Patents by Inventor Julia Boortz

Julia Boortz 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: 11895137
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Grant
    Filed: December 2, 2022
    Date of Patent: February 6, 2024
    Assignee: Palantir Technologies Inc.
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Publication number: 20230096596
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Application
    Filed: December 2, 2022
    Publication date: March 30, 2023
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Patent number: 11546364
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Grant
    Filed: August 26, 2020
    Date of Patent: January 3, 2023
    Assignee: Palantir Technologies Inc.
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Publication number: 20200396237
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Application
    Filed: August 26, 2020
    Publication date: December 17, 2020
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Patent number: 10798116
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Grant
    Filed: April 24, 2018
    Date of Patent: October 6, 2020
    Assignee: Palantir Technologies Inc.
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Publication number: 20180270264
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Application
    Filed: April 24, 2018
    Publication date: September 20, 2018
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Patent number: 9998485
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Grant
    Filed: September 15, 2014
    Date of Patent: June 12, 2018
    Assignee: PALANTIR TECHNOLOGIES, INC.
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Patent number: 9965937
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyzes (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Grant
    Filed: August 29, 2014
    Date of Patent: May 8, 2018
    Assignee: Palantir Technologies Inc.
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Publication number: 20160366164
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Application
    Filed: September 15, 2014
    Publication date: December 15, 2016
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Publication number: 20160344758
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Application
    Filed: August 29, 2014
    Publication date: November 24, 2016
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Patent number: 9344447
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analysis (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Grant
    Filed: September 15, 2014
    Date of Patent: May 17, 2016
    Assignee: Palantir Technologies Inc.
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Publication number: 20160006749
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Application
    Filed: September 15, 2014
    Publication date: January 7, 2016
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff
  • Patent number: 9202249
    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyzes (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
    Type: Grant
    Filed: August 29, 2014
    Date of Patent: December 1, 2015
    Assignee: Palantir Technologies Inc.
    Inventors: David Cohen, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Steven Berler, Alex Smaliy, Jack Grossman, James Thompson, Julia Boortz, Matthew Sprague, Parvathy Menon, Michael Kross, Michael Harris, Adam Borochoff