US 12,260,413 B2
System and method for expedited data transfer utilizing a secondary electronic data log
Paul Martin Mattison, Sherrills Ford, NC (US); Martha Sain McClellan, Lancaster, SC (US); and Yash Sharma, Delhi (IN)
Assigned to BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed by BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed on Feb. 17, 2022, as Appl. No. 17/674,329.
Prior Publication US 2023/0259946 A1, Aug. 17, 2023
Int. Cl. G06Q 20/40 (2012.01); G06Q 20/38 (2012.01)
CPC G06Q 20/403 (2013.01) [G06Q 20/389 (2013.01); G06Q 20/401 (2013.01); G06Q 20/405 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for expedited data transfer, the system comprising
at least one non-transitory storage device, and
at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to:
analyze an entity data log, wherein the entity data log comprises incoming data transactions for a group of users and outgoing data transactions for the group of users using a peer-to-peer transaction network and wherein the entity data log is stored on a distributed ledger;
forecast, using a machine learning algorithm, at least one user having a volume of data transactions;
forecast, using the machine learning algorithm, a predetermined time period, wherein the predetermined time period is based on data transactions during a defined timeframe;
determine the at least one user, from the group of users, having the volume of data transactions within the defined timeframe on the peer-to-peer transaction network via the machine learning algorithm to trigger a secondary data log on the distributed ledger;
receive a secondary data log request from the at least one user to utilize a secondary data log during the predetermined time period, wherein the secondary data log comprises a hash-graph distributed ledger and the secondary data log reduces entity log update frequencies;
determine, using the machine learning algorithm and a user preauthorized credit request, a preauthorized credit for the at least one user, based on the volume of data transactions within the defined timeframe;
forecast, using the machine learning algorithm, a time for transmitting the preauthorized credit to the secondary data log;
execute, via a resource distribution instrument, incoming data transactions for the at least one user and outgoing data transactions for the at least one user, wherein the resource distribution instrument comprises interactions via a mobile device;
maintain the secondary data log for the each of the at least one user during the predetermined time period, wherein the secondary data log comprises the incoming data transactions for the at least one user and the outgoing data transactions for the at least one user, wherein the secondary data log further comprises the preauthorized credit for the predetermined time period preventing real-time settlement for the volume of data transactions from the at least one user;
determine, upon conclusion of the predetermined time period, for the secondary data log, a sum of the outgoing data transactions and incoming data transactions; and
record the sum on the entity data log and allow a remaining net amount to remain on the entity data log for additional transactions without performing settlement, wherein the record is compressed on the distributed ledger creating a compressed chain, wherein the compressed chain comprises a compressed chain of ownership of an electronic digital certificate, wherein the compressed chain of ownership comprises 1% of the electronic digital certificate.