Patents by Inventor Annette M. Taberner-Miller

Annette M. Taberner-Miller 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).

  • Scalable complex event processing with probabilistic machine learning models to predict subsequent geolocations
    Patent number: 11037072
    Abstract: Provided is a process, including: obtaining a set of historical geolocations; segmenting the historical geolocations into a plurality of temporal bins; determining pairwise transition probabilities between a set of geographic places based on the historical geolocations; configuring a compute cluster by assigning subsets of the transition probabilities to computing devices in the compute cluster; receiving a geolocation stream indicative of current geolocations of individuals; selecting a computing device in the compute cluster in response to determining that the computing device contain transition probabilities for the received respective geolocation; selecting transition probabilities applicable to the received respective geolocation from among the subset of transition probabilities assigned to the selected computing device; predicting a subsequent geographic place based on the selected transition probabilities.
    Type: Grant
    Filed: December 11, 2019
    Date of Patent: June 15, 2021
    Assignee: RETAILMENOT, INC.
    Inventors: David John Reese, Annette M. Taberner-Miller, Sankalp Acharya, Lipphei Adam
  • SCALABLE COMPLEX EVENT PROCESSING WITH PROBABILISTIC MACHINE LEARNING MODELS TO PREDICT SUBSEQUENT GEOLOCATIONS
    Publication number: 20200118031
    Abstract: Provided is a process, including: obtaining a set of historical geolocations; segmenting the historical geolocations into a plurality of temporal bins; determining pairwise transition probabilities between a set of geographic places based on the historical geolocations; configuring a compute cluster by assigning subsets of the transition probabilities to computing devices in the compute cluster; receiving a geolocation stream indicative of current geolocations of individuals; selecting a computing device in the compute cluster in response to determining that the computing device contain transition probabilities for the received respective geolocation; selecting transition probabilities applicable to the received respective geolocation from among the subset of transition probabilities assigned to the selected computing device; predicting a subsequent geographic place based on the selected transition probabilities.
    Type: Application
    Filed: December 11, 2019
    Publication date: April 16, 2020
    Inventors: David John Reese, Annette M. Taberner-Miller, Sankalp Acharya, Lipphei Adam
  • Scalable complex event processing with probabilistic machine learning models to predict subsequent geolocations
    Patent number: 10540611
    Abstract: Provided is a process, including: obtaining a set of historical geolocations; segmenting the historical geolocations into a plurality of temporal bins; determining pairwise transition probabilities between a set of geographic places based on the historical geolocations; configuring a compute cluster by assigning subsets of the transition probabilities to computing devices in the compute cluster; receiving a geolocation stream indicative of current geolocations of individuals; selecting a computing device in the compute cluster in response to determining that the computing device contain transition probabilities for the received respective geolocation; selecting transition probabilities applicable to the received respective geolocation from among the subset of transition probabilities assigned to the selected computing device; predicting a subsequent geographic place based on the selected transition probabilities.
    Type: Grant
    Filed: May 5, 2016
    Date of Patent: January 21, 2020
    Assignee: RetailMeNot, Inc.
    Inventors: David John Reese, Annette M. Taberner-Miller, Sankalp Acharya, Lipphei Adam
  • SCALABLE COMPLEX EVENT PROCESSING WITH PROBABILISTIC MACHINE LEARNING MODELS TO PREDICT SUBSEQUENT GEOLOCATIONS
    Publication number: 20160328661
    Abstract: Provided is a process, including: obtaining a set of historical geolocations; segmenting the historical geolocations into a plurality of temporal bins; determining pairwise transition probabilities between a set of geographic places based on the historical geolocations; configuring a compute cluster by assigning subsets of the transition probabilities to computing devices in the compute cluster; receiving a geolocation stream indicative of current geolocations of individuals; selecting a computing device in the compute cluster in response to determining that the computing device contain transition probabilities for the received respective geolocation; selecting transition probabilities applicable to the received respective geolocation from among the subset of transition probabilities assigned to the selected computing device; predicting a subsequent geographic place based on the selected transition probabilities.
    Type: Application
    Filed: May 5, 2016
    Publication date: November 10, 2016
    Inventors: David John Reese, Annette M. Taberner-Miller, Sankalp Acharya, Lipphei Adam