Patents by Inventor Scott Gaffney

Scott Gaffney 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).

  • GRANULAR DATA FOR BEHAVIORAL TARGETING
    Publication number: 20140200999
    Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.
    Type: Application
    Filed: January 11, 2013
    Publication date: July 17, 2014
    Applicant: YAHOO! INC.
    Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
  • Bid optimization in search engine marketing
    Patent number: 8712832
    Abstract: Methods and apparatus are described for optimally allocating an online advertising budget for a search engine marketing (SEM) campaign among a fixed set of keywords.
    Type: Grant
    Filed: December 12, 2006
    Date of Patent: April 29, 2014
    Assignee: Yahoo! Inc.
    Inventors: Pavel Berkhin, Usama M. Fayyad, Scott Gaffney, Bassel Ojjeh, Rajesh Girish Parekh, Andrew Tomkins
  • Method and system for generating a linear machine learning model for predicting online user input actions
    Patent number: 8364627
    Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.
    Type: Grant
    Filed: January 31, 2011
    Date of Patent: January 29, 2013
    Assignee: Yahoo! Inc.
    Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
  • Method and System for Generating A Linear Machine Learning Model for Predicting Online User Input Actions
    Publication number: 20110131160
    Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.
    Type: Application
    Filed: January 31, 2011
    Publication date: June 2, 2011
    Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
  • Granular data for behavioral targeting using predictive models
    Patent number: 7921069
    Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.
    Type: Grant
    Filed: June 28, 2007
    Date of Patent: April 5, 2011
    Assignee: Yahoo! Inc.
    Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
  • Granular Data for Behavioral Targeting
    Publication number: 20090006363
    Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.
    Type: Application
    Filed: June 28, 2007
    Publication date: January 1, 2009
    Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
  • BID OPTIMIZATION IN SEARCH ENGINE MARKETING
    Publication number: 20080140489
    Abstract: Methods and apparatus are described for optimally allocating an online advertising budget for a search engine marketing (SEM) campaign among a fixed set of keywords.
    Type: Application
    Filed: December 12, 2006
    Publication date: June 12, 2008
    Applicant: YAHOO! INC.
    Inventors: Pavel Berkhin, Usama M. Fayyad, Scott Gaffney, Bassel Ojjeh, Rajesh Girish Parekh, Andrew Tomkins