MACHINE-LEARNING MODEL FOR DETECTING A DEVICE WITHIN A VENUE
A model is configured to determine whether a device is located within a venue. During a baseline time period, the system detects wireless pings from mobile devices. The system obtains device parameters from the wireless pings. The system evaluates the device parameters to determine whether a mobile...
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 | Isaacson, Carrie Mohan, Kapil Umezawa, Kai |
| description | A model is configured to determine whether a device is located within a venue. During a baseline time period, the system detects wireless pings from mobile devices. The system obtains device parameters from the wireless pings. The system evaluates the device parameters to determine whether a mobile device entered the venue or remained outside of the venue. The system trains a model on training data corresponding to the baseline time period, the model configured to differentiate between devices that enter the venue and devices that remain outside the venue based on device parameters associated with the device. The system applies the model to future detected devices to determine whether or not the devices enter the venue. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2022248166A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2022248166A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2022248166A13</originalsourceid><addsrcrecordid>eNrjZLDxdXT28PRz1fVxdQzy8_RzV_D1d3H1UXDzD1JwcQ1xdQ4BiTkC2WGezq4K4Z4hQNVAfpirX6grDwNrWmJOcSovlOZmUHZzDXH20E0tyI9PLS5ITE7NSy2JDw02MjAyMjKxMDQzczQ0Jk4VAHISKgI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>MACHINE-LEARNING MODEL FOR DETECTING A DEVICE WITHIN A VENUE</title><source>esp@cenet</source><creator>Isaacson, Carrie ; Mohan, Kapil ; Umezawa, Kai</creator><creatorcontrib>Isaacson, Carrie ; Mohan, Kapil ; Umezawa, Kai</creatorcontrib><description>A model is configured to determine whether a device is located within a venue. During a baseline time period, the system detects wireless pings from mobile devices. The system obtains device parameters from the wireless pings. The system evaluates the device parameters to determine whether a mobile device entered the venue or remained outside of the venue. The system trains a model on training data corresponding to the baseline time period, the model configured to differentiate between devices that enter the venue and devices that remain outside the venue based on device parameters associated with the device. The system applies the model to future detected devices to determine whether or not the devices enter the venue.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRICITY ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR ; TRANSMISSION ; WIRELESS COMMUNICATIONS NETWORKS</subject><creationdate>2022</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=20220804&DB=EPODOC&CC=US&NR=2022248166A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220804&DB=EPODOC&CC=US&NR=2022248166A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Isaacson, Carrie</creatorcontrib><creatorcontrib>Mohan, Kapil</creatorcontrib><creatorcontrib>Umezawa, Kai</creatorcontrib><title>MACHINE-LEARNING MODEL FOR DETECTING A DEVICE WITHIN A VENUE</title><description>A model is configured to determine whether a device is located within a venue. During a baseline time period, the system detects wireless pings from mobile devices. The system obtains device parameters from the wireless pings. The system evaluates the device parameters to determine whether a mobile device entered the venue or remained outside of the venue. The system trains a model on training data corresponding to the baseline time period, the model configured to differentiate between devices that enter the venue and devices that remain outside the venue based on device parameters associated with the device. The system applies the model to future detected devices to determine whether or not the devices enter the venue.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRICITY</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><subject>TRANSMISSION</subject><subject>WIRELESS COMMUNICATIONS NETWORKS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLDxdXT28PRz1fVxdQzy8_RzV_D1d3H1UXDzD1JwcQ1xdQ4BiTkC2WGezq4K4Z4hQNVAfpirX6grDwNrWmJOcSovlOZmUHZzDXH20E0tyI9PLS5ITE7NSy2JDw02MjAyMjKxMDQzczQ0Jk4VAHISKgI</recordid><startdate>20220804</startdate><enddate>20220804</enddate><creator>Isaacson, Carrie</creator><creator>Mohan, Kapil</creator><creator>Umezawa, Kai</creator><scope>EVB</scope></search><sort><creationdate>20220804</creationdate><title>MACHINE-LEARNING MODEL FOR DETECTING A DEVICE WITHIN A VENUE</title><author>Isaacson, Carrie ; Mohan, Kapil ; Umezawa, Kai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2022248166A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRICITY</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><topic>TRANSMISSION</topic><topic>WIRELESS COMMUNICATIONS NETWORKS</topic><toplevel>online_resources</toplevel><creatorcontrib>Isaacson, Carrie</creatorcontrib><creatorcontrib>Mohan, Kapil</creatorcontrib><creatorcontrib>Umezawa, Kai</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Isaacson, Carrie</au><au>Mohan, Kapil</au><au>Umezawa, Kai</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>MACHINE-LEARNING MODEL FOR DETECTING A DEVICE WITHIN A VENUE</title><date>2022-08-04</date><risdate>2022</risdate><abstract>A model is configured to determine whether a device is located within a venue. During a baseline time period, the system detects wireless pings from mobile devices. The system obtains device parameters from the wireless pings. The system evaluates the device parameters to determine whether a mobile device entered the venue or remained outside of the venue. The system trains a model on training data corresponding to the baseline time period, the model configured to differentiate between devices that enter the venue and devices that remain outside the venue based on device parameters associated with the device. The system applies the model to future detected devices to determine whether or not the devices enter the venue.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_epo_espacenet_US2022248166A1 |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR TRANSMISSION WIRELESS COMMUNICATIONS NETWORKS |
| title | MACHINE-LEARNING MODEL FOR DETECTING A DEVICE WITHIN A VENUE |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T00%3A35%3A52IST&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=Isaacson,%20Carrie&rft.date=2022-08-04&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2022248166A1%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 |