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公开(公告)号：US11895137B2
公开(公告)日：2024-02-06
申请号：US18061195
申请日：2022-12-02
发明人： 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
CPC分类号： H04L63/1425 , G06F16/285 , G06Q40/12 , H04L63/145 , H04L63/1408
摘要： 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.
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公开(公告)号：US11727481B2
公开(公告)日：2023-08-15
申请号：US17445439
申请日：2021-08-19
发明人： Alexander Visbal , James Thompson , Marvin Sum , Jason Ma , Bing Jie Fu , Ilya Nepomnyashchiy , Devin Witherspoon , Victoria Lai , Steven Berler , Alexei Smaliy , Suchan Lee
IPC分类号： G06Q40/00 , G06F16/28 , G06F16/9038 , G06F16/2457 , G06F3/0482 , G06F3/04842 , G06Q40/02 , H04L9/40 , G06F12/1036 , G06F16/34 , G06F18/23 , G06F18/40 , G06F21/55
CPC分类号： G06Q40/00 , G06F3/0482 , G06F3/04842 , G06F16/24578 , G06F16/287 , G06F16/9038 , G06Q40/02 , H04L63/1416 , H04L63/1425 , H04L63/1433 , G06F12/1036 , G06F16/34 , G06F18/23 , G06F18/40 , G06F21/552
摘要： 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.
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公开(公告)号：US20230096596A1
公开(公告)日：2023-03-30
申请号：US18061195
申请日：2022-12-02
发明人： 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
摘要： 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.
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公开(公告)号：US20160366164A1
公开(公告)日：2016-12-15
申请号：US14487021
申请日：2014-09-15
发明人： 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
IPC分类号： H04L29/06
CPC分类号： H04L63/1425 , G06F17/30598 , G06Q40/12 , H04L63/1408 , H04L63/145
摘要： 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.
摘要翻译： 本公开的实施例涉及一种数据分析系统，其可以自动生成存储器有效的集群数据结构，自动分析这些集群数据结构，并以优化的方式向分析者提供自动化分析的结果。 集群数据结构（本文中也称为数据集群）的自动化分析可以包括各种标准或规则的自动应用，以便生成数据集群的紧凑的，人类可读的分析。 可以将数据集群的可读分析（也称为“摘要”或“结论”）组织成交互式用户界面，以使分析人员能够在与各种数据集群相关联的信息之间快速导航，并有效地评估 这些数据集群在例如欺诈调查的背景下。 本公开的实施例还涉及聚类数据结构的自动评分。
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公开(公告)号：US11487774B2
公开(公告)日：2022-11-01
申请号：US17170470
申请日：2021-02-08
IPC分类号： G06F16/248 , G06F7/14 , G06F16/9535 , G06F16/28
摘要： Techniques for contextual modification of data sharing constraints in a distributed database system are disclosed. The constraint modifications can improve data sharing processes, particularly in applications that involve cross-model data sharing and/or have a need for low data latency.
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公开(公告)号：US20210385237A1
公开(公告)日：2021-12-09
申请号：US17445439
申请日：2021-08-19
发明人： Alexander Visbal , James Thompson , Marvin Sum , Jason Ma , Bing Jie Fu , Ilya Nepomnyashchiy , Devin Witherspoon , Victoria Lai , Steven Berler , Alexei Smaliy , Suchan Lee
IPC分类号： H04L29/06 , G06Q40/00 , G06F16/28 , G06F16/9038 , G06F16/2457 , G06F3/0482 , G06F3/0484 , G06Q40/02
摘要： 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.
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公开(公告)号：US10447712B2
公开(公告)日：2019-10-15
申请号：US15449042
申请日：2017-03-03
发明人： Alexander Visbal , James Thompson , Marvin Sum , Jason Ma , Bing Jie Fu , Ilya Nepomnyashchiy , Devin Witherspoon , Victoria Lai , Steven Berler , Alexei Smaliy , Suchan Lee
IPC分类号： G06Q40/00 , H04L29/06 , G06F16/28 , G06F16/9038 , G06F16/2457 , G06F3/0482 , G06F3/0484 , G06Q40/02 , G06F12/1036 , G06K9/62 , G06F16/34 , G06F21/55
摘要： 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.
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公开(公告)号：US09965937B2
公开(公告)日：2018-05-08
申请号：US14473920
申请日：2014-08-29
发明人： 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
CPC分类号： G08B21/18 , G06F3/04842 , H04L63/0281 , H04L63/1433 , H04L63/145
摘要： 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.
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9.发明授权Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures 有权基于各种数据结构中相关数据的自动聚类，对不良行为行为进行动态和交互式调查的系统和用户界面公开(公告)号：US09589299B2
公开(公告)日：2017-03-07
申请号：US15151904
申请日：2016-05-11
发明人： Alexander Visbal , James Thompson , Marvin Sum , Jason Ma , Bing Jie Fu , Ilya Nepomnyashchiy , Devin Witherspoon , Victoria Lai , Steven Berler , Alexei Smaliy , Suchan Lee
IPC分类号： G06Q40/00 , G06F3/0482 , G06F3/0484 , G06F12/10 , G06F17/30 , G06K9/62
CPC分类号： H04L63/1416 , G06F3/0482 , G06F3/04842 , G06F12/1036 , G06F17/3053 , G06F17/30601 , G06F17/30716 , G06F17/30991 , G06F21/552 , G06K9/6218 , G06K9/6253 , G06Q40/00 , G06Q40/02 , H04L63/1425 , H04L63/1433
摘要： 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.
摘要翻译： 本公开的实施例涉及一种数据分析系统，其可以自动生成存储器有效的集群数据结构，自动分析这些集群数据结构，自动标记和分组这些集群数据结构，并且提供自动化分析和分组的结果。 分析师的方式。 集群数据结构（本文也称为数据集群）的自动分析可以包括各种标准或规则的自动化应用，以便生成相关数据集群组的平铺显示，使得分析者可以快速有效地评估 数据集群。 特别地，可以在交互式用户界面中动态地重新分组和/或过滤数据集群，以便分析人员可以在与各种数据集群相关联的信息之间快速导航，并在上下文中有效地评估那些数据集群 例如欺诈调查。
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10.发明授权Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures 有权基于各种数据结构中相关数据的自动聚类，对不良行为行为进行动态和交互式调查的系统和用户界面公开(公告)号：US09367872B1
公开(公告)日：2016-06-14
申请号：US14579752
申请日：2014-12-22
发明人： Alexander Visbal , James Thompson , Marvin Sum , Jason Ma , Bing Jie Fu , Ilya Nepomnyashchiy , Devin Witherspoon , Vicktoria Lai , Steven Berler , Alexei Smaliy , Suchan Lee
CPC分类号： H04L63/1416 , G06F3/0482 , G06F3/04842 , G06F12/1036 , G06F17/3053 , G06F17/30601 , G06F17/30716 , G06F17/30991 , G06F21/552 , G06K9/6218 , G06K9/6253 , G06Q40/00 , G06Q40/02 , H04L63/1425 , H04L63/1433
摘要： 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.
摘要翻译： 本公开的实施例涉及一种数据分析系统，其可以自动生成存储器有效的集群数据结构，自动分析这些集群数据结构，自动标记和分组这些集群数据结构，并且提供自动化分析和分组的结果。 分析师的方式。 集群数据结构（本文也称为数据集群）的自动分析可以包括各种标准或规则的自动化应用，以便生成相关数据集群组的平铺显示，使得分析者可以快速有效地评估 数据集群。 特别地，可以在交互式用户界面中动态地重新分组和/或过滤数据集群，以便分析人员可以在与各种数据集群相关联的信息之间快速导航，并在上下文中有效地评估那些数据集群 例如欺诈调查。
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