# US 12,182,832 B2 · Selecting a virtual credit card for purchasing products from a merchant from user account information > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,182,832 B2 - Document ID: US12182832B2 - Status: Granted - Kind: B2 - Application number: 17/332,122 - Filed: 2021-05-27 - Published: 2021-12-02 - Granted: 2024-12-31 - Earliest claimed: 2020-05-27 - Assignees: PayCertify, Inc. - Applicants: PayCertify, Inc. - Inventors: Robert Chase Harmer - Web page: https://patentgrep.com/patent/US12182832B2 ## CPC - G06Q 30/0215: {Including financial accounts} - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06Q 20/227: {characterised in that multiple accounts are available, e.g. to the payer} - G06Q 20/24: Credit schemes, i.e. "pay after" - G06Q 20/26: Debit schemes, e.g. "pay now" - G06Q 20/351: {Virtual cards} - G06Q 20/387: {Payment using discounts or coupons (relating to marketing G06Q30/02)} - G07F 17/0035: {Participation in a loyalty or discount scheme (data processing for shopping and discounts G06Q30/00)} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G07F: COIN-FREED OR LIKE APPARATUS (coin sorting G07D3/00; coin testing G07D5/00; {handling coins or paper currencies apart from payment activated apparatus G07D; payment architectures, schemes or protocols G06Q20/00}) ## Abstract An online system receives financial account information for a user, such as debit card information, from a merchant system with which the user places an order. From the financial account information, the online system selects a virtual credit card providing one or more rewards, such as cashback, to the user. Based on characteristics of the order by the user, the online system identifies one or more offers, such as discounts or rebates, applicable to the order. The online system provides the virtual credit card information to the merchant system and stores the virtual credit card information, as well as information describing one or more identified offers for the order in association with the user and the virtual credit card, in an account maintained for the user. Rewards may be stored in the user's account at the online system for application when using the virtual credit card associated with the user. ## Claims ### Claim 1 A computer-implement method for switching data transmission rails in a real-time web browsing session, the computer-implemented method comprising: receiving data regarding prior data transmission transactions of a user, each prior data transmission transaction of the user corresponding to the user's usage of a particular data transmission rail, a data transmission rail being an architecture of how data is transmitted over a network; training a machine learning model using supervised learning based on the prior data transmission transactions to determine probabilities of the user completing web browsing sessions, each web browsing sessions being a transaction involving an order and completing a web browsing session comprising completing the transaction, the machine learning model trained from the prior data transmission transactions and outputting a probability of the user completing a particular web browsing session; receiving, at an online system during the real-time web browsing session, information describing a real-time data transmission transaction, information identifying the user, and account information of a first transaction account for the user, wherein the first transaction account is associated with a first data transmission rail, the real-time data transmission transaction being in progress during the real-time web browsing session and being adapted to use the first data transmission rail to transmit data, wherein the real-time data transmission transaction includes data that is fixed at a checkout page of the web browsing session, and the first transaction account is managed by a first institution; applying, in real time during the real-time web browsing session, the machine learning model to the information describing the data that is fixed at the checkout page to identify an alternative transaction that is associated with a second data transmission rail, wherein the machine learning model predicts, in real time during the real-time web browsing session, the alternative transaction is associated with at least a threshold probability of having the alternative transaction to be completed by the user, wherein the machine learning model accounts for time when the user previously completed transactions that are fixed at a checkout page based on the frequency with which certain data were included in the prior data transmission transactions, and time when the prior data transmission transactions were previously received; identifying, in real time during the real-time web browsing session, a second transaction account that is associated with the alternative transaction identified by the machine learning model, the alternative transaction associated with a second data transmission rail, wherein the second transaction account is selected to replace the first transaction account and is associated with a second institution, the second transaction account providing a reward to the user and the reward is increased compared to using the first transaction account to complete the transaction, and wherein identifying the second transaction account comprises: receiving information describing the data that are fixed at the checkout page; receiving reward program information of a plurality of candidate transaction accounts; and using the information describing the data that are fixed at the checkout page and the reward program information to select one of the candidate transaction accounts; presenting, by the online system and in real time during the real-time web browsing session, the second transaction account associated with the second data transmission rail to the user; receiving, by the online system in real time during the real-time web browsing session, a selection of the second transaction account from the user; storing, at the online system, an association between an identifier of the user, the second transaction account, and the alternative transaction; transmitting, in real time during the real-time web browsing session, information describing the second transaction account from the online system to the second institution via the second data transmission rail that replaces the first data transmission rail. ### Claim 2 (depends on 1) The method of claim 1, wherein identifying the second transaction account to replace the first transaction account comprises: determining a category from the received information; and selecting a virtual credit card providing a maximum reward to the user for purchases made through merchant systems having the determined category. ### Claim 3 (depends on 2) The method of claim 2, wherein the category of the merchant system comprises a merchant category code included in the received information. ### Claim 4 (depends on 1) The method of claim 1, wherein the candidate transaction accounts are further identified by: identifying one or more geographic locations relevant to the order from the received information; and identifying one or more offers having criteria satisfied by a geographic location relevant to the order. ### Claim 5 (depends on 4) The method of claim 4, wherein the geographic location relevant to the order comprises a geographic location of a merchant system. ### Claim 6 (depends on 4) The method of claim 4, wherein the geographic location relevant to the order comprises a geographic location of the user. ### Claim 7 (depends on 1) The method of claim 1, wherein applying, in real time during the real-time web browsing session, the machine learning model to the information describing the data that is fixed at the checkout page to identify the alternative transaction comprises: retrieving information stored by the online system describing previously received orders identifying the user; identifying one or more candidate products from the previously received orders and from the order; applying the machine learning model to each candidate product, the machine learning model outputting a probability of the user purchasing a candidate product; and identifying offers relevant to one or more candidate products based on the probabilities of the user purchasing candidate products. ### Claim 8 (depends on 7) The method of claim 7, wherein identifying the offers relevant to one or more candidate products based on the probabilities of the user purchasing candidate products comprises: identifying an offer relevant to a candidate product having at least a threshold probability of being purchased by the user. ### Claim 9 (depends on 7) The method of claim 7, wherein identifying the offers relevant to one or more candidate products based on the probabilities of the user purchasing candidate products comprises: ranking the candidate products based on the probabilities of the user purchasing candidate products; identifying an offer relevant to a candidate product having at least a threshold position in the ranking. ### Claim 10 (depends on 7) The method of claim 7, further comprising: receiving at the online system, an additional order stored in association with the user identifying the user and including a candidate product for which an identified offer is relevant; and transmitting information describing the identified offer to a merchant system from which the additional order was received in conjunction with information identifying the additional order and information identifying the user. ### Claim 11 (depends on 1) The method of claim 1, wherein transmitting, in real time during the real-time web browsing session, information describing the second transaction account from the online system to the second institution comprises: transmitting the information describing the selected second transaction account from the online system and information identifying at least one of the identified one or more offers. ### Claim 12 A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon, the instructions for determining a real-time selection during a web browsing session, wherein the instructions, when executed by a processor, cause the processor to: receive data regarding prior data transmission transactions of a user, each prior data transmission transaction of the user corresponding to the user's usage of a particular data transmission rail, a data transmission rail being an architecture of how data is transmitted over a network; train a machine learning model using supervised learning based on the prior data transmission transactions to determine probabilities of the user completing web browsing sessions, each web browsing sessions being a transaction involving an order and completing a web browsing session comprising completing the transaction, the machine learning model trained from the prior data transmission transactions and outputting a probability of the user completing a particular web browsing session; receive, at an online system during the real-time web browsing session, information describing a real-time data transmission transaction, information identifying the user, and account information of a first transaction account for the user, wherein the first transaction account is associated with a first data transmission rail, the real-time data transmission transaction being in progress during the real-time web browsing session and being adapted to use the first data transmission rail to transmit data, wherein the real-time data transmission transaction includes data that is fixed at a checkout page of the web browsing session, and the first transaction account is managed by a first institution; apply, in real time during the real-time web browsing session, the machine learning model to the information describing the data that is fixed at the checkout page to identify an alternative transaction that is associated with a second data transmission rail, wherein the machine learning model predicts, in real time during the real-time web browsing session, the alternative transaction is associated with at least a threshold probability of having the alternative transaction to be completed by the user, wherein the machine learning model accounts for time when the user previously completed transactions that are fixed at a checkout page based on the frequency with which certain data were included in the prior data transmission transactions, and time when the prior data transmission transactions were previously received; identify, in real time during the real-time web browsing session, a second transaction account that is associated with the alternative transaction identified by the machine learning model, the alternative transaction associated with a second data transmission rail, wherein the second transaction account is selected to replace the first transaction account and is associated with a second institution, the second transaction account providing a reward to the user and the reward is increased compared to using the first transaction account to complete the transaction, and wherein identifying the second transaction account comprises: receiving information describing the data that are fixed at the checkout page; receiving reward program information of a plurality of candidate transaction accounts; and using the information describing the data that are fixed at the checkout page and the reward program information to select one of the candidate transaction accounts; present, by the online system and in real time during the real-time web browsing session, the second transaction account associated with the second data transmission rail to the user; receive, by the online system in real time during the real-time web browsing session, a selection of the second transaction account from the user; store, at the online system, an association between an identifier of the user, the second transaction account, and the alternative transaction; transmit, in real time during the real-time web browsing session, information describing the second transaction account from the online system to the second institution via the second data transmission rail that replaces the first data transmission rail. ### Claim 13 (depends on 12) The computer program product of claim 12, wherein identify the second transaction account to replace the first transaction account comprises: determine a category from the received information; and select a virtual credit card providing a maximum reward to the user for purchases made through merchant systems having the determined category. ### Claim 14 (depends on 13) The computer program product of claim 13, wherein the category of the merchant system comprises a merchant category code included in the received information. ### Claim 15 (depends on 12) The computer program product of claim 12, wherein the candidate transaction accounts are further identified by: identify one or more geographic locations relevant to the order from the received information; and identify one or more offers having criteria satisfied by a geographic location relevant to the order. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the geographic location relevant to the order comprises a geographic location of a merchant system. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the geographic location relevant to the order comprises a geographic location of the user. ### Claim 18 (depends on 12) The computer program product of claim 12, wherein apply, in real time during the real-time web browsing session, the machine learning model to the information describing the data that is fixed at the checkout page to identify the alternative transaction comprises: retrieve information stored by the online system describing previously received orders identifying the user; identify one or more candidate products from the previously received orders and from the order; apply the machine learning model to each candidate product, the machine learning model outputting a probability of the user purchasing a candidate product; and identify offers relevant to one or more candidate products based on the probabilities of the user purchasing candidate products. ### Claim 19 (depends on 18) The computer program product of claim 18, wherein identify the offers relevant to one or more candidate products based on the probabilities of the user purchasing candidate products comprises: identify an offer relevant to a candidate product having at least a threshold probability of being purchased by the user. ### Claim 20 (depends on 18) The computer program product of claim 18, wherein identifying the offers relevant to one or more candidate products based on the probabilities of the user purchasing candidate products comprises: ranking the candidate products based on the probabilities of the user purchasing candidate products; identifying an offer relevant to a candidate product having at least a threshold position in the ranking. ### Claim 21 (depends on 18) The computer program product of claim 18, wherein the non-transitory computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: receive at the online system, an additional order stored in association with the user identifying the user and including a candidate product for which an identified offer is relevant; and transmit information describing the identified offer to a merchant system from which the additional order was received in conjunction with information identifying the additional order and information identifying the user. ### Claim 22 (depends on 12) The computer program product of claim 12, wherein the non-transitory computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: transmit the information describing the selected second transaction account from the online system and information identifying one or more offers to a merchant system.