17527626. COMPUTER-BASED SYSTEMS CONFIGURED FOR MACHINE LEARNING ASSISTED DATA REPLICATION AND METHODS OF USE THEREOF simplified abstract (Capital One Services, LLC)

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COMPUTER-BASED SYSTEMS CONFIGURED FOR MACHINE LEARNING ASSISTED DATA REPLICATION AND METHODS OF USE THEREOF

Organization Name

Capital One Services, LLC

Inventor(s)

Ebrima N. Ceesay of McLean VA (US)

Hrishikesh Mukundan Menon of Glen Allen VA (US)

Mohamed Seck of Aubrey TX (US)

COMPUTER-BASED SYSTEMS CONFIGURED FOR MACHINE LEARNING ASSISTED DATA REPLICATION AND METHODS OF USE THEREOF - A simplified explanation of the abstract

This abstract first appeared for US patent application 17527626 titled 'COMPUTER-BASED SYSTEMS CONFIGURED FOR MACHINE LEARNING ASSISTED DATA REPLICATION AND METHODS OF USE THEREOF

Simplified Explanation

The abstract describes a patent application for systems and methods of data replication using machine learning techniques. Here is a simplified explanation of the abstract:

  • The patent application describes a computer-implemented method for data replication using a trained machine learning model.
  • The method involves identifying an existing object in a storage bucket for replication and determining a commencing time for replication based on predicted replication failure.
  • Once the existing object is identified, a snapshot of the bucket is captured, which includes information about the existing object, metadata, and access control list (ACL).
  • The existing object is then replicated to a destination cloud hosted at a cross-region storage, according to the determined commencing time.

Potential Applications:

  • This technology can be applied in cloud storage systems to ensure data replication and backup.
  • It can be used in disaster recovery scenarios to replicate data to a different region or cloud provider.
  • The machine learning techniques can be utilized in data centers to optimize replication processes and reduce replication failures.

Problems Solved:

  • The technology addresses the problem of replication failures by using machine learning to predict and determine the appropriate time for replication.
  • It solves the challenge of efficiently replicating large amounts of data by capturing snapshots and replicating only the necessary objects.

Benefits:

  • By using machine learning, the technology improves the accuracy of replication predictions, reducing the risk of data loss.
  • The ability to capture snapshots and replicate only specific objects reduces the replication time and resource requirements.
  • The cross-region storage in the destination cloud provides redundancy and ensures data availability in case of failures or disasters.


Original Abstract Submitted

Systems and methods of data replication via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: utilizing a trained replication machine learning model to identify an existing object in the bucket for replication, and a commencing time to replicate the existing object, the commencing time determined based on replication failure predicted by the replication machine learning model; capturing, in response to identifying the existing object for replication, a snapshot of the bucket, the snapshot comprising information related to at least one of: the existing object, metadata of the existing object, and/or an access control list (ACL) of the existing object; and replicating the existing object to a destination cloud according to the determined commencing time, the destination cloud being hosted at a cross-region storage.

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