# US 11,907,186 B2 · System and method for electronic data archival in a distributed data network

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- Number: US 11,907,186 B2
- Document ID: US11907186B2
- Status: Granted
- Kind: B2
- Application number: 17/725,591
- Filed: 2022-04-21
- Published: 2023-10-26
- Granted: 2024-02-20
- Earliest claimed: 2022-04-21
- Assignees: Bank of America Corporation
- Applicants: Bank of America Corporation
- Inventors: Raja Arumugam Maharaja
- Web page: https://patentgrep.com/patent/US11907186B2

## CPC

- G06F 16/212: {with details for data modelling support}
- G06F 16/219: {Managing data history or versioning (querying versioned data G06F16/2474; querying temporal data G06F16/2477)}
- G06F 16/2365: {Ensuring data consistency and integrity}
- G06F 16/258: {Data format conversion from or to a database}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Embodiments of the invention are directed to a system, method, or computer program product for an approach to electronic data archival in a distributed data network. The system allows for replicating and transmitting data for archival purposes from a source to a destination using a machine learning algorithm. The machine learning algorithm selectively distributes only data which has changed within a database, and thus prevents the necessity of repetitively archiving an entire database. The system categorizes changed data based on the characteristics of the data, and thereafter distributes the data via a distributed data network.

## Claims

### Claim 1

A system for electronic data archival in a distributed data network, the system comprising:
a memory device with computer-readable program code stored thereon;
a communication device;
a processing device operatively coupled to the memory device and the communication device, wherein the processing device is configured to execute the computer-readable program code to:
receive schema comprising data;
parse and divide the data into blocks;
create a distributed ledger and assign a separate distributed ledger for each schema owner, wherein a source and a destination each comprise a machine learning algorithm;
use the machine learning algorithm at the source to create and assign an assortment identifier to each block, wherein the assortment identifier is unchanged if there are no changes to the data in the block;
identify changes to the data in the schema based on changes to the assortment identifier and distribute data into categories using the machine learning algorithm at the source, the categories used by the machine learning algorithm to determine how to transmit the data, wherein the categories comprise:
a first category for receiving new data, wherein the new data is added to a new block, and the new block is added into the first category,
a second category for receiving attribute level changes, wherein a position indicator and the data to be changed are denoted and placed into the second category;
a third category for receiving small volume schema, wherein a generative adversarial network is implemented to convert the data to an image, and the image is placed into the third category; and
dynamically generated one or more additional categories, wherein the data is placed into the one or more additional categories; and
distribute the categories to the distributed ledger.

### Claim 2 (depends on 1)

The system of claim 1, wherein distributing the categories to the distributed ledger further comprises using the at least a portion of the machine learning algorithm to create triggers for initiating data movement wherein creating triggers for initiating data movement further comprises:
for the first category, using a data movement trigger to initiate data movement;
for the second category, using an update trigger to initiate data movement;
for the third category, using a generative adversarial network trigger configured to convert the image into data corresponding to the image and to initiate data movement; and
for the fourth category, using a dynamic trigger to initiate data movement.

### Claim 3 (depends on 2)

The system of claim 2, wherein the processing device is further configured to execute the computer-readable program code to:
load the data onto the distributed ledger; and
use the machine learning algorithm to initiate a count match between the source and the destination.

### Claim 4 (depends on 3)

The system of claim 3, wherein loading the data onto the distributed ledger further comprises a distributed ledger transaction, the distributed ledger transaction comprising a plurality of blocks, each block comprising a hash, a count, a data level information, a mismatch indicator, and the trigger.

### Claim 5 (depends on 2)

The system of claim 2, wherein the destination schema is evaluated by the machine learning algorithm to determine the location of where the data corresponding to the image is to be placed.

### Claim 6 (depends on 5)

The system of claim 5, wherein the image is split and the machine learning algorithm loads the data into the destination at the correct location within the schema.

### Claim 7

A computer program product for electronic data archival in a distributed data network, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:
receiving schema comprising data;
parsing and dividing the data into blocks;
creating a distributed ledger and assigning a separate distributed ledger for each schema owner, wherein a source and a destination each comprise a machine learning algorithm;
using the machine learning algorithm at the source to create and assign an assortment identifier to each block, wherein the assortment identifier is unchanged if there are no changes to the data in the block;
identifying changes to the data in the schema based on changes to the assortment identifier and distribute data into categories using the machine learning algorithm at the source, the categories used by the machine learning algorithm to determine how to transmit the data, wherein the categories comprise:
a first category for receiving new data, wherein the new data is added to a new block, and the new block is added into the first category;
a second category for receiving attribute level changes, wherein a position indicator and the data to be changed are denoted and placed into the second category;
a third category for receiving small volume schema, wherein a generative adversarial network is implemented to convert the data to an image, and the image is placed into the third category; and
dynamically generated one or more additional categories, wherein the data is placed into the one or more additional categories; and
distributing the categories to the distributed ledger.

### Claim 8 (depends on 7)

The computer program product of claim 7, wherein distributing the categories to the distributed ledger further comprises using the at least a portion of the machine learning algorithm to create triggers for initiating data movement wherein creating triggers for initiating data movement further comprises:
for the first category, using a data movement trigger to initiate data movement;
for the second category, using an update trigger to initiate data movement;
for the third category, using a generative adversarial network trigger configured to convert the image into data corresponding to the image and to initiate data movement; and
for the fourth category, using a dynamic trigger to initiate data movement.

### Claim 9 (depends on 8)

The computer program product of claim 8, wherein the processing device is further configured to execute the computer-readable program code to:
load the data onto the distributed ledger; and
use the machine learning algorithm to initiate a count match between the source and the destination.

### Claim 10 (depends on 9)

The computer program product of claim 9, wherein loading the data onto the distributed ledger further comprises a distributed ledger transaction, the distributed ledger transaction comprising a plurality of blocks, each block comprising a hash, a count, a data level information, a mismatch indicator, and the trigger.

### Claim 11 (depends on 8)

The computer program product of claim 8, wherein the destination schema is evaluated by the machine learning algorithm to determine the location of where the data corresponding to the image is to be placed.

### Claim 12 (depends on 11)

The computer program product of claim 11, wherein the image is split and the machine learning algorithm loads the data into the destination at the correct location within the schema.

### Claim 13

A computer-implemented method for electronic data archival in a distributed data network, the method comprising:
providing a computing system comprising a computer processing device and a non-transitory computer readable medium, where the non-transitory computer readable medium comprises configured computer program instruction code, such that when said computer program instruction code is operated by said computer processing device, said computer processing device performs the following operations:
receiving schema comprising data;
parsing and dividing the data into blocks;
creating a distributed ledger and assigning a separate distributed ledger for each schema owner, wherein a source and a destination each comprise a machine learning algorithm;
using the machine learning algorithm at the source to create and assign an assortment identifier to each block, wherein the assortment identifier is unchanged if there are no changes to the data in the block;
identifying changes to the data in the schema and distribute data into categories using the machine learning algorithm at the source, the categories used by the machine learning algorithm to determine how to transmit the data, wherein the categories comprise:
a first category for receiving new data, wherein the new data is added to a new block, and the new block is added into the first category;
a second category for receiving attribute level changes, wherein a position indicator and the data to be changed are denoted and placed into the second category;
a third category for receiving small volume schema, wherein a generative adversarial network is implemented to convert the data to an image, and the image is placed into the third category; and
dynamically generated one or more additional categories, wherein the data is placed into the one or more additional categories; and
distributing the categories to the distributed ledger.

### Claim 14 (depends on 13)

The computer-implemented method of claim 13, wherein distributing the categories to the distributed ledger further comprises using the at least a portion of the machine learning algorithm to create triggers for initiating data movement wherein creating triggers for initiating data movement further comprises:
for the first category, using a data movement trigger to initiate data movement;
for the second category, using an update trigger to initiate data movement;
for the third category, using a generative adversarial network trigger configured to convert the image into data corresponding to the image and to initiate data movement; and
for the fourth category, using a dynamic trigger to initiate data movement.

### Claim 15 (depends on 14)

The computer-implemented method of claim 14, wherein the computer processing device further performs the following operations:
loading the data onto the distributed ledger; and
using the machine learning algorithm to initiate a count match between the source and the destination.

### Claim 16 (depends on 14)

The computer-implemented method of claim 14, wherein the destination schema is evaluated by the machine learning algorithm to determine the location of where the data corresponding to the image is to be placed.

### Claim 17 (depends on 16)

The computer-implemented method of claim 16, wherein the image is split and the machine learning algorithm loads the data into the destination at the correct location within the schema.