Granted Patent B1

US 12,748,753 · App. 19/552,465 · Granted Sep 29, 2026

Inventor: Carmen D. Hearn (San Juan Capistrano, CA)

Assignee: EQUIFAX INC.

G06F16/24528G06F16/211G06F16/24535

Abstract

Claims (82)

1 . A computer-implemented method, in which one or more processing devices perform operations comprising:

receiving transfer operation data associated with a plurality of postponed transfer operations, each postponed transfer operation of the plurality of postponed transfer operations initiated at a respective initial time point;

performing a conversion process to convert the transfer operation data into a standardized format, the conversion process including operations comprising:

segmenting each postponed transfer operation of the plurality of postponed transfer operations into a respective set of sub-operations, each sub-operation of the respective set of sub-operations allocated a corresponding amount of protected resources, wherein at least one sub-operation of the respective set of sub-operations is scheduled at a time point subsequent to the respective initial time point;

determining, based on the transfer operation data, a respective status of each sub-operation of the respective set of sub-operations; and

encoding the respective status in association with each sub-operation of the respective set of sub-operations; and

generating a data structure comprising the transfer operation data in the standardized format, the data structure presenting the respective set of sub-operations in an ordered arrangement based on a respective scheduled time point of each sub-operation of the respective set of sub-operations.

2 . The computer-implemented method of claim 1 , further comprising:

determining, using a predictive model trained using a training process, an anomaly indicator for a target entity from predictor variables associated with the target entity, wherein the anomaly indicator indicates a likelihood of an adverse event occurring in association with the target entity, wherein the training process includes operations comprising:

accessing training vectors having a plurality of sets of training predictor variables and a plurality of training outputs corresponding to a respective set of training predictor variables, wherein at least a portion of the training vectors are generated using the transfer operation data in the data structure; and

performing iterative adjustments of parameters of the predictive model based on an optimization function of the predictive model; and

outputting the anomaly indicator for use in controlling access of the target entity to one or more interactive computing environments.

3 . The computer-implemented method of claim 1 , wherein the standardized format is a structured data format defined by a schema, and wherein the schema comprises a respective data type and a respective set of rules corresponding to each data entry of the data structure.

4 . The computer-implemented method of claim 1 , wherein encoding the respective status in association with each sub-operation of the respective set of sub-operations further comprises, for a particular sub-operation of the respective set of sub-operations:

determining, based on the transfer operation data, an outcome of the particular sub-operation;

selecting, from a set of predefined status indicators, a status indicator corresponding to the outcome; and

storing the status indicator in association with the particular sub-operation.

5 . The computer-implemented method of claim 1 , wherein generating the data structure comprising the transfer operation data in the standardized format further comprises:

executing a rule engine configured to:

select, for each postponed transfer operation of the plurality of postponed transfer operations, one or more rule sets based on a schema of the data structure; and

apply the one or more rule sets to encode the transfer operation data in the data structure in compliance with the schema.

6 . The computer-implemented method of claim 1 , further comprising:

encoding a null value in the data structure in association with a particular sub-operation of the respective set of sub-operations, wherein the particular sub-operation is scheduled to be executed at a future time point;

receiving additional transfer operation data generated subsequent to the future time point;

determining, based on the additional transfer operation data, a status of the particular sub-operation; and

updating the data structure by encoding the status in association with the particular sub-operation, wherein the status replaces the null value in the data structure.

7 . The computer-implemented method of claim 1 , wherein a time window between a scheduled execution of each sub-operation of the respective set of sub-operations is less than thirty days.

8 . A system comprising:

a processor; and

a memory in which instructions executable by the processor are stored to cause the processor to perform operations comprising:

receiving transfer operation data associated with a plurality of postponed transfer operations, each postponed transfer operation of the plurality of postponed transfer operations initiated at a respective initial time point;

performing a conversion process to convert the transfer operation data into a standardized format, the conversion process including operations comprising:

segmenting each postponed transfer operation of the plurality of postponed transfer operations into a respective set of sub-operations, each sub-operation of the respective set of sub-operations allocated a corresponding amount of protected resources, wherein at least one sub-operation of the respective set of sub-operations is scheduled at a time point subsequent to the respective initial time point;

determining, based on the transfer operation data, a respective status of each sub-operation of the respective set of sub-operations; and

encoding the respective status in association with each sub-operation of the respective set of sub-operations; and

generating a data structure comprising the transfer operation data in the standardized format, the data structure presenting the respective set of sub-operations in an ordered arrangement based on a respective scheduled time point of each sub-operation of the respective set of sub-operations.

9 . The system of claim 8 , wherein the operations further comprise:

determining, using a predictive model trained using a training process, an anomaly indicator for a target entity from predictor variables associated with the target entity, wherein the anomaly indicator indicates a likelihood of an adverse event occurring in association with the target entity, wherein the training process includes operations comprising:

accessing training vectors having a plurality of sets of training predictor variables and a plurality of training outputs corresponding to a respective set of training predictor variables, wherein at least a portion of the training vectors are generated using the transfer operation data in the data structure; and

performing iterative adjustments of parameters of the predictive model based on an optimization function of the predictive model; and

outputting the anomaly indicator for use in controlling access of the target entity to one or more interactive computing environments.

10 . The system of claim 8 , wherein the standardized format is a structured data format defined by a schema, and wherein the schema comprises a respective data type and a respective set of rules corresponding to each data entry of the data structure.

11 . The system of claim 8 , wherein encoding the respective status in association with each sub-operation of the respective set of sub-operations further comprises, for a particular sub-operation of the respective set of sub-operations:

determining, based on the transfer operation data, an outcome of the particular sub-operation;

selecting, from a set of predefined status indicators, a status indicator corresponding to the outcome; and

storing the status indicator in association with the particular sub-operation.

12 . The system of claim 8 , wherein generating the data structure comprising the transfer operation data in the standardized format further comprises:

executing a rule engine configured to:

select, for each postponed transfer operation of the plurality of postponed transfer operations, one or more rule sets based on a schema of the data structure; and

apply the one or more rule sets to encode the transfer operation data in the data structure in compliance with the schema.

13 . The system of claim 8 , wherein the operations further comprise:

encoding a null value in the data structure in association with a particular sub-operation of the respective set of sub-operations, wherein the particular sub-operation is scheduled to be executed at a future time point;

receiving additional transfer operation data generated subsequent to the future time point;

determining, based on the additional transfer operation data, a status of the particular sub-operation; and

updating the data structure by encoding the status in association with the particular sub-operation, wherein the status replaces the null value in the data structure.

14 . The system of claim 8 , wherein a time window between a scheduled execution of each sub-operation of the respective set of sub-operations is less than thirty days.

15 . A non-transitory computer-readable storage medium having program code that is executable by a processing device for causing the processing device to perform operations, the operations comprising:

receiving transfer operation data associated with a plurality of postponed transfer operations, each postponed transfer operation of the plurality of postponed transfer operations initiated at a respective initial time point;

performing a conversion process to convert the transfer operation data into a standardized format, the conversion process including operations comprising:

segmenting each postponed transfer operation of the plurality of postponed transfer operations into a respective set of sub-operations, each sub-operation of the respective set of sub-operations allocated a corresponding amount of protected resources, wherein at least one sub-operation of the respective set of sub-operations is scheduled at a time point subsequent to the respective initial time point;

determining, based on the transfer operation data, a respective status of each sub-operation of the respective set of sub-operations; and

encoding the respective status in association with each sub-operation of the respective set of sub-operations; and

generating a data structure comprising the transfer operation data in the standardized format, the data structure presenting the respective set of sub-operations in an ordered arrangement based on a respective scheduled time point of each sub-operation of the respective set of sub-operations.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise:

determining, using a predictive model trained using a training process, an anomaly indicator for a target entity from predictor variables associated with the target entity, wherein the anomaly indicator indicates a likelihood of an adverse event occurring in association with the target entity, wherein the training process includes operations comprising:

accessing training vectors having a plurality of sets of training predictor variables and a plurality of training outputs corresponding to a respective set of training predictor variables, wherein at least a portion of the training vectors are generated using the transfer operation data in the data structure; and

performing iterative adjustments of parameters of the predictive model based on an optimization function of the predictive model; and

outputting the anomaly indicator for use in controlling access of the target entity to one or more interactive computing environments.

17 . The non-transitory computer-readable storage medium of claim 15 , wherein the standardized format is a structured data format defined by a schema, and wherein the schema comprises a respective data type and a respective set of rules corresponding to each data entry of the data structure.

18 . The non-transitory computer-readable storage medium of claim 15 , wherein encoding the respective status in association with each sub-operation of the respective set of sub-operations further comprises, for a particular sub-operation of the respective set of sub-operations:

determining, based on the transfer operation data, an outcome of the particular sub-operation;

selecting, from a set of predefined status indicators, a status indicator corresponding to the outcome; and

storing the status indicator in association with the particular sub-operation.

19 . The non-transitory computer-readable storage medium of claim 15 , wherein generating the data structure comprising the transfer operation data in the standardized format further comprises:

executing a rule engine configured to:

select, for each postponed transfer operation of the plurality of postponed transfer operations, one or more rule sets based on a schema of the data structure; and

apply the one or more rule sets to encode the transfer operation data in the data structure in compliance with the schema.

20 . The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise:

encoding a null value in the data structure in association with a particular sub-operation of the respective set of sub-operations, wherein the particular sub-operation is scheduled to be executed at a future time point;

receiving additional transfer operation data generated subsequent to the future time point;

determining, based on the additional transfer operation data, a status of the particular sub-operation; and

updating the data structure by encoding the status in association with the particular sub-operation, wherein the status replaces the null value in the data structure.

References Cited (23)