Granted Patent B2

US 12,737,679 · App. 18/093,459 · Granted Sep 15, 2026

Inventors: Yogi Ahuja (Warwick, PA); Geetha Dwarakapuram (Newtown, PA)

Assignee: BANK OF AMERICA CORPORATION

G06N20/00G06F16/2365

Abstract

Claims (52)

1 . A system for electronic compliance evaluation of transmitted object data via a machine learning model, the system comprising:

at least one non-transitory storage device; and

at least one processor coupled to the at least one non-transitory storage device, wherein the at least one processor is configured to:

receive a request to transfer data from a first position to a second position, the data stored in a repository;

transmit the data from the repository to a field scanning utility, wherein the field scanning utility comprises a machine learning model, the machine learning model configured to identify and remove non-compliant objects from the data, wherein the machine learning model is configured to receive regulatory status records, the regulatory status records comprising a regulatory status for each object coupled to corresponding objects, and wherein the regulatory status of each object has been determined by:

determining a processing unit characteristic for each at least one endpoint device:

transmitting the object to the at least one endpoint device, each of the at least one endpoint device receiving the object;

measuring a byte transfer efficiency of the transmission of the object to the at least one endpoint device using an application on the at least one endpoint device, wherein the byte transfer efficiency is measured for adequacy of object rendering according to a regulatory requirement for visually and/or auditory impaired persons;

transmitting the byte transfer efficiency to the first position;

forming an array comprising at least one block, each of the at least one block comprising the processing unit characteristic and the byte transfer efficiency corresponding to the processing unit characteristic;

assigning a compliance indicator, individually, to each of the at least one block, wherein the compliance indicator is a binary output corresponding to a comparison between the byte transfer efficiency and a target byte transfer efficiency, wherein achieving the target byte transfer efficiency indicates adhering to a regulation; and

determining a regulatory status for the array, wherein the regulatory status comprises a binary indicator of adherence to a regulatory compliance metric based on whether a predefined percentage of blocks in the array satisfy the target byte transfer efficiency;

transmit the non-compliant objects, using the field scanning utility, to a quarantine unit;

transmit a notification to at least one endpoint device, the notification structured to receive an input from a user, the input comprising a disposition of the non-compliant objects; and

transmit the data to the second position.

2 . The system of claim 1 , wherein identifying and removing non-compliant objects from the data occurs at a regular interval, and wherein the data remains in the repository after the field scanning utility identifies and removes the non-compliant objects from the data.

3 . The system of claim 1 , wherein an output of the machine learning model is dynamically altered as a result of a change to a data input to the at least one learning input.

4 . The system of claim 1 , wherein the compliance indicator is either a positive compliance indicator or a negative compliance indicator, the positive compliance indicator indicating the at least one block adhering to the regulation and the negative compliance indicator indicating the at least one block not adhering to the regulation.

5 . The system of claim 1 , wherein the compliance indicator is determined by conditional formatting of the block, the conditional formatting of the block configured to automatically assign a positive compliance indicator or a negative compliance indicator based on at least one selected from a group consisting of: a mathematical difference between the processing unit characteristic and the target processing unit characteristic, and a mathematical difference between the byte transfer efficiency and the target byte transfer efficiency.

6 . A computer program product for electronic compliance evaluation of transmitted object data via a machine learning model, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to:

receive a request to transfer data from a first position to a second position, the data stored in a repository;

transmit the data from the repository to a field scanning utility, wherein the field scanning utility comprises a machine learning model, the machine learning model configured to identify and remove non-compliant objects from the data, wherein the machine learning model is configured to receive regulatory status records, the regulatory status records comprising a regulatory status for each object coupled to corresponding objects, and wherein the regulatory status of each object has been determined by:

determining a processing unit characteristic for each at least one endpoint device;

transmitting the object to the at least one endpoint device, each of the at least one endpoint device receiving the object;

measuring a byte transfer efficiency of the transmission of the object to the at least one endpoint device using an application on the at least one endpoint device wherein the byte transfer efficiency is measured for adequacy of object rendering according to a regulatory requirement for visually and/or auditory impaired persons;

transmitting the byte transfer efficiency to the first position;

forming an array comprising at least one block, each of the at least one block comprising the processing unit characteristic and the byte transfer efficiency corresponding to the processing unit characteristic;

assigning a compliance indicator, individually, to each of the at least one block, wherein the compliance indicator is a binary output corresponding to a comparison between the byte transfer efficiency and a target byte transfer efficiency, wherein achieving the target byte transfer efficiency indicates adhering to a regulation; and

determining a regulatory status for the array, wherein the regulatory status comprises a binary indicator of adherence to a regulatory compliance metric based on whether a predefined percentage of blocks in the array satisfy the target byte transfer efficiency;

transmit the non-compliant objects, using the field scanning utility, to a quarantine unit;

transmit a notification to at least one endpoint device, the notification structured to receive an input from a user, the input comprising a disposition of the non-compliant objects; and

transmit the data to the second position.

7 . The computer program product of claim 6 , wherein identifying and removing non-compliant objects from the data occurs at a regular interval, and wherein the data remains in the repository after the field scanning utility identifies and removes the non-compliant objects from the data.

8 . The computer program product of claim 6 , wherein an output of the machine learning model is dynamically altered as a result of a change to a data input to the at least one learning input.

9 . The computer program product of claim 6 , wherein the compliance indicator is either a positive compliance indicator or a negative compliance indicator, the positive compliance indicator indicating the at least one block adhering to the regulation and the negative compliance indicator indicating the at least one block not adhering to the regulation.

10 . The computer program product of claim 6 , wherein the compliance indicator is determined by conditional formatting of the block, the conditional formatting of the block configured to automatically assign a positive compliance indicator or a negative compliance indicator based on at least one selected from a group consisting of: a mathematical difference between the processing unit characteristic and the target processing unit characteristic, and a mathematical difference between the byte transfer efficiency and the target byte transfer efficiency.

11 . A method for electronic compliance evaluation of transmitted object data via a machine learning model, the method comprising:

receiving a request to transfer data from a first position to a second position, the data stored in a repository;

transmitting the data from the repository to a field scanning utility, wherein the field scanning utility comprises a machine learning model, the machine learning model configured to identify and remove non-compliant objects from the data, wherein the machine learning model is configured to receive regulatory status records, the regulatory status records comprising a regulatory status for each object coupled to corresponding objects, and wherein the regulatory status of each object has been determined by:

determining a processing unit characteristic for each at least one endpoint device;

transmitting the object to the at least one endpoint device, each of the at least one endpoint device receiving the object;

measuring a byte transfer efficiency of the transmission of the object to the at least one endpoint device using an application on the at least one endpoint device, wherein the byte transfer efficiency is measured for adequacy of object rendering according to a regulatory requirement for visually and/or auditory impaired persons;

transmitting the byte transfer efficiency to the first position;

forming an array comprising at least one block, each of the at least one block comprising the processing unit characteristic and the byte transfer efficiency corresponding to the processing unit characteristic;

assigning a compliance indicator, individually, to each of the at least one block, wherein the compliance indicator is a binary output corresponding to a comparison between the byte transfer efficiency and a target byte transfer efficiency, wherein achieving the target byte transfer efficiency indicates adhering to a regulation; and

determining a regulatory status for the array, wherein the regulatory status comprises a binary indicator of adherence to a regulatory compliance metric based on whether a predefined percentage of blocks in the array satisfy the target byte transfer efficiency;

transmitting the non-compliant objects, using the field scanning utility, to a quarantine unit;

transmit a notification to at least one endpoint device, the notification structured to receive an input from a user, the input comprising a disposition of the non-compliant objects; and

transmitting the data to the second position.

12 . The method of claim 11 , wherein identifying and removing non-compliant objects from the data occurs at a regular interval, and wherein the data remains in the repository after the field scanning utility identifies and removes the non-compliant objects from the data.

13 . The method of claim 11 , wherein the compliance indicator is either a positive compliance indicator or a negative compliance indicator, the positive compliance indicator indicating the at least one block adhering to the regulation and the negative compliance indicator indicating the at least one block not adhering to the regulation.

14 . The method of claim 11 , wherein the compliance indicator is determined by conditional formatting of the block, the conditional formatting of the block configured to automatically assign a positive compliance indicator or a negative compliance indicator based on at least one selected from a group consisting of: a mathematical difference between the processing unit characteristic and the target processing unit characteristic, and a mathematical difference between the byte transfer efficiency and the target byte transfer efficiency.

Assignments (1)

Continuity (1)

References Cited (26)