20240036928. AUTOMATIC MACHINE LEARNING-BASED PROCESSING WITH TEMPORALLY INCONSISTENT EVENTS simplified abstract (Capital One Services, LLC)

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AUTOMATIC MACHINE LEARNING-BASED PROCESSING WITH TEMPORALLY INCONSISTENT EVENTS

Organization Name

Capital One Services, LLC

Inventor(s)

Abhay Donthi of Arlington VA (US)

Tania Cruz Morales of Washington DC (US)

Jason Zwierzynski of Arlington VA (US)

Joshua Edwards of Philadelphia PA (US)

Jennifer Kwok of Brooklyn NY (US)

Sara Rose Brodsky of New York NY (US)

AUTOMATIC MACHINE LEARNING-BASED PROCESSING WITH TEMPORALLY INCONSISTENT EVENTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240036928 titled 'AUTOMATIC MACHINE LEARNING-BASED PROCESSING WITH TEMPORALLY INCONSISTENT EVENTS

Simplified Explanation

The embodiments described in this patent application aim to address the issue of resource insufficiency in a resource source, despite inconsistent resource accumulation. In a distributed computing environment, where computing resource sources/pools are present, a machine learning model is trained to predict times when a threshold amount of resources will be available at the resource source. Accumulation data, describing events when resources were made available, is obtained and used with the machine learning model to determine a request frequency for a specific request type. This allows the system to request resources in accordance with the predicted times of resource availability.

  • The patent application addresses the problem of inconsistent resource accumulation at a resource source.
  • It proposes the use of a machine learning model to predict times when a threshold amount of resources will be available.
  • Accumulation data is collected to train the machine learning model.
  • The system determines a request frequency based on the predicted times of resource availability.
  • Resources are requested in accordance with the determined request frequency.

Potential Applications

  • Distributed computing environments with resource sources/pools.
  • Cloud computing platforms.
  • Resource management systems.

Problems Solved

  • Resource insufficiency despite inconsistent accumulation.
  • Efficient resource allocation in distributed computing environments.
  • Predicting resource availability in advance.

Benefits

  • Improved resource utilization.
  • Reduced resource wastage.
  • Enhanced system performance and efficiency.
  • Better planning and allocation of resources.


Original Abstract Submitted

embodiments described herein reduce resource insufficiency of a resource source despite inconsistent resource accumulation at the resource source. for example, a request frequency may be determined to define times at which the resource source is predicted to be sufficient despite the inconsistent accumulation or influx. in one use case, with respect to a distributed computing environment having computing resource source(s)/pool(s), a requesting system may identify a machine learning model trained to generate predictions for a resource source at which inconsistent resource accumulation occurs. the system may obtain accumulation data that describes accumulation events at which resources were made available at the resource source. using the accumulation data with the machine learning model, the system may determine a request frequency for a request type based on predicted times at which a threshold amount of resources is available at the resource source and may request resources in accordance with the request frequency.

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