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).
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Publication number: 20240028933Abstract: 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: ApplicationFiled: December 10, 2020Publication date: January 25, 2024Inventors: Christian Alexander Martine, Robert Oliver Burns Zeldin, Dinkar Jain, Jurgen Anne Francois Marie Van Gael, Anand Sumatilal Bhalgat, Tianshi Gao
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Patent number: 11348032Abstract: Machine-trained models are generated based on a model description that defines parameters for training the model and that can inherit parameters from parent model descriptions. When a parent model description changes, the changes made to the parent model description are applied to the model description automatically. When a target model is re-generated, a description of the set of parameters for generating the target model is received. The parent model is then identified from the received description, and a description of the set of parameters for generating the parent model is retrieved. Using the description for the target model and the parent model, a pipeline for generating the target model is generated. Finally, the pipeline is executed to generate the target model.Type: GrantFiled: September 2, 2018Date of Patent: May 31, 2022Assignee: Meta Platforms, Inc.Inventors: Jurgen Anne Francois Marie Van Gael, Yu Ning, Hao Shi, Fei Xie, Bingyue Peng, Shyamsundar Rajaram, Xin Liu, Zhen Yao, Peng Yang, Robert Oliver Burns Zeldin, Piyush Bansal
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Patent number: 10896380Abstract: 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: GrantFiled: August 30, 2017Date of Patent: January 19, 2021Assignee: Facebook, Inc.Inventors: Christian Alexander Martine, Robert Oliver Burns Zeldin, Dinkar Jain, Jurgen Anne Francois Marie Van Gael, Anand Sumatilal Bhalgat, Tianshi Gao
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Publication number: 20190065978Abstract: 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: ApplicationFiled: August 30, 2017Publication date: February 28, 2019Inventors: Christian Alexander Martine, Robert Oliver Burns Zeldin, Dinkar Jain, Jurgen Anne Francois Marie Van Gael, Anand Sumatilal Bhalgat, Tianshi Gao
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Patent number: 9256829Abstract: 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: GrantFiled: January 19, 2015Date of Patent: February 9, 2016Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
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Publication number: 20150134579Abstract: 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: ApplicationFiled: January 19, 2015Publication date: May 14, 2015Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
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Patent number: 8938407Abstract: 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: GrantFiled: June 17, 2013Date of Patent: January 20, 2015Assignee: Microsoft CorporationInventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
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Patent number: 8706653Abstract: 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: GrantFiled: December 8, 2010Date of Patent: April 22, 2014Assignee: Microsoft CorporationInventors: Gjergji Kasneci, Jurgen Anne Francois Marie Van Gael, Thore Kraepel, Ralf Herbrich, David Stern
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Patent number: 8589317Abstract: 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: GrantFiled: December 16, 2010Date of Patent: November 19, 2013Assignee: Microsoft CorporationInventors: Ulrich Paquet, David Stern, Jurgen Anne Francois Marie Van Gael, Ralf Herbrich
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Publication number: 20130282631Abstract: 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: ApplicationFiled: June 17, 2013Publication date: October 24, 2013Inventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
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Patent number: 8473437Abstract: 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: GrantFiled: December 17, 2010Date of Patent: June 25, 2013Assignee: Microsoft CorporationInventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
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Publication number: 20120158630Abstract: 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: ApplicationFiled: December 17, 2010Publication date: June 21, 2012Applicant: Microsoft CorporationInventors: Tauhid Rashed Zaman, Jurgen Anne Francois Marie Van Gael, David Stern, Ralf Herbrich, Gilad Lotan
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Publication number: 20110066577Abstract: 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: ApplicationFiled: September 15, 2009Publication date: March 17, 2011Applicant: Microsoft CorporationInventors: Jurgen Anne Francois Marie Van Gael, Ralf Herbrich, Thore Graepel