Patents by Inventor Melissa Runfeldt

Melissa Runfeldt 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).

  • Recognition of biases in data and models
    Patent number: 10984283
    Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.
    Type: Grant
    Filed: September 10, 2019
    Date of Patent: April 20, 2021
    Assignee: salesforce.com, inc.
    Inventors: Sarah Aerni, Natalie Casey, Shubha Nabar, Melissa Runfeldt, Sara Beth Asher
  • RECOGNITION OF BIASES IN DATA AND MODELS
    Publication number: 20210073579
    Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.
    Type: Application
    Filed: September 10, 2019
    Publication date: March 11, 2021
    Inventors: Sarah Aerni, Natalie Casey, Shubha Nabar, Melissa Runfeldt, Sara Beth Asher