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公开(公告)号:USD836129S1
公开(公告)日:2018-12-18
申请号:US29565639
申请日:2016-05-23
设计人: Joshua Goldenberg , James Thompson
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公开(公告)号:US20180270264A1
公开(公告)日:2018-09-20
申请号:US15961431
申请日:2018-04-24
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 , H04L63/1408 , 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.
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公开(公告)号:US09881074B2
公开(公告)日:2018-01-30
申请号:US15053155
申请日:2016-02-25
发明人: John Chakerian , Robert Fink , Mark Schafer , James Thompson , Marvin Sum , Allen Cai
IPC分类号: G06F7/02 , G06F17/30 , G06F17/21 , G06F3/0482 , G06F3/0484
CPC分类号: G06F17/30598 , G06F3/0482 , G06F3/04842 , G06F17/212 , G06F17/30011 , G06F17/30386 , G06F17/3053 , G06F17/30601 , G06F17/30705 , G06F17/3071 , G06F17/30867
摘要: Systems and methods are disclosed for news events detection and visualization. In accordance with one implementation, a method is provided for news events detection and visualization. The method includes, for example, obtaining a document vector based from a document, obtaining one or more clusters of documents, each cluster associated with a plurality of documents, a cluster vector, and a cluster weight, determining a matching cluster from the one or more clusters based at least on the similarity between the document vector and the cluster vector of the matching cluster, and associating the document with the matching cluster.
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公开(公告)号:US09569070B1
公开(公告)日:2017-02-14
申请号:US14076385
申请日:2013-11-11
IPC分类号: G06F3/0481 , G06F3/0482 , G06F9/44 , G06F17/30 , H04L29/08 , G06F3/0484
CPC分类号: G06F3/0482 , G06F3/0481 , G06F3/04817 , G06F3/04842 , G06F17/30067 , G06F17/30575 , G06F17/30578 , H04L29/08306 , H04L29/0854
摘要: Systems, methods, and graphical user interfaces are disclosed that assist a user in deconflicting concurrency conflicts in a peering network in which ambiguous concurrency conflicts can arise. In accordance with some embodiments, a method for assisting a user in deconflicting concurrency conflicts is disclosed. The method includes detecting a plurality of ambiguous data conflicts between the local deployment and the peer deployment. The method further includes providing a graphical user interface to a user at the local deployment that allows the user to filter the plurality of ambiguous data conflicts according to a selected data conflict type of a plurality of predefined data conflict types selectable by the user through the graphical user interface. By providing such as graphical user interface, the user can easily filter a large number (e.g., hundreds) of ambiguous concurrency conflicts that may exist at a given time between the local deployment and the peer deployment.
摘要翻译: 公开了系统,方法和图形用户界面,其帮助用户解决在其中可能出现歧义并发冲突的对等网络中的并发冲突。 根据一些实施例,公开了一种用于辅助用户解包并发冲突的方法。 该方法包括检测本地部署和对等部署之间的多个不明确的数据冲突。 该方法还包括在本地部署时向用户提供图形用户界面,其允许用户根据用户可通过图形选择的多个预定数据冲突类型的所选数据冲突类型来过滤多个模糊数据冲突 用户界面。 通过提供诸如图形用户界面,用户可以容易地过滤在本地部署和对等部署之间的给定时间可能存在的大量(例如数百个)不明确的并发冲突。
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公开(公告)号:US20160344758A1
公开(公告)日:2016-11-24
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
IPC分类号: H04L29/06 , G08B21/18 , G06F3/0484
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 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:US09449035B2
公开(公告)日:2016-09-20
申请号:US14645304
申请日:2015-03-11
发明人: Jack Grossman , James Thompson
IPC分类号: G06F17/30
CPC分类号: G06F17/245 , G06F3/0482 , G06F17/218 , G06F17/30315 , G06F17/30339 , G06F17/3056 , G06F17/30864 , G06F17/30997
摘要: Systems and methods are disclosed for active column filtering. In accordance with one implementation, a method is provided for active column filtering. The method includes providing a table having data values arranged in rows and columns, providing a first filter location indicator whose location is visually associated with a first column, and providing a first interface based on a selection of the first filter location indicator, wherein the first interface's location is visually associated with the first column. The method also includes acquiring a first filter input entered into the first interface, filtering the table based on the acquired first filter input, providing the filtered table for displaying, and providing an applied filter indicator, whose location is visually associated with the first column, the applied filter indicator including at least the first filter input.
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公开(公告)号:US20160253750A1
公开(公告)日:2016-09-01
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 , G06F17/30
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.
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