Patents by Inventor Jennifer Listgarten

Jennifer Listgarten 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).

  • Publication number: 20210210168
    Abstract: In embodiments of latent space harmonization (LSH) for predictive modeling, different training data sets are obtained from different measurement methods, where input data among the training data sets is quantifiable in a common space but a mapping between output data among the training data sets is unknown. A LSH module receives the training data sets and maps a common supervised target variable of the output data to a shared latent space where the output data can be jointly yielded. Mappings from the shared latent space back to the output training data of each training data set are determined and used to generate a trained predictive model. The trained predictive model is useable to predict output data from new input data with improved predictive power from the training data obtained using various, otherwise incongruent, measurement techniques.
    Type: Application
    Filed: January 7, 2021
    Publication date: July 8, 2021
    Inventors: Nicolo Fusi, Jennifer Listgarten, Gregory Byer Darnell
  • Patent number: 10923213
    Abstract: In embodiments of latent space harmonization (LSH) for predictive modeling, different training data sets are obtained from different measurement methods, where input data among the training data sets is quantifiable in a common space but a mapping between output data among the training data sets is unknown. A LSH module receives the training data sets and maps a common supervised target variable of the output data to a shared latent space where the output data can be jointly yielded. Mappings from the shared latent space back to the output training data of each training data set are determined and used to generate a trained predictive model. The trained predictive model is useable to predict output data from new input data with improved predictive power from the training data obtained using various, otherwise incongruent, measurement techniques.
    Type: Grant
    Filed: December 2, 2016
    Date of Patent: February 16, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Nicolo Fusi, Jennifer Listgarten, Gregory Byer Darnell
  • Patent number: 10120975
    Abstract: This disclosure presents a model for identifying correlations in genome-wide association studies (GWAS) with function-valued traits that provides increased power and computational efficiency by use of a Gaussian process regression with radial basis function (RBF) kernels to model the function-valued traits and specialized factorizations to achieve speed. A Gaussian Process is assigned to each partition for each allele of a given single nucleotide polymorphism (SNP) which yields flexible alternative models and handles a large number of data points in a way that is statistically and computationally efficient. This model provides techniques for handling missing and unaligned function values such as would occur when not all individuals are measured at the same time points. If the data is complete algebraic re-factorization by decomposition into Kronecker products reduces the time complexity of this model thereby increasing processing speed and reducing memory usage as compared to a naive implementation.
    Type: Grant
    Filed: March 30, 2016
    Date of Patent: November 6, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Nicolo Fusi, Jennifer Listgarten
  • Publication number: 20180157794
    Abstract: In embodiments of latent space harmonization (LSH) for predictive modeling, different training data sets are obtained from different measurement methods, where input data among the training data sets is quantifiable in a common space but a mapping between output data among the training data sets is unknown. A LSH module receives the training data sets and maps a common supervised target variable of the output data to a shared latent space where the output data can be jointly yielded. Mappings from the shared latent space back to the output training data of each training data set are determined and used to generate a trained predictive model. The trained predictive model is useable to predict output data from new input data with improved predictive power from the training data obtained using various, otherwise incongruent, measurement techniques.
    Type: Application
    Filed: December 2, 2016
    Publication date: June 7, 2018
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Nicolo Fusi, Jennifer Listgarten, Gregory Byer Darnell
  • Publication number: 20170286593
    Abstract: This disclosure presents a model for identifying correlations in genome-wide association studies (GWAS) with function-valued traits that provides increased power and computational efficiency by use of a Gaussian process regression with radial basis function (RBF) kernels to model the function-valued traits and specialized factorizations to achieve speed. A Gaussian Process is assigned to each partition for each allele of a given single nucleotide polymorphism (SNP) which yields flexible alternative models and handles a large number of data points in a way that is statistically and computationally efficient. This model provides techniques for handling missing and unaligned function values such as would occur when not all individuals are measured at the same time points. If the data is complete algebraic re-factorization by decomposition into Kronecker products reduces the time complexity of this model thereby increasing processing speed and reducing memory usage as compared to a naive implementation.
    Type: Application
    Filed: March 30, 2016
    Publication date: October 5, 2017
    Inventors: Nicolo Fusi, Jennifer Listgarten
  • Publication number: 20170176956
    Abstract: A control system comprises an input configured to receive sensor data sensed from a target system to be controlled by the control system. The control system has an input-aware stacker, the input-aware stacker being a predictor; and a plurality of base predictors configured to compute base outputs from features of the sensor data. The input-aware stacker is input-aware in that it is configured to take as input the features as well as the base outputs to compute a prediction. The input-aware stacker is configured to compute the prediction from uncertainty data about the base outputs and/or from at least some combinations of the features of the sensor data. The control system has an output configured to send instructions to the target system on the basis of the computed prediction.
    Type: Application
    Filed: December 17, 2015
    Publication date: June 22, 2017
    Inventors: Nicolo Fusi, Jennifer Listgarten, Miriam Huntley
  • Publication number: 20140066320
    Abstract: Described herein are technologies pertaining to computationally-efficiently performing genome-wide association studies. Feature selection methods are used to identify genetic markers for addressing potential confounding in the data. Then, single SNPs, or groups of genetic markers are analyzed to ascertain whether such groups are causal or tagging of causal as to a specified phenotype, after taking in to account the feature-selected SNPs. Group and univariate analysis is accomplished by way of analyzing a group of genetic markers conditioned upon other genetic markers that are found to be predictive of the specified phenotype.
    Type: Application
    Filed: September 4, 2012
    Publication date: March 6, 2014
    Applicant: MICROSOFT CORPORATION
    Inventors: David Earl Heckerman, Jennifer Listgarten, Christoph Anthony Lippert, Jing Xiang, Nicolo Fusi, Carl M. Kadie, Robert I. Davidson
  • Publication number: 20130246017
    Abstract: A computer-executable algorithm that estimates parameters of a predictive model in computation time of less than O(n2k2) when k<=n, is described herein, wherein n is a number of data items considered when estimating the parameters of the predictive model and k is a number of features of each data item considered when estimating the parameters of the predictive model. The parameters are estimated to maximize the probability of observing target values in the training data given the features considered in the training data.
    Type: Application
    Filed: July 16, 2012
    Publication date: September 19, 2013
    Applicant: Microsoft Corporation
    Inventors: David Earl Heckerman, Jennifer Listgarten, Carl M. Kadie, Omer Weissbrod
  • Publication number: 20130246033
    Abstract: Described herein are technologies pertaining to predicting whether a living being, such as a human being, an animal, or a plant, has a phenotype or set of phenotypes in real-time or near real-time. A filter set of genetic markers are determined heuristically, by first univariately computing scores for respective genetic markers that are indicative of their predictive ability with respect to the phenotype or the set of phenotypes. Thereafter, during training, the filter set is initially selected and thereafter expanded based upon the scores, until predictive accuracy for the phenotype or set of phenotypes reaches a threshold or is optimized. The filter set, which includes a relatively small number of genetic markers, is subsequently employed for real-time or near-real time phenotype prediction.
    Type: Application
    Filed: March 14, 2012
    Publication date: September 19, 2013
    Applicant: MICROSOFT CORPORATION
    Inventors: David Earl Heckerman, Jennifer Listgarten, Carl M. Kadie, Omer Weissbrod
  • Patent number: 8473218
    Abstract: A system described herein includes a receiver component that receives an HLA data set, wherein the HLA data set comprises low resolution HLA data. An HLA refinement component comprises a statistical model that automatically refines the HLA data set to transform the low resolution HLA data to high resolution HLA data.
    Type: Grant
    Filed: April 29, 2009
    Date of Patent: June 25, 2013
    Assignee: Microsoft Corporation
    Inventors: Jennifer Listgarten, David Earl Heckerman, Carl M. Kadie
  • Patent number: 8121797
    Abstract: Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.
    Type: Grant
    Filed: December 21, 2007
    Date of Patent: February 21, 2012
    Assignee: Microsoft Corporation
    Inventors: David E. Heckerman, Carl M. Kadie, Jennifer Listgarten, Noah Aaron Zaitlen, Nebojsa Jojic
  • Patent number: 7885905
    Abstract: The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model.
    Type: Grant
    Filed: October 17, 2007
    Date of Patent: February 8, 2011
    Assignee: Microsoft Corporation
    Inventors: David E Heckerman, Jennifer Listgarten, Carl M Kadie
  • Publication number: 20100191513
    Abstract: A system described herein includes a receiver component that receives an HLA data set, wherein the HLA data set comprises low resolution HLA data. An HLA refinement component comprises a statistical model that automatically refines the HLA data set to transform the low resolution HLA data to high resolution HLA data.
    Type: Application
    Filed: April 29, 2009
    Publication date: July 29, 2010
    Applicant: Microsoft Corporation
    Inventors: Jennifer Listgarten, David Earl Heckerman, Carl M. Kadie
  • Publication number: 20090106172
    Abstract: The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model.
    Type: Application
    Filed: October 17, 2007
    Publication date: April 23, 2009
    Applicant: MICROSOFT CORPORATION
    Inventors: David E. Heckerman, Jennifer Listgarten, Carl M. Kadie
  • Publication number: 20080172215
    Abstract: Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.
    Type: Application
    Filed: December 21, 2007
    Publication date: July 17, 2008
    Applicant: MICROSOFT CORPORATION
    Inventors: David E. Heckerman, Carl M. Kadie, Jennifer Listgarten, Noah Aaron Zaitlen, Nebojsa Jojic