Patents by Inventor Jurgen Anne Francois Marie Van Gael

Jurgen Anne Francois Marie Van Gael 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).

  • Patent number: 10896380
    Abstract: A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).
    Type: Grant
    Filed: August 30, 2017
    Date of Patent: January 19, 2021
    Assignee: Facebook, Inc.
    Inventors: Christian Alexander Martine, Robert Oliver Burns Zeldin, Dinkar Jain, Jurgen Anne Francois Marie Van Gael, Anand Sumatilal Bhalgat, Tianshi Gao
  • Publication number: 20190065978
    Abstract: A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).
    Type: Application
    Filed: August 30, 2017
    Publication date: February 28, 2019
    Inventors: Christian Alexander Martine, Robert Oliver Burns Zeldin, Dinkar Jain, Jurgen Anne Francois Marie Van Gael, Anand Sumatilal Bhalgat, Tianshi Gao
  • Patent number: 9256829
    Abstract: One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network.
    Type: Grant
    Filed: January 19, 2015
    Date of Patent: February 9, 2016
    Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
  • Publication number: 20150134579
    Abstract: One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network.
    Type: Application
    Filed: January 19, 2015
    Publication date: May 14, 2015
    Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
  • Patent number: 8938407
    Abstract: One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network.
    Type: Grant
    Filed: June 17, 2013
    Date of Patent: January 20, 2015
    Assignee: Microsoft Corporation
    Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
  • Patent number: 8706653
    Abstract: Knowledge corroboration is described. In an embodiment many judges provide answers to many questions so that at least one answer is provided to each question and at least some of the questions have answers from more than one judge. In an example a probabilistic learning system takes features describing the judges or the questions or both and uses those features to learn an expertise of each judge. For example, the probabilistic learning system has a graphical assessment component which aggregates the answers in a manner which takes into account the learnt expertise in order to determine enhanced answers. In an example the enhanced answers are used for knowledge base clean-up or web-page classification and the learnt expertise is used to select judges for future questions. In an example the probabilistic learning system has a logical component that propagates answers according to logical relations between the questions.
    Type: Grant
    Filed: December 8, 2010
    Date of Patent: April 22, 2014
    Assignee: Microsoft Corporation
    Inventors: Gjergji Kasneci, Jurgen Anne Francois Marie Van Gael, Thore Kraepel, Ralf Herbrich, David Stern
  • Patent number: 8589317
    Abstract: Many computing scenarios involve the classification of content items within one or more categories. The content item set may be too large for humans to classify, but an automated classifier (e.g., an artificial neural network) may not be able to classify all content items with acceptable accuracy. Instead, the automated classifier may calculate a classification confidence while classifying respective content items. Content items having a low classification confidence may be sent to a human classifier, and may be added, along with the categories identified by the human classifier, to a training set. The automated classifier may then be retrained using the training set, thereby incrementally improving the classification confidence of the automated classifier while conserving the involvement of human classifiers. Additionally, human classifiers may be rewarded for classifying the content items, and the costs of such rewards may be considered while selecting content items for the training set.
    Type: Grant
    Filed: December 16, 2010
    Date of Patent: November 19, 2013
    Assignee: Microsoft Corporation
    Inventors: Ulrich Paquet, David Stern, Jurgen Anne Francois Marie Van Gael, Ralf Herbrich
  • Publication number: 20130282631
    Abstract: One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network.
    Type: Application
    Filed: June 17, 2013
    Publication date: October 24, 2013
    Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
  • Patent number: 8473437
    Abstract: One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network.
    Type: Grant
    Filed: December 17, 2010
    Date of Patent: June 25, 2013
    Assignee: Microsoft Corporation
    Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
  • Publication number: 20120158630
    Abstract: One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network.
    Type: Application
    Filed: December 17, 2010
    Publication date: June 21, 2012
    Applicant: Microsoft Corporation
    Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
  • Publication number: 20110066577
    Abstract: Machine learning using relational databases is described. In an embodiment a model of a probabilistic relational database is formed by augmenting relation schemas of a relational database with probabilistic attributes. In an example, the model comprises constraints introduced by linking the probabilistic attributes using factor statements. For example, a compiler translates the model into a factor graph data structure which may be passed to an inference engine to carry out machine learning. For example, this enables machine learning to be integrated with the data and it is not necessary to pre-process or reformat large scale data sets for a particular problem domain. In an embodiment a machine learning system for estimating skills of players in an online gaming environment is provided. In another example, a machine learning system for data mining of medical data is provided. In some examples, missing attribute values are filled using machine learning results.
    Type: Application
    Filed: September 15, 2009
    Publication date: March 17, 2011
    Applicant: Microsoft Corporation
    Inventors: Jurgen Anne Francois Marie Van Gael, Ralf Herbrich, Thore Graepel