Patents by Inventor Galina Nogin

Galina Nogin 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).

  • Predicting and utilizing variability of travel times in mapping services
    Patent number: 10175054
    Abstract: A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.
    Type: Grant
    Filed: April 10, 2015
    Date of Patent: January 8, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Dawn Woodard, Eric J. Horvitz, Galina Nogin, Paul B. Koch, David Racz, Moises Goldszmidt
  • PREDICTING AND UTILIZING VARIABILITY OF TRAVEL TIMES IN MAPPING SERVICES
    Publication number: 20160202074
    Abstract: A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.
    Type: Application
    Filed: April 10, 2015
    Publication date: July 14, 2016
    Inventors: Dawn Woodard, Eric J. Horvitz, Galina Nogin, Paul B. Koch, David Racz, Moises Goldszmidt