Patents by Inventor Bing Jie Fu
Bing Jie Fu 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: 11895137Abstract: 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: GrantFiled: December 2, 2022Date of Patent: February 6, 2024Assignee: 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
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Patent number: 11727481Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: GrantFiled: August 19, 2021Date of Patent: August 15, 2023Assignee: Palantir Technologies Inc.Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Publication number: 20230096596Abstract: 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: ApplicationFiled: December 2, 2022Publication date: March 30, 2023Inventors: 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
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Patent number: 11546364Abstract: 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: GrantFiled: August 26, 2020Date of Patent: January 3, 2023Assignee: 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
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Publication number: 20210385237Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: ApplicationFiled: August 19, 2021Publication date: December 9, 2021Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Patent number: 11102224Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: GrantFiled: August 28, 2019Date of Patent: August 24, 2021Assignee: Palantir Technologies Inc.Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Publication number: 20200396237Abstract: 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: ApplicationFiled: August 26, 2020Publication date: December 17, 2020Inventors: 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
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Patent number: 10798116Abstract: 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: GrantFiled: April 24, 2018Date of Patent: October 6, 2020Assignee: 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
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Publication number: 20190387008Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: ApplicationFiled: August 28, 2019Publication date: December 19, 2019Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Patent number: 10447712Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: GrantFiled: March 3, 2017Date of Patent: October 15, 2019Assignee: Palantir Technologies Inc.Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Publication number: 20180270264Abstract: 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: ApplicationFiled: April 24, 2018Publication date: September 20, 2018Inventors: 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
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Patent number: 9998485Abstract: 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: GrantFiled: September 15, 2014Date of Patent: June 12, 2018Assignee: 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
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Patent number: 9965937Abstract: 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: GrantFiled: August 29, 2014Date of Patent: May 8, 2018Assignee: 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
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Publication number: 20170244735Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: ApplicationFiled: March 3, 2017Publication date: August 24, 2017Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Patent number: 9589299Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: GrantFiled: May 11, 2016Date of Patent: March 7, 2017Assignee: PALANTIR TECHNOLOGIES INC.Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Publication number: 20160366164Abstract: 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: ApplicationFiled: September 15, 2014Publication date: December 15, 2016Inventors: 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
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Publication number: 20160344758Abstract: 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: ApplicationFiled: August 29, 2014Publication date: November 24, 2016Inventors: 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
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Publication number: 20160253750Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: ApplicationFiled: May 11, 2016Publication date: September 1, 2016Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Publication number: 20160180451Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: ApplicationFiled: December 22, 2014Publication date: June 23, 2016Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Victoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee
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Patent number: 9367872Abstract: 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, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping 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 tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.Type: GrantFiled: December 22, 2014Date of Patent: June 14, 2016Assignee: PALANTIR TECHNOLOGIES INC.Inventors: Alexander Visbal, James Thompson, Marvin Sum, Jason Ma, Bing Jie Fu, Ilya Nepomnyashchiy, Devin Witherspoon, Vicktoria Lai, Steven Berler, Alexei Smaliy, Suchan Lee