Patents by Inventor Jesse Rappaport

Jesse Rappaport 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).

  • Publication number: 20210256326
    Abstract: A previously trained classification model associated with the machine learning system is configured to process an input to generate i) a first prediction that represents a characteristic associated with the input, and ii) a representation of accuracy associated with the prediction. A retraining subsystem is configured to receive the input, the first prediction, and the representation of accuracy. The retraining subsystem processes the input to generate a prediction representing a characteristic. A sufficiency of certainty of the first prediction is determined based on at least the input, the first prediction, the measure of accuracy, and the second prediction. Based at least on the determined sufficiency the retraining subsystem causes the machine learning system to be automatically retrained, be retrained using the input with active learning or not retrained.
    Type: Application
    Filed: May 5, 2021
    Publication date: August 19, 2021
    Inventors: Matthew ZEILER, Jesse RAPPAPORT, Samuel DODGE, Michael GORMISH
  • Patent number: 11030492
    Abstract: A previously trained classification model associated with the machine learning system is configured to process an input to generate i) a first prediction that represents a characteristic associated with the input, and ii) a representation of accuracy associated with the prediction. A retraining subsystem is configured to receive the input, the first prediction, and the representation of accuracy. The retraining subsystem processes the input to generate a prediction representing a characteristic. A sufficiency of certainty of the first prediction is determined based on at least the input, the first prediction, the measure of accuracy, and the second prediction. Based at least on the determined sufficiency the retraining subsystem causes the machine learning system to be automatically retrained, be retrained using the input with active learning or not retrained.
    Type: Grant
    Filed: January 16, 2019
    Date of Patent: June 8, 2021
    Assignee: CLARIFAI, INC.
    Inventors: Matthew Zeiler, Jesse Rappaport, Samuel Dodge, Michael Gormish
  • Publication number: 20200226431
    Abstract: A previously trained classification model associated with the machine learning system is configured to process an input to generate i) a first prediction that represents a characteristic associated with the input, and ii) a representation of accuracy associated with the prediction. A retraining subsystem is configured to receive the input, the first prediction, and the representation of accuracy. The retraining subsystem processes the input to generate a prediction representing a characteristic. A sufficiency of certainty of the first prediction is determined based on at least the input, the first prediction, the measure of accuracy, and the second prediction. Based at least on the determined sufficiency the retraining subsystem causes the machine learning system to be automatically retrained, be retrained using the input with active learning or not retrained.
    Type: Application
    Filed: January 16, 2019
    Publication date: July 16, 2020
    Inventors: Matthew Zeiler, Jesse Rappaport, Samuel Dodge, Michael Gormish
  • Patent number: D672570
    Type: Grant
    Filed: September 23, 2011
    Date of Patent: December 18, 2012
    Inventor: Jesse Rappaport