Granted Patent B1

US 12,475,453 · App. 18/757,806 · Granted Nov 18, 2025

Inventor: Manu Kurian (Dallas, TX)

Assignee: Bank of America Corporation

G06Q20/382G06Q20/20G06Q20/40

Abstract

Claims (69)

1 . A system architecture for supporting bifurcated data transmission the system architecture comprising:

a point-of-sale (“POS”) device, the POS further comprises a memory storing instructions, when executed by a processor, causes the processor to perform operations comprising:

receiving a transaction request from a requestor;

breaking up the transaction request into micro-data, each micro-data including a tiny portion of the transaction request and a header that:

identifies the transaction request and a number that identifies a location of the micro-data within the transaction request, wherein compiling the micro-data in numerical order recreates the transaction request; and

sends the micro-data to a quantum processor for arranging the micro-data in a queue in a random order;

compiling a confirmatory data packet including data identifying the point-of-sale device and a total number of the micro-data; and

transmitting the transaction request to a central server by triggering, in parallel, a first and second data transfer wherein:

the first data transfer comprises transmitting the micro-data to a first IP address in the random order; and

the second data transfer comprises transmitting the confirmatory data packet to a second IP address;

the central server further comprising a central server memory storing central server instructions that, when executed by one or more central server processors, cause a first central server processor to perform operations comprising:

hosting the first IP address and the second IP address, the central server being configured to:

store the micro-data received by the first IP address in a temporary queue;

after receipt of a threshold number of micro-data, instructing a second central server processor to extract the data stored in the confirmatory data packet and execute a confirmatory routine, the confirmatory routine including confirming whether the point-of-sale device is authorized to execute a transaction via the central server; and

in response to a confirmation of the authorization of the central server to execute the transaction, instructing the first central server processor to:

generate a fillable template with a plurality of slots equal to the total number of micro-data stored in the confirmatory data packet, each slot being assigned a number corresponding a number stored in a header of a piece of micro-data;

initiate the re-creation of the transaction request by storing each piece of micro-data in the temporary queue in a slot having an assigned number equal to the number of the piece of micro-data;

after the generation of the fillable template performing, in parallel, the method steps of:

storing each additional micro-data received in the fillable template, each additional micro-data being stored in a slot having an assigned number equal to the number of the additional micro-data; and

execute an artificial intelligence (“AI”) algorithm to use training data to complete the fillable template by filling in slots not yet filled by micro-data; and

in response to the AI algorithm generating an AI-completed fillable template with a predicted accuracy level greater than a threshold accuracy level, processing the AI-completed fillable template as the transaction prior to having received all of the micro-data.

2 . The system architecture of claim 1 wherein the micro-data includes at least one thousand pieces of data.

3 . The system architecture of claim 1 wherein the micro-data includes a number of pieces of data that is greater than a threshold value.

4 . The system architecture of claim 1 wherein each piece of micro-data stores a portion of a string of code.

5 . The system architecture of claim 1 wherein the central server, in response to the second central server processor failing to confirm the authorization of the central server to execute the transaction, executes a force reject of the transaction request.

6 . The system architecture of claim 5 wherein the force reject includes:

instructing the first IP address to delete all stored micro-data received from the POS and quarantine any future micro-data received from the POS; and

instructing the second IP address to delete the confirmatory data set.

7 . The system architecture of claim 1 further comprising the first central server processor executing a routine to verify each micro-data before adding it to the temporary queue, wherein the verifying comprises determining if the micro-data includes corrupted or harmful data.

8 . The system architecture of claim 1 further comprising the first central server processor executing a routine to verify each micro-data before adding it to the fillable template, wherein the verifying comprises determining if the micro-data includes corrupted or harmful data.

9 . The system architecture of claim 1 further comprising the central server executing a machine learning algorithm to:

plot receipt times of each received micro-data;

generate an expected behavior of subsequent receipt times of micro-data; and

if receipt times of micro-data deviates from the expected behavior by a deviation amount, executes a force reject of the transaction.

10 . The system architecture of claim 1 further comprising:

receiving all of the micro-data prior to the AI algorithm generating an AI-completed fillable template, the received micro-data being used to fully populate the fillable template; and

executing the transaction based on the data stored in the fully populated fillable template.

11 . A method for supporting bifurcated data transmission, the method comprising:

receiving, a point-of-sale (“POS”) device, a transaction request from a requestor;

breaking up the transaction request into micro-data, each micro-data including a tiny portion of the transaction request and a header that:

identifies the transaction request and a number that identifies a location of the micro-data within the transaction request, wherein compiling the micro-data in numerical order recreates the transaction request; and

sends the micro-data to a quantum processor for arranging the micro-data in a queue in a random order;

compiling a confirmatory data packet including data identifying the point-of-sale device and a total number of the micro-data; and

transmitting the transaction request to a central server by triggering, in parallel, a first and second data transfer wherein:

the first data transfer comprises transmitting the micro-data to a first IP address in the random order; and

the second data transfer comprises transmitting the confirmatory data packet to a second IP address;

hosting, using the central server, the first IP address and the second IP address;

storing, using the central server, the micro-data received by the first IP address in a temporary queue;

after receipt of a threshold number of micro-data, instructing, using the central server to extract the data stored in the confirmatory data packet;

executing, using the central server, a confirmatory routine, the confirmatory routine including confirming whether the point-of-sale device is authorized to execute a transaction via the central server; and

in response to a confirmation of the authorization of the central server to execute the transaction, using the central server processor to instruct:

generate a fillable template with a plurality of slots equal to the total number of micro-data stored in the confirmatory data packet, each slot being assigned a number corresponding a number stored in a header of a piece of micro-data;

initiate the re-creation of the transaction request by storing each piece of micro-data in the temporary queue in a slot having an assigned number equal to the number of the piece of micro-data;

after the generation of the fillable template performing, using the central server, in parallel:

storing each additional micro-data received in the fillable template, each additional micro-data being stored in a slot having an assigned number equal to the number of the additional micro-data; and

executing an artificial intelligence (“AI”) algorithm to use training data to complete the fillable template by filling in slots not yet filled by micro-data; and

in response to the AI algorithm generating an AI-completed fillable template with a predicted accuracy level greater than a threshold accuracy level, the central server processing the AI-completed fillable template as the transaction prior to having received all of the micro-data.

12 . The method of claim 11 wherein the micro-data includes at least one thousand pieces of data.

13 . The method of claim 11 wherein the micro-data includes a number of pieces of data that is greater than a threshold value.

14 . The method of claim 11 wherein each piece of micro-data stores a portion of a string of code.

15 . The method of claim 11 further comprising using the central server processor executing a routine to verify each micro-data before adding it to the temporary queue, wherein the verifying comprises determining if the micro-data includes corrupted or harmful data.

16 . The method of claim 11 further comprising executing, using the central server, a routine to verify each micro-data before adding it to the fillable template, wherein the verifying comprises determining if the micro-data includes corrupted or harmful data.

17 . The method of claim 11 further comprising executing, using the central server, a machine learning algorithm to:

plot receipt times of each received micro-data;

generate an expected behavior of subsequent receipt times of micro-data; and

if receipt times of micro-data deviates from the expected behavior by a deviation amount, executing a forced reject of the transaction.

18 . The method of claim 11 further comprising:

receiving, using the central server, all of the micro-data prior to the AI algorithm generating an AI-completed fillable template, the received micro-data being used to fully populate the fillable template; and

executing, using the central server, the transaction based on the data stored in the fully populated fillable template.

Assignments (1)

References Cited (71)