Patents by Inventor Scott Enman

Scott Enman 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).

  • Avoiding sentiment model overfitting in a machine language model
    Patent number: 10496751
    Abstract: Provided are techniques for avoiding sentiment model overfitting in a machine language model. A current list of keywords in a current sentiment model can be updated to create a proposed list of keywords in a proposed sentiment model. Machine-generated sentiment results, based on the proposed sentiment model, are presented to identify model overfitting, without revising the current set of keywords. The proposed set of keywords can be edited, and when overfitting is not present, the current list of keywords is replaced by the proposed list of keywords.
    Type: Grant
    Filed: December 13, 2017
    Date of Patent: December 3, 2019
    Assignee: SALESFORCE.COM, INC.
    Inventors: Michael Jones, Scott Enman, Collin Chun-Kit Lee, David Campbell, Christopher John Nicholls
  • AVOIDING SENTIMENT MODEL OVERFITTING IN A MACHINE LANGUAGE MODEL
    Publication number: 20180101521
    Abstract: Provided are techniques for avoiding sentiment model overfitting in a machine language model. A current list of keywords in a current sentiment model can be updated to create a proposed list of keywords in a proposed sentiment model. Machine-generated sentiment results, based on the proposed sentiment model, are presented to identify model overfitting, without revising the current set of keywords. The proposed set of keywords can be edited, and when overfitting is not present, the current list of keywords is replaced by the proposed list of keywords.
    Type: Application
    Filed: December 13, 2017
    Publication date: April 12, 2018
    Inventors: Michael Jones, Scott Enman, Collin Chun-Kit Lee, David Campbell, Christopher John Nicholls
  • Avoiding sentiment model overfitting in a machine language model
    Patent number: 9881000
    Abstract: Provided are techniques for avoiding sentiment model overfitting in a machine language model. A current list of keywords in a current sentiment model can be updated to create a proposed list of keywords in a proposed sentiment model. Machine-generated sentiment results, based on the proposed sentiment model, are presented to identify model overfitting, without revising the current set of keywords. The proposed set of keywords can be edited, and when overfitting is not present, the current list of keywords is replaced by the proposed list of keywords.
    Type: Grant
    Filed: July 18, 2016
    Date of Patent: January 30, 2018
    Assignee: SALESFORCE.COM, INC.
    Inventors: Michael Jones, Scott Enman, Collin Chun-Kit Lee, David Campbell, Christopher John Nicholls
  • AVOIDING SENTIMENT MODEL OVERFITTING IN A MACHINE LANGUAGE MODEL
    Publication number: 20180018321
    Abstract: Provided are techniques for avoiding sentiment model overfitting in a machine language model. A current list of keywords in a current sentiment model can be updated to create a proposed list of keywords in a proposed sentiment model. Machine-generated sentiment results, based on the proposed sentiment model, are presented to identify model overfitting, without revising the current set of keywords. The proposed set of keywords can be edited, and when overfitting is not present, the current list of keywords is replaced by the proposed list of keywords.
    Type: Application
    Filed: July 18, 2016
    Publication date: January 18, 2018
    Inventors: Michael Jones, Scott Enman, Collin Chun-kit Lee, David Campbell, Christopher John Nicholls