# US 12,118,564 B2 · User behavior-based machine learning in entity account configuration > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,118,564 B2 - Document ID: US12118564B2 - Status: Granted - Kind: B2 - Application number: 17/941,861 - Filed: 2022-09-09 - Published: 2023-01-05 - Granted: 2024-10-15 - Earliest claimed: 2021-01-26 - Assignees: Zenpayroll, Inc. - Applicants: ZenPayroll, Inc. - Inventors: Kathy Lin; Matthew Castillon; Sahil Jolly; Elizabeth Lee Scanlon; Jeanette Quick; Yair Levin; David Tao; Namrata Jain; Chris Shen - Web page: https://patentgrep.com/patent/US12118564B2 ## CPC - G06Q 20/405: {Establishing or using transaction specific rules} - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06Q 20/02: involving a neutral party, e.g. certification authority, notary or trusted third party [TTP] - G06Q 20/102: {Bill distribution or payments} - G06Q 20/28: Pre-payment schemes, e.g. "pay before" - G06Q 40/02: Banking, e.g. interest calculation or account maintenance (credit or loans G06Q40/03) - G06Q 40/125: {Finance or payroll} - G06Q 10/1053: Employment or hiring - G06Q 10/1091: Recording time for administrative or management purposes - 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 flexible advance system allows users to request and receive advances instantly. The flexible advance system facilitates intra-system transfers between a third-party entity and employee accounts at a third-party system by generating and providing funding instructions to the third-party entity. Funding instructions include a funding amount that the flexible advance system predicts using one or more machine-learned models that account for seasonality and time delays. In executing the instructions from the flexible advance system, the third-party entity transfers funds to its entity account with the third-party system based on the funding amount. Once the flexible advance system receives an indication that the third-party entity has executed the instructions, the flexible advance system authorizes users of the flexible advance system to request short-term advances. The flexible advance system processes short-term advances such that corresponding funds are immediately transferred from the entity account of the third-party entity to employee accounts without delay. ## Claims ### Claim 1 A computer-implemented method comprising: training, by an online system, a machine-learned model by: initializing weights of the machine-learned model with an initial set of values to produce an initialized machine-learned model; accessing historical seasonality data from a plurality of time intervals; generating a plurality of training vectors based on the historical seasonality data, wherein each of the plurality of training vectors is associated with a time interval of the plurality of time intervals, and wherein each respective training vector is associated with a label indicating a usage amount of a respective time interval; for each of the plurality of training vectors, applying the initialized machine-learned model to the training vector to generate a prediction of a funding amount for the respective time interval based on the respective training vector; and updating the weights of the initialized machine-learned model based on 1) the predictions and the label associated with each of the training vectors until a difference between the predictions and the label associated with each of the training vectors is within a threshold difference and 2) a predicted usage associated with each of the plurality of time intervals and an actual usage associated with each of the plurality of time intervals to produce the machine-learned model. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising applying the machine-learned model to seasonality data associated with user accounts of a third-party system to predict a funding amount for a future time interval. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein an amount of funds equal to or greater than the predicted funding amount is transferred to an account associated with the third-party system in advance of the future time interval. ### Claim 4 (depends on 2) The computer-implemented method of claim 2, further comprising applying the machine-learned model to second seasonality data associated with the user accounts to predict a second funding amount for a second future time interval. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein a second amount of funds equal to or greater than the second funding amount is transferred to the account associated with the third-party system in advance of the second future time interval. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising retraining the machine-learned model based on a determination of whether predicted funding amounts are sufficient for an associated time interval. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein at least one training vector includes at a set of numerical values, wherein at least a first one of the numerical values represents an adoption value, and wherein at least a second one of the numerical values represents a buffer amount. ### Claim 8 A non-transitory computer-readable storage medium containing computer program code that, when executed by a hardware processor, causes the hardware processor to perform steps comprising: training, by an online system, a machine-learned model by: initializing weights of the machine-learned model with an initial set of values to produce an initialized machine-learned model; accessing historical seasonality data from a plurality of time intervals; generating a plurality of training vectors based on the historical seasonality data, wherein each of the plurality of training vectors is associated with a time interval of the plurality of time intervals, and wherein each respective training vector is associated with a label indicating a usage amount of a respective time interval; for each of the plurality of training vectors, applying the initialized machine-learned model to the training vector to generate a prediction of a funding amount for the respective time interval based on the respective training vector; and updating the weights of the initialized machine-learned model based on 1) the predictions and the label associated with each of the training vectors until a difference between the predictions and the label associated with each of the training vectors is within a threshold difference and 2) a predicted usage associated with each of the plurality of time intervals and an actual usage associated with each of the plurality of time intervals to produce the machine-learned model. ### Claim 9 (depends on 8) The non-transitory computer-readable storage medium of claim 8, further comprising code that, when executed by the hardware processor, cause the hardware processor to perform steps comprising applying the machine-learned model to seasonality data associated with user accounts of a third-party system to predict a funding amount for a future time interval. ### Claim 10 (depends on 9) The non-transitory computer-readable storage medium of claim 9, wherein an amount of funds equal to or greater than the predicted funding amount is transferred to an account associated with the third-party system in advance of the future time interval. ### Claim 11 (depends on 9) The non-transitory computer-readable storage medium of claim 9, further comprising code that, when executed by the hardware processor, cause the hardware processor to perform steps comprising applying the machine-learned model to second seasonality data associated with the user accounts to predict a second funding amount for a second future time interval. ### Claim 12 (depends on 11) The non-transitory computer-readable storage medium of claim 11, wherein a second amount of funds equal to or greater than the second funding amount is transferred to the account associated with the third-party system in advance of the second future time interval. ### Claim 13 (depends on 8) The non-transitory computer-readable storage medium of claim 8, further comprising code that, when executed by the hardware processor, cause the hardware processor to perform steps comprising retraining the machine-learned model based on a determination of whether predicted funding amounts are sufficient for an associated time interval. ### Claim 14 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein at least one training vector includes at a set of numerical values, wherein at least a first one of the numerical values represents an adoption value, and wherein at least a second one of the numerical values represents a buffer amount. ### Claim 15 A system comprising: a hardware processor; and a non-transitory computer-readable medium containing instructions that, when executed by the hardware processor, cause the hardware processor to train a machine-learned model by: initializing weights of the machine-learned model with an initial set of values to produce an initialized machine-learned model; accessing historical seasonality data from a plurality of time intervals; generating a plurality of training vectors based on the historical seasonality data, wherein each of the plurality of training vectors is associated with a time interval of the plurality of time intervals, and wherein each respective training vector is associated with a label indicating a usage amount of a respective time interval; for each of the plurality of training vectors, applying the initialized machine-learned model to the training vector to generate a prediction of a funding amount for the respective time interval based on the respective training vector; and updating the weights of the initialized machine-learned model based on 1) the predictions and the label associated with each of the training vectors until a difference between the predictions and the label associated with each of the training vectors is within a threshold difference and 2) a predicted usage associated with each of the plurality of time intervals and an actual usage associated with each of the plurality of time intervals to produce the machine-learned model. ### Claim 16 (depends on 15) The system of claim 15, further comprising instructions that cause the hardware processor to perform further steps comprising applying the machine-learned model to seasonality data associated with user accounts of a third-party system to predict a funding amount for a future time interval. ### Claim 17 (depends on 16) The system of claim 16, wherein an amount of funds equal to or greater than the predicted funding amount is transferred to an account associated with the third-party system in advance of the future time interval. ### Claim 18 (depends on 16) The system of claim 16, further comprising instructions that cause the hardware processor to perform further steps comprising applying the machine-learned model to second seasonality data associated with the user accounts to predict a second funding amount for a second future time interval. ### Claim 19 (depends on 18) The system of claim 18, wherein a second amount of funds equal to or greater than the second funding amount is transferred to the account associated with the third-party system in advance of the second future time interval. ### Claim 20 (depends on 15) The system of claim 15, further comprising instructions that cause the hardware processor to perform further steps comprising retraining the machine-learned model based on a determination of whether predicted funding amounts are sufficient for an associated time interval.