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公开(公告)号：US10721268B2
公开(公告)日：2020-07-21
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
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: 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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公开(公告)号：US20190052648A1
公开(公告)日：2019-02-14
申请号：US14928512
申请日：2015-10-30
Applicant: Palantir Technologies Inc.
Inventor： Geoff Stowe , Harkirat Singh , Stefan Bach , 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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公开(公告)号：US09965937B2
公开(公告)日：2018-05-08
申请号：US14473920
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
CPC classification number: G08B21/18 , G06F3/04842 , H04L63/0281 , H04L63/1433 , 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.
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14.发明授权公开(公告)号：US09674662B2
公开(公告)日：2017-06-06
申请号：US14806517
申请日：2015-07-22
Applicant: Palantir Technologies, Inc.
Inventor： Carl Freeland , Jacob Scott , Eric Sadur , Timothy Ronan , Michael Kross , Huey Kwik
CPC classification number: H04W4/023 , G06F17/30247 , G06F17/30265 , G06F17/30342 , G06F17/30569 , G06F17/30607 , G06F17/30876 , G06T1/00 , H04L67/18 , H04L67/306 , H04M1/72572 , H04W4/026 , H04W4/08 , Y10S707/915
Abstract: A mobile data analysis system is provided that enables mobile device location tracking, secure messaging, and real-time sharing of intelligence information, among other features. In one embodiment, a method and apparatus is provided for creating data objects from one or more digital images captured by a mobile device. A computing device receives a first digital image file comprising first image metadata, wherein the first image metadata includes one or more image properties each having an image property value. The computing device transforms one or more of the image property values of the one or more image properties into one or more particular values. The computing device populates one or more data object property values of a data object with the one or more particular values. The computing device stores the data object in a data repository.
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公开(公告)号：US20160249170A1
公开(公告)日：2016-08-25
申请号：US15145177
申请日：2016-05-03
Applicant: Palantir Technologies, Inc.
Inventor： Carl Freeland , Jacob Scott , Eric Sadur , Timothy Ronan , Michael Kross , Huey Kwik
CPC classification number: H04W4/023 , G06F16/2291 , G06F16/258 , G06F16/289 , G06F16/58 , G06F16/583 , G06F16/955 , G06T1/00 , H04L67/18 , H04L67/306 , H04M1/72572 , H04W4/026 , H04W4/08 , Y10S707/915
Abstract: A mobile data analysis system is provided that enables mobile device location tracking, secure messaging, and real-time sharing of intelligence information, among other features. In an embodiment, a mobile data analysis system may comprise one or more mobile device user accounts. For example, mobile device user accounts may be created for field analysts within an organization and used by the field analysts to authenticate with the mobile data analysis system using a mobile or other computing device. In an embodiment, mobile device user accounts may be grouped into one or more mobile device teams. Mobile device user accounts may be grouped into mobile device teams based on organizational roles, areas of responsibility, or any other characteristics. In an embodiment, mobile device teams may be associated with visibility settings that control user access to information associated with mobile device user accounts of particular mobile device teams.
Abstract translation: 提供了一种移动数据分析系统，其能够实现移动设备位置跟踪，安全消息传递和智能信息的实时共享等功能。 在一个实施例中，移动数据分析系统可以包括一个或多个移动设备用户帐户。 例如，可以为组织内的现场分析人员创建移动设备用户帐户，并由现场分析人员使用移动设备用户帐户使用移动或其他计算设备与移动数据分析系统进行身份验证。 在一个实施例中，移动设备用户帐户可以被分组成一个或多个移动设备团队。 基于组织角色，责任区域或任何其他特征，移动设备用户帐户可以被分组到移动设备团队中。 在一个实施例中，移动设备团队可以与控制用户对与特定移动设备团队的移动设备用户帐户相关联的信息的访问的可见性设置相关联。
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Patent Agency Ranking
公开(公告)号：US08712906B1
公开(公告)日：2014-04-29
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
IPC: G06Q40/00
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：US11546364B2
公开(公告)日：2023-01-03
申请号：US17003398
申请日：2020-08-26
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：US20220239672A1
公开(公告)日：2022-07-28
申请号：US17658893
申请日：2022-04-12
Applicant: Palantir Technologies Inc.
Inventor： Harkirat Singh , Geoffrey Stowe , Brendan Weickert , Matthew Sprague , Michael Kross , Adam Borochoff , Parvathy Menon , Michael Harris
IPC: H04L9/40 , 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
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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公开(公告)号：US10459609B2
公开(公告)日：2019-10-29
申请号：US16043825
申请日：2018-07-24
Applicant: Palantir Technologies Inc.
Inventor： Brian Lee , Gaspard van Koningsveld , Kevin Morgan , Michael Kross
IPC: G06F3/0483 , G06F3/0484 , G06F17/22
Abstract: The present disclosure relates to systems and techniques for multi-stage rendering of data pages for display in a data page display window. The present disclosure also relates to rendering data as part of a background instance of a data page renderer. The present disclosure also relates to displaying data requested from a workspace application in a data page window once the requested data is sufficiently rendered as part of a background instance of a data page renderer. The present disclosure also relates to providing a pool of background instances of multiple data page renderers for rendering and pre-rendering data pages for storage and eventual display in a data page window.
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公开(公告)号：US10264014B2
公开(公告)日：2019-04-16
申请号：US14928512
申请日：2015-10-30
Applicant: Palantir Technologies Inc.
Inventor： Geoff Stowe , Harkirat Singh , Stefan Bach , 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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