</source><creator>Narayan, Chandrasekhar ; Gruhl, Daniel ; Kato, Linda Ha ; Gentile, Anna Lisa ; Zubarev, Dmitry ; Park, Nathaniel H ; Alba, Alfredo ; DeLuca, Chad Eric ; Welch, Steven R ; Ristoski, Petar</creator><creatorcontrib>Narayan, Chandrasekhar ; Gruhl, Daniel ; Kato, Linda Ha ; Gentile, Anna Lisa ; Zubarev, Dmitry ; Park, Nathaniel H ; Alba, Alfredo ; DeLuca, Chad Eric ; Welch, Steven R ; Ristoski, Petar</creatorcontrib><description>A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; 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&date=20230530&DB=EPODOC&CC=US&NR=11663273B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230530&DB=EPODOC&CC=US&NR=11663273B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Narayan, Chandrasekhar</creatorcontrib><creatorcontrib>Gruhl, Daniel</creatorcontrib><creatorcontrib>Kato, Linda Ha</creatorcontrib><creatorcontrib>Gentile, Anna Lisa</creatorcontrib><creatorcontrib>Zubarev, Dmitry</creatorcontrib><creatorcontrib>Park, Nathaniel H</creatorcontrib><creatorcontrib>Alba, Alfredo</creatorcontrib><creatorcontrib>DeLuca, Chad Eric</creatorcontrib><creatorcontrib>Welch, Steven R</creatorcontrib><creatorcontrib>Ristoski, Petar</creatorcontrib><title>Cognitive horizon surveillance</title><description>A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZJBzzk_PyyzJLEtVyMgvyqzKz1MoLi0qS83MyUnMS07lYWBNS8wpTuWF0twMim6uIc4euqkF-fGpxQWJyal5qSXxocGGhmZmxkbmxk5GxsSoAQAy2CUx</recordid><startdate>20230530</startdate><enddate>20230530</enddate><creator>Narayan, Chandrasekhar</creator><creator>Gruhl, Daniel</creator><creator>Kato, Linda Ha</creator><creator>Gentile, Anna Lisa</creator><creator>Zubarev, Dmitry</creator><creator>Park, Nathaniel H</creator><creator>Alba, Alfredo</creator><creator>DeLuca, Chad Eric</creator><creator>Welch, Steven R</creator><creator>Ristoski, Petar</creator><scope>EVB</scope></search><sort><creationdate>20230530</creationdate><title>Cognitive horizon surveillance</title><author>Narayan, Chandrasekhar ; Gruhl, Daniel ; Kato, Linda Ha ; Gentile, Anna Lisa ; Zubarev, Dmitry ; Park, Nathaniel H ; Alba, Alfredo ; DeLuca, Chad Eric ; Welch, Steven R ; Ristoski, Petar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11663273B23</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>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Narayan, Chandrasekhar</creatorcontrib><creatorcontrib>Gruhl, Daniel</creatorcontrib><creatorcontrib>Kato, Linda Ha</creatorcontrib><creatorcontrib>Gentile, Anna Lisa</creatorcontrib><creatorcontrib>Zubarev, Dmitry</creatorcontrib><creatorcontrib>Park, Nathaniel H</creatorcontrib><creatorcontrib>Alba, Alfredo</creatorcontrib><creatorcontrib>DeLuca, Chad Eric</creatorcontrib><creatorcontrib>Welch, Steven R</creatorcontrib><creatorcontrib>Ristoski, Petar</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Narayan, Chandrasekhar</au><au>Gruhl, Daniel</au><au>Kato, Linda Ha</au><au>Gentile, Anna Lisa</au><au>Zubarev, Dmitry</au><au>Park, Nathaniel H</au><au>Alba, Alfredo</au><au>DeLuca, Chad Eric</au><au>Welch, Steven R</au><au>Ristoski, Petar</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Cognitive horizon surveillance</title><date>2023-05-30</date><risdate>2023</risdate><abstract>A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_epo_espacenet_US11663273B2 |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Cognitive horizon surveillance |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T09%3A35%3A23IST&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=Narayan,%20Chandrasekhar&rft.date=2023-05-30&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS11663273B2%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 |