ADAPTIVE REMITTANCE LEARNING
The present disclosure involves systems, software, and computer implemented methods for a remittance system that pre-populates remittance data based on historical usage of remittance transactions. One example system includes operations to generate, using a predictive model, data indicating a predict...
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| creator | CHOW, ARTHUR CARROLL RASTOGI, KUSHANK LEE, JOHN JONG-SUK JAGGA, ARUN VICTOR GANDHI, RAJEEV KUMAR ROUHANI, ARMON MILLER, ROBERT KYLE DUNJIC, MILOS TRIPATHI, KAMANA KARBASI, MARYAM TAX, DAVID SAMUEL |
| description | The present disclosure involves systems, software, and computer implemented methods for a remittance system that pre-populates remittance data based on historical usage of remittance transactions. One example system includes operations to generate, using a predictive model, data indicating a predicted likelihood of a user selecting at least one data exchange transaction, wherein the data indicates the predicted likelihood of the user performing the at least one data exchange transaction. A request is received to access a remittance page. In response, the at least one data exchange transaction that was previously generated is selected from a repository of predicted likelihoods. Remittance data associated with a UI element is generated that includes the at least one data exchange transaction. The remittance data is transmitted to the device. An indication from the device is received for interacting with the UI element. The data exchange transaction is executed in response to receiving the indication. |
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One example system includes operations to generate, using a predictive model, data indicating a predicted likelihood of a user selecting at least one data exchange transaction, wherein the data indicates the predicted likelihood of the user performing the at least one data exchange transaction. A request is received to access a remittance page. In response, the at least one data exchange transaction that was previously generated is selected from a repository of predicted likelihoods. Remittance data associated with a UI element is generated that includes the at least one data exchange transaction. The remittance data is transmitted to the device. An indication from the device is received for interacting with the UI element. The data exchange transaction is executed in response to receiving the indication.</description><language>eng ; fre</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>2020</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=20200326&DB=EPODOC&CC=CA&NR=3018861A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76318</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200326&DB=EPODOC&CC=CA&NR=3018861A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>CHOW, ARTHUR CARROLL</creatorcontrib><creatorcontrib>RASTOGI, KUSHANK</creatorcontrib><creatorcontrib>LEE, JOHN JONG-SUK</creatorcontrib><creatorcontrib>JAGGA, ARUN VICTOR</creatorcontrib><creatorcontrib>GANDHI, RAJEEV KUMAR</creatorcontrib><creatorcontrib>ROUHANI, ARMON</creatorcontrib><creatorcontrib>MILLER, ROBERT KYLE</creatorcontrib><creatorcontrib>DUNJIC, MILOS</creatorcontrib><creatorcontrib>TRIPATHI, KAMANA</creatorcontrib><creatorcontrib>KARBASI, MARYAM</creatorcontrib><creatorcontrib>TAX, DAVID SAMUEL</creatorcontrib><title>ADAPTIVE REMITTANCE LEARNING</title><description>The present disclosure involves systems, software, and computer implemented methods for a remittance system that pre-populates remittance data based on historical usage of remittance transactions. 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One example system includes operations to generate, using a predictive model, data indicating a predicted likelihood of a user selecting at least one data exchange transaction, wherein the data indicates the predicted likelihood of the user performing the at least one data exchange transaction. A request is received to access a remittance page. In response, the at least one data exchange transaction that was previously generated is selected from a repository of predicted likelihoods. Remittance data associated with a UI element is generated that includes the at least one data exchange transaction. The remittance data is transmitted to the device. An indication from the device is received for interacting with the UI element. The data exchange transaction is executed in response to receiving the indication.</abstract><oa>free_for_read</oa></addata></record> |
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| language | eng ; fre |
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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 | ADAPTIVE REMITTANCE LEARNING |
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