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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
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 , G06F16/285 , G06Q40/12 , H04L63/145 , H04L63/1408
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.
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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
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
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.
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公开(公告)号：US10834123B2
公开(公告)日：2020-11-10
申请号：US16248120
申请日：2019-01-15
Applicant: Palantir Technologies Inc.
Inventor： Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC: 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
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.
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公开(公告)号：US20190166135A1
公开(公告)日：2019-05-30
申请号：US16248120
申请日：2019-01-15
Applicant: Palantir Technologies Inc.
Inventor： Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC: H04L29/06 , G06F16/242 , G06F16/335 , G06F16/28 , G06F16/2458 , G06Q40/00 , G06F16/35 , G06Q40/02 , G06F16/26 , G06Q10/10 , G06Q30/00 , G06Q20/40 , G06F16/2457 , G06F16/9535
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.
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公开(公告)号：US10111037B1
公开(公告)日：2018-10-23
申请号：US15357635
申请日：2016-11-21
Applicant: PALANTIR TECHNOLOGIES INC.
Inventor： Matthew Sprague , Miklos Danka , Bill Dwyer
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20160366164A1
公开(公告)日：2016-12-15
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
IPC: H04L29/06
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.
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。
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公开(公告)号：US20140310282A1
公开(公告)日：2014-10-16
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
IPC: G06Q30/00
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: 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。
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公开(公告)号：US08818892B1
公开(公告)日：2014-08-26
申请号：US13968213
申请日：2013-08-15
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。
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公开(公告)号：US10798116B2
公开(公告)日：2020-10-06
申请号：US15961431
申请日：2018-04-24
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
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.
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公开(公告)号：US20190158509A1
公开(公告)日：2019-05-23
申请号：US16239081
申请日：2019-01-03
Applicant: Palantir Technologies Inc.
Inventor： Harkirat Singh , Brendan Weickert , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
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.
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