-
公开(公告)号：US10216801B2
公开(公告)日：2019-02-26
申请号：US14819272
申请日：2015-08-05
Applicant: Palantir Technologies Inc.
Inventor： Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
Abstract: Techniques are disclosed for 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.
-
公开(公告)号：US09998485B2
公开(公告)日：2018-06-12
申请号：US14487021
申请日：2014-09-15
Applicant: Palantir Technologies Inc.
Inventor： 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 classification number: H04L63/1425 , G06F17/30598 , G06Q40/12 , H04L63/1408 , H04L63/145
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.
-
公开(公告)号：US09344447B2
公开(公告)日：2016-05-17
申请号：US14486991
申请日：2014-09-15
Applicant: Palantir Technologies Inc.
Inventor： 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 classification number: H04L63/1425 , G06F17/30598 , G06Q40/12 , H04L63/1408 , H04L63/145
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.
Abstract translation: 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。 可以将数据集群的人可读分析（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。
-
公开(公告)号：US09313233B2
公开(公告)日：2016-04-12
申请号：US14027118
申请日：2013-09-13
Applicant: PALANTIR TECHNOLOGIES, INC.
Inventor： Matthew Sprague , Andy Isaacson
CPC classification number: H04L63/302 , H04W64/00
Abstract: Systems and methods are disclosed for detecting associated devices. In accordance with one implementation, a method is provided for detecting associated devices. The method includes obtaining information about a target device and determining, based on the information about the target device, one or more target observations that include a target time and a target location. The method also includes identifying one or more second observations of one or more candidate devices, wherein the candidate observations include a second time and a second location that correspond with the target time and the target location. In addition, the method includes determining, from the one or more candidate devices, any associated devices that may correspond with the target device.
Abstract translation: 公开了用于检测相关设备的系统和方法。 根据一个实现，提供了一种用于检测相关设备的方法。 该方法包括获取关于目标设备的信息，并且基于关于目标设备的信息，确定包括目标时间和目标位置的一个或多个目标观察结果。 该方法还包括识别一个或多个候选设备的一个或多个第二观测，其中候选观测包括与目标时间和目标位置对应的第二时间和第二位置。 此外，该方法包括从一个或多个候选设备确定可能与目标设备相对应的任何相关联的设备。
-
公开(公告)号：US20160034470A1
公开(公告)日：2016-02-04
申请号：US14819272
申请日：2015-08-05
Applicant: Palantir Technologies Inc.
Inventor： Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC: G06F17/30
CPC classification number: 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
Abstract: Techniques are disclosed for 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.
Abstract translation: 公开了用于优先考虑多个聚类的技术。 优先化集群通常可以包括识别用于对多个集群进行优先级排序的评分策略。 每个集群都是从种子生成的，并存储使用种子检索的数据集合。 对于每个集群，根据评分策略评估集群存储的数据集合的元素，并根据评估将分数分配给集群。 可以根据分配给多个聚类的各个分数对聚类进行排名。 由每个集群存储的数据的收集可以包括通过得分策略评估的财务数据以获得欺诈的风险。 分配给每个集群的分数可能对应于处于风险中的金额。
-
Patent Agency Ranking
公开(公告)号：US09202249B1
公开(公告)日：2015-12-01
申请号：US14473552
申请日：2014-08-29
Applicant: Palantir Technologies Inc.
Inventor： 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: G06Q40/00
CPC classification number: H04L63/1425 , G06F17/30598 , G06Q40/12 , H04L63/1408 , H04L63/145
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.
Abstract translation: 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。
-
公开(公告)号：US09177344B1
公开(公告)日：2015-11-03
申请号：US14139640
申请日：2013-12-23
Applicant: Palantir Technologies, Inc.
Inventor： Harkirat Singh , Brendan Weickert , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
CPC classification number: 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
Abstract: 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.
Abstract translation: 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。
-
公开(公告)号：US09165299B1
公开(公告)日：2015-10-20
申请号：US14139713
申请日：2013-12-23
Applicant: Palantir Technologies, Inc.
Inventor： Geoff Stowe , Harkirat Singh , Stefan Bach , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
CPC classification number: 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
Abstract: 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.
Abstract translation: 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。
-
公开(公告)号：US09135658B2
公开(公告)日：2015-09-15
申请号：US14264445
申请日：2014-04-29
Applicant: Palantir Technologies, Inc.
Inventor： Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
CPC classification number: 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
Abstract: 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.
Abstract translation: 公开了用于优先考虑多个聚类的技术。 优先化集群通常可以包括识别用于对多个集群进行优先级排序的评分策略。 每个集群都是从种子生成的，并存储使用种子检索的数据集合。 对于每个集群，根据评分策略评估集群存储的数据集合的元素，并根据评估将分数分配给集群。 可以根据分配给多个聚类的各个分数对聚类进行排名。 由每个集群存储的数据的收集可以包括通过得分策略评估的财务数据以获得欺诈的风险。 分配给每个集群的分数可能对应于处于风险中的金额。
-
公开(公告)号：US08788407B1
公开(公告)日：2014-07-22
申请号：US14139603
申请日：2013-12-23
Applicant: Palantir Technologies, Inc.
Inventor： Harkirat Singh , Geoff Stowe , Brendan Weickert , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC: G06Q40/00
CPC classification number: 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
Abstract: 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.
Abstract translation: 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。
-
-
-
-
-
-
-
-
-
Search Results
40
records
(took 0.048 seconds)
