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1. 发明授权

US11336681B2 Malware data clustering 有权
公开(公告)号：US11336681B2
公开(公告)日：2022-05-17
申请号：US16898850
申请日：2020-06-11
申请人： Palantir Technologies Inc.
发明人： Harkirat Singh , Geoffrey Stowe , Brendan Weickert , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC分类号： G06Q40/00 , H04L29/06 , G06F16/2457 , G06F16/23 , G06F16/242 , G06F16/28 , G06F16/9535 , G06Q10/10 , G06Q40/02 , G06F16/335 , G06F16/35 , G06F16/26 , G06F16/2458 , G06Q20/40 , G06Q30/00 , G06Q20/38
摘要： In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

2. 发明申请

US20180270264A1 EXTERNAL MALWARE DATA ITEM CLUSTERING AND ANALYSIS 审中-公开
公开(公告)号：US20180270264A1
公开(公告)日：2018-09-20
申请号：US15961431
申请日：2018-04-24
申请人： Palantir Technologies Inc.
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 , G06Q40/00 , G06F17/30
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.

3. 发明申请

US20160344758A1 EXTERNAL MALWARE DATA ITEM CLUSTERING AND ANALYSIS 有权
标题翻译：外部恶意数据项集合和分析
公开(公告)号：US20160344758A1
公开(公告)日：2016-11-24
申请号：US14473920
申请日：2014-08-29
申请人： Palantir Technologies Inc.
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。

4. 发明授权

US09171334B1 Tax data clustering 有权
标题翻译：税务数据聚类
公开(公告)号：US09171334B1
公开(公告)日：2015-10-27
申请号：US14139628
申请日：2013-12-23
申请人： Palantir Technologies, Inc.
发明人： Alexander Visbal , Adam Borochoff , Jacob Albertson , Trevor Austin , Christopher Rogers , Daniel Campos , Matthew Sprague , Michael Kross , Parvathy Menon , Michael Harris
IPC分类号： G06Q40/00
CPC分类号： G06F17/3053 , G06F17/30345 , G06F17/30412 , G06F17/30539 , G06F17/30572 , G06F17/30598 , G06F17/30601 , G06F17/30604 , G06F17/30699 , G06F17/30705 , G06F17/3071 , G06F17/30867 , G06Q10/10 , G06Q20/4016 , G06Q30/0185 , G06Q40/00 , G06Q40/02 , G06Q40/025 , G06Q40/10 , G06Q40/123
摘要： In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.
摘要翻译：在各种实施例中，公开了用于从种子生成相关数据集合的集合的系统，方法和技术。可以根据种子生成策略或规则生成种子。可以通过例如检索种子，将种子添加到第一群集，检索群集策略或规则，以及基于聚类策略将相关数据和/或数据实体添加到群集来生成群集。可以基于给定簇中的数据的属性来生成各种聚类分数。此外，可以基于与集群相关联的各种聚类分数来生成集群组合。群集可能会根据群集元素进行排名。各种实施例可以使分析人员能够发现与数据集群相关的各种见解，并且可以适用于各种任务，包括例如税欺诈检测，信标恶意软件检测，恶意软件用户代理检测和/或活动趋势检测其他。

5. 发明申请

US20200304522A1 MALWARE DATA CLUSTERING 审中-公开
公开(公告)号：US20200304522A1
公开(公告)日：2020-09-24
申请号：US16898850
申请日：2020-06-11
申请人： Palantir Technologies Inc.
发明人： Harkirat Singh , Geoffrey Stowe , Brendan Weickert , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC分类号： H04L29/06 , G06Q40/00 , G06F16/2457 , G06F16/23 , G06F16/242 , G06F16/28 , G06F16/9535 , G06Q10/10 , G06Q40/02 , G06F16/335 , G06F16/35 , G06F16/26 , G06F16/2458 , G06Q20/40 , G06Q30/00 , G06Q20/38
摘要： In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

6. 发明授权

US10721268B2 Systems and user interfaces for dynamic and interactive investigation based on automatic clustering of related data in various data structures 有权
公开(公告)号：US10721268B2
公开(公告)日：2020-07-21
申请号：US16239081
申请日：2019-01-03
申请人： Palantir Technologies Inc.
发明人： Harkirat Singh , Brendan Weickert , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC分类号： G06Q40/00 , H04L29/06 , G06F16/2457 , G06F16/23 , G06F16/242 , G06F16/28 , G06F16/9535 , G06Q10/10 , G06Q40/02 , G06F16/335 , G06F16/35 , G06F16/26 , G06F16/2458 , G06Q20/40 , G06Q30/00 , G06Q20/38
摘要： In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

7. 发明申请

US20190052648A1 SYSTEMS AND USER INTERFACES FOR DYNAMIC AND INTERACTIVE INVESTIGATION BASED ON AUTOMATIC CLUSTERING OF RELATED DATA IN VARIOUS DATA STRUCTURES 审中-公开
公开(公告)号：US20190052648A1
公开(公告)日：2019-02-14
申请号：US14928512
申请日：2015-10-30
申请人： Palantir Technologies Inc.
发明人： Geoff Stowe , Harkirat Singh , Stefan Bach , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC分类号： H04L29/06 , G06Q20/38 , G06F17/30
摘要： In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

8. 发明授权

US09965937B2 External malware data item clustering and analysis 有权
公开(公告)号：US09965937B2
公开(公告)日：2018-05-08
申请号：US14473920
申请日：2014-08-29
申请人： Palantir Technologies Inc.
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 , G06F21/00 , G08B21/18 , G06F3/0484 , G06F21/56 , G06F17/30
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.

9. 发明授权

US09503844B1 System and method for collocation detection 有权
标题翻译：搭配检测的系统和方法
公开(公告)号：US09503844B1
公开(公告)日：2016-11-22
申请号：US14088251
申请日：2013-11-22
申请人： PALANTIR TECHNOLOGIES, INC.
发明人： Matthew Sprague , Miklos Danka , Bill Dwyer
IPC分类号： H04M11/04 , H04W4/02 , H04W12/02 , H04L29/08
CPC分类号： H04W4/02 , H04L63/1425 , H04L63/30 , H04L67/22 , H04W12/02
摘要： Systems and methods are disclosed for collocation detection. In accordance with one implementation, a method is provided for collocation detection. The method includes obtaining a first object observation that includes a first object identifier, a first observation time, and a first observation location. The method also includes obtaining a second object observation that includes a second object identifier, a second observation time, and a second observation location. In addition, the method includes associating the first observation with a first area on a map, associating the second observation with a second area on the map, and determining whether a potential meeting occurred between objects associated with the first object identifier and the second object identifier based on the first and second observation times, and the first and second areas.
摘要翻译：公开了用于搭配检测的系统和方法。根据一个实现，提供了一种用于搭配检测的方法。该方法包括获得包括第一对象标识符，第一观察时间和第一观察位置的第一对象观察。该方法还包括获得包括第二对象标识符，第二观察时间和第二观察位置的第二对象观察。此外，该方法包括将第一观察与地图上的第一区域相关联，将第二观测与地图上的第二区域相关联，以及确定与第一对象标识符相关联的对象与第二对象标识符之间是否发生潜在会议基于第一和第二观察时间，以及第一和第二区域。

10. 发明授权

US08712906B1 Prioritizing data clusters with customizable scoring strategies 有权
公开(公告)号：US08712906B1
公开(公告)日：2014-04-29
申请号：US13968213
申请日：2013-08-15
申请人： Palantir Technologies, Inc.
发明人： Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC分类号： G06Q40/00
摘要： Techniques are disclosed for prioritizing a plurality of clusters. Prioritizing clusters may generally include identifying a scoring strategy for prioritizing the plurality of clusters. Each cluster is generated from a seed and stores a collection of data retrieved using the seed. For each cluster, elements of the collection of data stored by the cluster are evaluated according to the scoring strategy and a score is assigned to the cluster based on the evaluation. The clusters may be ranked according to the respective scores assigned to the plurality of clusters. The collection of data stored by each cluster may include financial data evaluated by the scoring strategy for a risk of fraud. The score assigned to each cluster may correspond to an amount at risk.

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