Patents by Inventor Elad TZOREFF

Elad TZOREFF has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Allocation of processing resources to processing nodes
    Patent number: 11824731
    Abstract: There is provided a computer implemented method of allocating processing resources for processing by processing nodes, comprising: training predictive models, each predictive model for a respective processing node, each predictive model trained on a training dataset comprising records, each record including a historical amount of processing resources allocated to the respective processing node and a ground truth label indicating historical processing outcomes, wherein each processing node exhibits diminishing returns of processing outcomes with increasing allocated processing resources, wherein each predictive model is implemented as a monotonically increasing function that reaches a saturation level, solving an optimization allocation problem using the predictive models to identify a respective amount of processing resources for allocation to each processing node that maximizes a total of processing outcomes for a predetermined total amount of processing resources, and generating instructions for allocation
    Type: Grant
    Filed: December 29, 2021
    Date of Patent: November 21, 2023
    Assignee: Salesforce, Inc.
    Inventors: Elad Tzoreff, Rafi Dalla Torre
  • ALLOCATION OF PROCESSING RESOURCES TO PROCESSING NODES
    Publication number: 20230208725
    Abstract: There is provided a computer implemented method of allocating processing resources for processing by processing nodes, comprising: training predictive models, each predictive model for a respective processing node, each predictive model trained on a training dataset comprising records, each record including a historical amount of processing resources allocated to the respective processing node and a ground truth label indicating historical processing outcomes, wherein each processing node exhibits diminishing returns of processing outcomes with increasing allocated processing resources, wherein each predictive model is implemented as a monotonically increasing function that reaches a saturation level, solving an optimization allocation problem using the predictive models to identify a respective amount of processing resources for allocation to each processing node that maximizes a total of processing outcomes for a predetermined total amount of processing resources, and generating instructions for allocation
    Type: Application
    Filed: December 29, 2021
    Publication date: June 29, 2023
    Applicant: salesforce.com, inc.
    Inventors: Elad TZOREFF, Rafi DALLA TORRE