CROSS-CHANNEL RECOMMENDATION PROCESSING
Cross-channel and cross-source data are aggregated into an aggregated data store. Custom segmentation is generated from the aggregated data. A campaign is monitored for the custom segmentation with successes and failures provided as dynamic feedback to a machine learning process that dynamically adj...
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| creator | Leung Ronald Chiwai Turner David Allen Tripathi Pragya Licht Yehoshua Zvi |
| description | Cross-channel and cross-source data are aggregated into an aggregated data store. Custom segmentation is generated from the aggregated data. A campaign is monitored for the custom segmentation with successes and failures provided as dynamic feedback to a machine learning process that dynamically adjusts the segmentation and the campaign for optimal performance. In an embodiment, a final recommendation is provided identifying a final optimal segmentation and campaign. |
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Custom segmentation is generated from the aggregated data. A campaign is monitored for the custom segmentation with successes and failures provided as dynamic feedback to a machine learning process that dynamically adjusts the segmentation and the campaign for optimal performance. In an embodiment, a final recommendation is provided identifying a final optimal segmentation and campaign.</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 DIGITAL DATA PROCESSING ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2017</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=20171102&DB=EPODOC&CC=US&NR=2017316435A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20171102&DB=EPODOC&CC=US&NR=2017316435A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Leung Ronald Chiwai</creatorcontrib><creatorcontrib>Turner David Allen</creatorcontrib><creatorcontrib>Tripathi Pragya</creatorcontrib><creatorcontrib>Licht Yehoshua Zvi</creatorcontrib><title>CROSS-CHANNEL RECOMMENDATION PROCESSING</title><description>Cross-channel and cross-source data are aggregated into an aggregated data store. Custom segmentation is generated from the aggregated data. A campaign is monitored for the custom segmentation with successes and failures provided as dynamic feedback to a machine learning process that dynamically adjusts the segmentation and the campaign for optimal performance. In an embodiment, a final recommendation is provided identifying a final optimal segmentation and campaign.</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 DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2017</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZFB3DvIPDtZ19nD083P1UQhydfb39XX1c3EM8fT3UwgI8nd2DQ729HPnYWBNS8wpTuWF0twMym6uIc4euqkF-fGpxQWJyal5qSXxocFGBobmxoZmJsamjobGxKkCAJC8JQQ</recordid><startdate>20171102</startdate><enddate>20171102</enddate><creator>Leung Ronald Chiwai</creator><creator>Turner David Allen</creator><creator>Tripathi Pragya</creator><creator>Licht Yehoshua Zvi</creator><scope>EVB</scope></search><sort><creationdate>20171102</creationdate><title>CROSS-CHANNEL RECOMMENDATION PROCESSING</title><author>Leung Ronald Chiwai ; Turner David Allen ; Tripathi Pragya ; Licht Yehoshua Zvi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2017316435A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2017</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 DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>Leung Ronald Chiwai</creatorcontrib><creatorcontrib>Turner David Allen</creatorcontrib><creatorcontrib>Tripathi Pragya</creatorcontrib><creatorcontrib>Licht Yehoshua Zvi</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Leung Ronald Chiwai</au><au>Turner David Allen</au><au>Tripathi Pragya</au><au>Licht Yehoshua Zvi</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>CROSS-CHANNEL RECOMMENDATION PROCESSING</title><date>2017-11-02</date><risdate>2017</risdate><abstract>Cross-channel and cross-source data are aggregated into an aggregated data store. Custom segmentation is generated from the aggregated data. A campaign is monitored for the custom segmentation with successes and failures provided as dynamic feedback to a machine learning process that dynamically adjusts the segmentation and the campaign for optimal performance. In an embodiment, a final recommendation is provided identifying a final optimal segmentation and campaign.</abstract><oa>free_for_read</oa></addata></record> |
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| 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 DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | CROSS-CHANNEL RECOMMENDATION PROCESSING |
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