Patents by Inventor Matt Avant

Matt Avant 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: 11531682
    Abstract: Methods and apparatuses related to federated search of multiple sources with conflict resolution are disclosed. A method may comprise obtaining a set of data ontologies (e.g., types, properties, and links) associated with a plurality of heterogeneous data sources; receiving a selection of a graph comprising a plurality of graph nodes connected by one or more graph edges; and transforming the graph into one or more search queries across the plurality of heterogeneous data sources. A method may comprise obtaining a first data object as a result of executing a first search query across a plurality of heterogeneous data sources; resolving, based on one or more resolution rules, at least the first data object with a repository data object; deduplicating data associated with at least the first data object and the repository data object prior to storing the deduplicated data in a repository that has a particular data model.
    Type: Grant
    Filed: January 17, 2020
    Date of Patent: December 20, 2022
    Assignee: Palantir Technologies Inc.
    Inventors: Danielle Kramer, Andrew Israel, Jeffrey Chen, David Cohen, Stephen Freiberg, Bryan Offutt, Matt Avant, Peter Wilczynski, Jason Hoch, Robert Liu, William Waldrep, Kevin Zhang, Alexander Landau, David Tobin
  • Publication number: 20200151189
    Abstract: Methods and apparatuses related to federated search of multiple sources with conflict resolution are disclosed. A method may comprise obtaining a set of data ontologies (e.g., types, properties, and links) associated with a plurality of heterogeneous data sources; receiving a selection of a graph comprising a plurality of graph nodes connected by one or more graph edges; and transforming the graph into one or more search queries across the plurality of heterogeneous data sources. A method may comprise obtaining a first data object as a result of executing a first search query across a plurality of heterogeneous data sources; resolving, based on one or more resolution rules, at least the first data object with a repository data object; deduplicating data associated with at least the first data object and the repository data object prior to storing the deduplicated data in a repository that has a particular data model.
    Type: Application
    Filed: January 17, 2020
    Publication date: May 14, 2020
    Inventors: DANIELLE KRAMER, ANDREW ISRAEL, JEFFREY CHEN, DAVID COHEN, STEPHEN FREIBERG, BRYAN OFFUTT, MATT AVANT, PETER WILCZYNSKI, JASON HOCH, ROBERT LIU, WILLIAM WALDREP, KEVIN ZHANG, ALEXANDER LANDAU, DAVID TOBIN
  • Patent number: 10545982
    Abstract: Methods and apparatuses related to federated search of multiple sources with conflict resolution are disclosed. A method may comprise obtaining a set of data ontologies (e.g., types, properties, and links) associated with a plurality of heterogeneous data sources; receiving a selection of a graph comprising a plurality of graph nodes connected by one or more graph edges; and transforming the graph into one or more search queries across the plurality of heterogeneous data sources. A method may comprise obtaining a first data object as a result of executing a first search query across a plurality of heterogeneous data sources; resolving, based on one or more resolution rules, at least the first data object with a repository data object; deduplicating data associated with at least the first data object and the repository data object prior to storing the deduplicated data in a repository that has a particular data model.
    Type: Grant
    Filed: July 23, 2015
    Date of Patent: January 28, 2020
    Assignee: Palantir Technologies Inc.
    Inventors: Danielle Kramer, Andrew Israel, Jeffrey Chen, David Cohen, Stephen Freiberg, Bryan Offutt, Matt Avant, Peter Wilczynski, Jason Hoch, Robert Liu, William Waldrep, Kevin Zhang, Alexander Landau, David Tobin