# US 12,118,191 B2 · Graphical user interface providing entry manipulation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,118,191 B2 - Document ID: US12118191B2 - Status: Granted - Kind: B2 - Application number: 18/401,775 - Filed: 2024-01-02 - Published: 2024-06-06 - Granted: 2024-10-15 - Earliest claimed: 2022-12-01 - Assignees: Truist Bank - Applicants: Truist Bank - Inventors: Alexis Pastore - Web page: https://patentgrep.com/patent/US12118191B2 ## CPC - G06F 3/0484: for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range - G06F 3/0482: Interaction with lists of selectable items, e.g. menus - 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 40/03: Credit; Loans; Processing thereof - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06F 18/2321: using statistics or function optimisation, e.g. modelling of probability density functions - G06F 18/24143: {Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]} - G06F 18/24323: {Tree-organised classifiers} - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06Q 20/3221: {Access to banking information through M-devices} - G06Q 20/389: {Keeping log of transactions for guaranteeing non-repudiation of a transaction} - G06Q 20/405: {Establishing or using transaction specific rules} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - 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 ## Abstract A system and method for allowing a user to manage transactions in an online credit card application. The system includes a back-end server operating the online application and including a processor for processing data and information, a communications interface communicatively coupled to the processor, and a memory device storing data and executable code. When the code is executed, the processor can link one or more external bank accounts to the online application, provide a list of transactions that were made using the credit card, enable a user to select one or more of the transactions in the list to be paid independent of the other transactions, and enable the user to pay the selected transactions using the one or more external bank accounts. ## Claims ### Claim 1 A system for displaying a graphical user interface allowing entry manipulation, said system comprising: a back-end server operating an online application and including: at least one processor employing a machine learning model for processing data and information; a communications interface communicatively coupled to the at least one processor; and a memory device storing data and executable code that, when executed, causes the at least one processor to: link one or more external accounts to the online application; provide a list of entries associated with a specified instrument; enable a user to select one or more of the entries in the list to be satisfied independent of the other entries; and enable the user to satisfy the selected entries using the one or more external accounts, wherein the machine learning model is trained using unsupervised or semi-supervised training where none of the outputs or some of the outputs of the model are unknown, and wherein the machine learning model is further trained by comparing a generated output produced by the model in response to training data with a desired or correct output to generate an error that is communicated back through the model. ### Claim 2 (depends on 1) The system according to claim 1 wherein the external accounts are bank accounts, the entries are transactions, the specified instrument is a credit card and satisfy the entries is paying the transactions. ### Claim 3 (depends on 2) The system according to claim 2 wherein the at least one processor allows the user to rearrange the order of the transactions in the list as they appear on an application page. ### Claim 4 (depends on 2) The system according to claim 2 wherein the at least one processor allows the user to group or categorize the transactions in the list by transaction type. ### Claim 5 (depends on 2) The system according to claim 2 wherein the at least one processor identifies which transactions have been paid. ### Claim 6 (depends on 2) The system according to claim 2 wherein the at least one processor allows the user to independently select when to pay the transactions in the list. ### Claim 7 (depends on 2) The system according to claim 2 wherein the at least one processor allows the user to split an amount of the transaction to be paid. ### Claim 8 A system for managing transactions in an online credit card application, said system comprising: a back-end server operating the online application and including: at least one processor employing a machine learning model for processing data and information; a communications interface communicatively coupled to the at least one processor; and a memory device storing data and executable code that, when executed, causes the at least one processor to: link one or more external bank accounts to the online application; provide a list of transactions that were made using the credit card; enable a user to select one or more of the transactions in the list to be paid independent of the other transactions; and enable the user to pay the selected transactions using the one or more external bank accounts, wherein the machine learning model is trained using unsupervised or semi-supervised training where none of the outputs or some of the outputs of the model are unknown, and wherein the machine learning model is further trained by comparing a generated output produced by the model in response to training data with a desired or correct output to generate an error that is communicated back through the model. ### Claim 9 (depends on 8) The system according to claim 8 wherein the at least one processor allows the user to rearrange the order of the transactions in the list as they appear on an application page. ### Claim 10 (depends on 8) The system according to claim 8 wherein the at least one processor allows the user to group or categorize the transactions in the list by transaction type. ### Claim 11 (depends on 8) The system according to claim 8 wherein the at least one processor identifies which transactions have been paid. ### Claim 12 (depends on 8) The system according to claim 8 wherein the at least one processor allows the user to independently select when to pay the transactions in the list. ### Claim 13 (depends on 8) The system according to claim 8 wherein the at least one processor allows the user to split an amount of the transaction to be paid. ### Claim 14 A method for managing transactions in an online credit card application, said method comprising: providing a back-end server operating the online application and including: at least one processor employing a machine learning model for processing data and information; a communications interface communicatively coupled to the at least one processor; and a memory device storing data and executable code, said method causing the at least one processor to: link one or more external bank accounts to the online application; provide a list of transactions that were made using the credit card; enable a user to select one or more of the transactions in the list to be paid independent of the other transactions; and enable the user to pay the selected transactions using the one or more external bank accounts, wherein the machine learning model is trained using unsupervised or semi-supervised training where none of the outputs or some of the outputs of the model are unknown, and wherein the machine learning model is further trained by comparing a generated output produced by the model in response to training data with a desired or correct output to generate an error that is communicated back through the model. ### Claim 15 (depends on 14) The method according to claim 14 wherein the method causes the at least one processor to allow the user to rearrange the order of the transactions in the list as they appear on an application page. ### Claim 16 (depends on 14) The method according to claim 14 wherein the method causes the at least one processor to allow the user to group or categorize the transactions in the list by transaction type. ### Claim 17 (depends on 14) The method according to claim 14 wherein the method causes the at least one processor to identify which transactions have been paid. ### Claim 18 (depends on 14) The method according to claim 14 wherein the method causes the at least one processor to allow the user to independently select when to pay the transactions in the list. ### Claim 19 (depends on 14) The method according to claim 14 wherein the method causes the at least one processor to allow the user to split an amount of the transaction to be paid.