Patents by Inventor Thore Graepel
Thore Graepel 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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Patent number: 11093702Abstract: Checking and/or completing for data grids is described such as for grids having rows and columns of cells at least some of which contain data values such as numbers or categories. In various embodiments predictive probability distributions are obtained from an inference engine for one or more of the cells and the predictive probability distributions are used for various tasks such as to suggest values to complete blank cells, highlight cells having outlying values, identify potential errors, suggest corrections to potential errors, identify similarities between cells, identify differences between cells, cluster rows of the data grid, and other tasks. In various embodiments a graphical user interface displays a data grid and provides facilities for completing, error checking/correcting, and analyzing data in the data grid.Type: GrantFiled: June 22, 2012Date of Patent: August 17, 2021Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Thore Graepel, Filip Radlinski, Andrew Donald Gordon, Pushmeet Kohli, John Winn, Lucas Bordeaux, Yoram Bachrach
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Patent number: 10997512Abstract: Systems and methods for inferring user traits based on indirect questions. Indirect questions may be generated based on one or more triggers. The answers to the indirect questions provide cues to a system as to whether a user has one or more attributes associated with a trait. This information may be used to personalize a computing device.Type: GrantFiled: July 6, 2015Date of Patent: May 4, 2021Inventors: Margaret Novotny, Jacob Miller, William Wagner, Yelisaveta Pesenson, Aryn Shelander, Sheena Stevens, Claudio Russo, Thore Graepel, Andrew D. Gordon, Christopher E. Miles
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Patent number: 9727880Abstract: Predicting user responses to items is useful in many application domains, such as personalized information retrieval and recommendation systems. In an embodiment a contacts service identifies contacts of a target user and predictions are elicited from the contacts about the target user's response to an item. In various examples, the predictions are combined taking into account weights of the contacts to produce a prediction of the target user's response. For example, the response may be one or more of: a numerical rating, a word or phrase describing the targets user's opinion of the item and a word or phrase stating a reason that the target user holds the opinion. In examples, accuracy of the predictions is calculated after observing the target user's actual response. The accuracy may be used to calculate and display scores and rankings of the contact's prediction abilities and to update the weights of the contacts.Type: GrantFiled: October 25, 2011Date of Patent: August 8, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Mohammad Raza, Thore Graepel
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Publication number: 20160350667Abstract: Systems and methods for inferring user traits based on indirect questions. Indirect questions may be generated based on one or more triggers. The answers to the indirect questions provide cues to a system as to whether a user has one or more attributes associated with a trait. This information may be used to personalize a computing device.Type: ApplicationFiled: July 6, 2015Publication date: December 1, 2016Applicant: Microsoft Technology Licensing, LLCInventors: Margaret Novotny, Jacob Miller, William Wagner, Yelisaveta Pesenson, Aryn Shelander, Sheena Stevens, Claudio Russo, Thore Graepel, Andrew D. Gordon, Christopher E. Miles
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Patent number: 9413557Abstract: Online recommendations are tracked through a forwarding service. The forwarding service can provide such statistics to an ad service, which can provide incentives to the recommending user and a consuming user. Example incentives may include an accumulation of points by the recommending user, a discount to the consuming user if a purchase is made in response to the recommendation, etc. To determine how much of an incentive each participant in the recommendation flow receives, a graph is created to model the recommendation flow and incentives are allocated using a cooperative game description based on this graph that associates each participant with a power index that represents that participants share of the incentive.Type: GrantFiled: June 18, 2010Date of Patent: August 9, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Ralf Herbrich, Thore Graepel, Yoram Bachrach
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Patent number: 9104961Abstract: There is provided a method and system for modeling a data generating process. The method includes generating a dyadic Bayesian model including a pair of probabilistic functions representing a prior distribution and a sampling distribution, and modeling a data generating process based on the dyadic Bayesian model using observed data. The method includes generating a learner object for the dyadic Bayesian model. The method further includes training the dyadic Bayesian model with the learner object based on the observed data to produce a trained dyadic Bayesian model. The method also includes generating a posterior distribution over parameters based on the trained dyadic Bayesian model. The method also further includes generating a posterior predictive distribution based on the posterior distribution. The method also includes predicting an outcome of observable variables based on the posterior predictive distribution.Type: GrantFiled: October 8, 2012Date of Patent: August 11, 2015Assignee: Microsoft Technology Licensing, LLCInventors: Andrew D. Gordon, Thore Graepel, Aditya Nori, Sriram Rajamani, Johannes Borgstroem
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Publication number: 20150012378Abstract: A recommender system may he used to predict a user behavior that a user will give in relation to an item. In an embodiment such predictions are used to enable items to be recommended to users. For example, products may be recommended to customers, potential friends may be recommended to users of a social networking tool, organizations may be recommended to automated users or other items may be recommended to users. In an embodiment a memory stores a data structure specifying a bi-linear collaborative filtering model of user behaviors. In the embodiment an automated inference process may be applied to the data structure in order to predict a user behavior given information about a user and information about an item. For example, the user information comprises user features as well as a unique user identifier.Type: ApplicationFiled: July 8, 2014Publication date: January 8, 2015Inventors: Ralf Herbrich, Thore Graepel, David Stern
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Patent number: 8781915Abstract: A recommender system may be used to predict a user behavior that a user will give in relation to an item. In an embodiment such predictions are used to enable items to be recommended to users. For example, products may be recommended to customers, potential friends may be recommended to users of a social networking tool, organizations may be recommended to automated users or other items may be recommended to users. In an embodiment a memory stores a data structure specifying a bi-linear collaborative filtering model of user behaviors. In the embodiment an automated inference process may be applied to the data structure in order to predict a user behavior given information about a user and information about an item. For example, the user information comprises user features as well as a unique user identifier.Type: GrantFiled: October 17, 2008Date of Patent: July 15, 2014Assignee: Microsoft CorporationInventors: Ralf Herbrich, Thore Graepel, David Stern
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Publication number: 20140156571Abstract: Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics.Type: ApplicationFiled: February 4, 2014Publication date: June 5, 2014Applicant: Microsoft CorporationInventors: Philipp Hennig, David Stern, Thore Graepel, Ralf Herbrich
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Publication number: 20140101090Abstract: There is provided a method and system for modeling a data generating process. The method includes generating a dyadic Bayesian model including a pair of probabilistic functions representing a prior distribution and a sampling distribution, and modeling a data generating process based on the dyadic Bayesian model using observed data.Type: ApplicationFiled: October 8, 2012Publication date: April 10, 2014Applicant: Microsoft CorporationInventors: Andrew D. Gordon, Thore Graepel, Aditya Nori, Sriram Rajamani, Johannes Borgstroem
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Patent number: 8672764Abstract: Matchmaking processes at online game services often result in players having to wait unacceptably long times to receive a match or immediately receiving a poorly matched session. By using a matchmaking process which dynamically adapts a good balance is achieved between the quality of proposed matches (for example, in terms of how balanced, interesting and fun those matches are likely to be) and the waiting time for potential matches. A matchmaking threshold is specified. When a player seeks a match a waiting time is observed, for example, as to how long that player waits until starting a game or dropping out. Information about such waiting times is used to dynamically update the matchmaking threshold. The update is made on the basis of a relationship between information about the observed waiting time and a target waiting time. Further control may be achieved by using separate matchmaking thresholds and target waiting times for different game categories.Type: GrantFiled: March 29, 2007Date of Patent: March 18, 2014Assignee: Microsoft CorporationInventors: Thore Graepel, Ralf Herbrich, David Shaw
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Patent number: 8645298Abstract: Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics.Type: GrantFiled: October 26, 2010Date of Patent: February 4, 2014Assignee: Microsoft CorporationInventors: Philipp Hennig, David Stern, Thore Graepel, Ralf Herbrich
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Publication number: 20130346844Abstract: Checking and/or completing for data grids is described such as for grids having rows and columns of cells at least some of which contain data values such as numbers or categories. In various embodiments predictive probability distributions are obtained from an inference engine for one or more of the cells and the predictive probability distributions are used for various tasks such as to suggest values to complete blank cells, highlight cells having outlying values, identify potential errors, suggest corrections to potential errors, identify similarities between cells, identify differences between cells, cluster rows of the data grid, and other tasks. In various embodiments a graphical user interface displays a data grid and provides facilities for completing, error checking/correcting, and analyzing data in the data grid.Type: ApplicationFiled: June 22, 2012Publication date: December 26, 2013Applicant: MICROSOFT CORPORATIONInventors: Thore Graepel, Filip Radlinski, Andrew Donald Gordon, Pushmeet Kohli, John Winn, Lucas Bordeaux, Yoram Bachrach
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Patent number: 8433660Abstract: Managing a portfolio of experts is described where the experts may be for example, automated experts or human experts. In an embodiment a selection engine selects an expert from a portfolio of experts and assigns the expert to a specified task. For example, the selection engine has a Bayesian machine learning system which is iteratively updated each time an experts performance on a task is observed. For example, sparsely active binary task and expert feature vectors are input to the selection engine which maps those feature vectors to a multi-dimensional trait space using a mapping learnt by the machine learning system. In examples, an inner product of the mapped vectors gives an estimate of a probability distribution over expert performance. In an embodiment the experts are automated problem solvers and the task is a hard combinatorial problem such as a constraint satisfaction problem or combinatorial auction.Type: GrantFiled: December 1, 2009Date of Patent: April 30, 2013Assignee: Microsoft CorporationInventors: David Stern, Horst Cornelius Samulowitz, Ralf Herbrich, Thore Graepel
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Publication number: 20130103692Abstract: Predicting user responses to items is useful in many application domains, such as personalized information retrieval and recommendation systems. In an embodiment a contacts service identifies contacts of a target user and predictions are elicited from the contacts about the target user's response to an item. In various examples, the predictions are combined taking into account weights of the contacts to produce a prediction of the target user's response. For example, the response may be one or more of: a numerical rating, a word or phrase describing the targets user's opinion of the item and a word or phrase stating a reason that the target user holds the opinion. In examples, accuracy of the predictions is calculated after observing the target user's actual response. The accuracy may be used to calculate and display scores and rankings of the contact's prediction abilities and to update the weights of the contacts.Type: ApplicationFiled: October 25, 2011Publication date: April 25, 2013Applicant: MICROSOFT CORPORATIONInventors: Mohammad Raza, Thore Graepel
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Patent number: 8417650Abstract: Event prediction in dynamic environments is described. In an embodiment a prediction engine may use the learnt information to predict events in order to control a system such as for internet advertising, email filtering, fraud detection or other applications. In an example one or more variables exists for pre-specified features describing or associated with events and each variable is considered to have an associated weight and time stamp. For example, belief about each weight is represented using a probability distribution and a dynamics process is used to modify the probability distribution in a manner dependent on the time stamp for that weight. For example, the uncertainty about the associated variable's influence on prediction of future events is increased. Examples of different schedules for applying the dynamics process are given.Type: GrantFiled: January 27, 2010Date of Patent: April 9, 2013Assignee: Microsoft CorporationInventors: Thore Graepel, Joaquin Quinonero Candela, Thomas Ivan Borchert, Ralf Herbrich
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Patent number: 8374973Abstract: Reputation systems have been used to promote trust between participants in activities including online activities such as online market places. Existing online market places provide a reputation system which is a simple cumulative registry of user ratings on a given market place member. However, this simple system is open to abuse in situations where, for example, many positive ratings are given in a fraudulent manner. By modeling both reputation of participants and required reputation of participants an improved reputation system is provided. The required reputation may be thought of as a threshold, referred to herein as a required threshold, which may be used in determining how to update an indication of the reputation of a participant in the activity. The reputation system is able to learn information about required reputation and reputation of participants using an update process which is robust to participants who consistently give feedback of a particular type.Type: GrantFiled: March 29, 2007Date of Patent: February 12, 2013Assignee: Microsoft CorporationInventors: Ralf Herbrich, Thore Graepel, David Shaw
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Patent number: 8364612Abstract: 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: GrantFiled: September 15, 2009Date of Patent: January 29, 2013Assignee: Microsoft CorporationInventors: Jurgen Anne Francios Marie Van Gael, Ralf Herbrich, Thore Graepel
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Publication number: 20130024448Abstract: Document features or document ranking values can be associated with a distribution of values. Feature values, feature value coefficients, and/or document ranking values can be generated based on sampled values from the distribution of values. This can allow the relative ranking of a document to vary.Type: ApplicationFiled: July 21, 2011Publication date: January 24, 2013Applicant: MICROSOFT CORPORATIONInventors: RALF HERBRICH, WILLIAM RAMSEY, ANTOINE ATALLAH, THORE GRAEPEL, PAUL VIOLA
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Patent number: 8204838Abstract: A scalable clustering system is described. In an embodiment the clustering system is operable for extremely large scale applications where millions of items having tens of millions of features are clustered. In an embodiment the clustering system uses a probabilistic cluster model which models uncertainty in the data set where the data set may be for example, advertisements which are subscribed to keywords, text documents containing text keywords, images having associated features or other items. In an embodiment the clustering system is used to generate additional features for associating with a given item. For example, additional keywords are suggested which an advertiser may like to subscribe to. The additional features that are generated have associated probability values which may be used to rank those features in some embodiments. User feedback about the generated features is received and used to revise the feature generation process in some examples.Type: GrantFiled: April 10, 2009Date of Patent: June 19, 2012Assignee: Microsoft CorporationInventors: Anton Schwaighofer, Joaquin QuiƱonero Candela, Thomas Borchert, Thore Graepel, Ralf Herbrich