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1. 发明授权

US11637867B2 Cyber security sharing and identification system 有权
公开(公告)号：US11637867B2
公开(公告)日：2023-04-25
申请号：US17129563
申请日：2020-12-21
申请人： Palantir Technologies Inc.
发明人： Jacob Albertson , Melody Hildebrandt , Harkirat Singh , Shyam Sankar , Rick Ducott , Peter Maag , Marissa Kimball
IPC分类号： H04L29/06 , H04L9/40 , G06F21/50 , G06F21/55
摘要： Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

2. 发明申请

US20180337952A1 CYBER SECURITY SHARING AND IDENTIFICATION SYSTEM 审中-公开
公开(公告)号：US20180337952A1
公开(公告)日：2018-11-22
申请号：US15923949
申请日：2018-03-16
申请人： Palantir Technologies Inc.
发明人： Jacob Albertson , Melody Hildebrandt , Harkirat Singh , Shyam Sankar , Rick Ducott , Peter Maag , Marissa Kimball
IPC分类号： H04L29/06 , G06F21/50 , G06F21/55
CPC分类号： H04L63/20 , G06F21/50 , G06F21/55 , H04L63/14 , H04L63/1441
摘要： Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

3. 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。

4. 发明申请

US20230034113A1 SYSTEMS AND USER INTERFACES FOR HOLISTIC, DATA-DRIVEN INVESTIGATION OF BAD ACTOR BEHAVIOR BASED ON CLUSTERING AND SCORING OF RELATED DATA 有权
公开(公告)号：US20230034113A1
公开(公告)日：2023-02-02
申请号：US17937694
申请日：2022-10-03
申请人： Palantir Technologies Inc.
发明人： Sean Hunter , Aditya Kumar , Jacob Albertson
IPC分类号： G06Q40/04 , G06F3/0482 , G06F16/28 , G06F16/2457 , G06Q40/00
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, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to 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 risky trading investigation.

5. 发明申请

US20190164224A1 SYSTEMS AND USER INTERFACES FOR HOLISTIC, DATA-DRIVEN INVESTIGATION OF BAD ACTOR BEHAVIOR BASED ON CLUSTERING AND SCORING OF RELATED DATA 审中-公开
公开(公告)号：US20190164224A1
公开(公告)日：2019-05-30
申请号：US16264983
申请日：2019-02-01
申请人： Palantir Technologies Inc.
发明人： Sean Hunter , Aditya Kumar , Jacob Albertson
IPC分类号： G06Q40/04 , G06Q40/00 , G06F3/0482 , G06F16/2457 , G06F16/28
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, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to 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 risky trading investigation.

6. 发明授权

US10223748B2 Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data 有权
公开(公告)号：US10223748B2
公开(公告)日：2019-03-05
申请号：US15239482
申请日：2016-08-17
申请人： Palantir Technologies Inc.
发明人： Sean Hunter , Aditya Kumar , Jacob Albertson
IPC分类号： G06Q40/00 , G06Q40/04 , G06F17/30 , G06F3/0482
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, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to 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 risky trading investigation.

7. 发明申请

US20170032463A1 SYSTEMS AND USER INTERFACES FOR HOLISTIC, DATA-DRIVEN INVESTIGATION OF BAD ACTOR BEHAVIOR BASED ON CLUSTERING AND SCORING OF RELATED DATA 审中-公开
标题翻译：基于相关数据的分类和分类的僵尸行为的数据驱动调查系统和用户界面
公开(公告)号：US20170032463A1
公开(公告)日：2017-02-02
申请号：US15239482
申请日：2016-08-17
申请人： Palantir Technologies Inc.
发明人： Sean Hunter , Aditya Kumar , Jacob Albertson
IPC分类号： G06Q40/04 , G06F17/30
CPC分类号： G06Q40/04 , G06F3/0482 , G06F17/30522 , G06F17/30598 , G06Q40/00
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, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to 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 risky trading investigation.
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，自动标记和分组这些集群数据结构，并且提供自动化分析和分组的结果。分析师的方式。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，规则，指标或场景的自动化应用，以便生成分析师可能快速得到的分数，报告，警报或结论，有效地用于评估数据集群。特别地，可以在交互式用户界面中动态地重新分组和/或过滤数据集群，以便分析人员可以在与各种数据集群相关联的信息之间快速导航，并在上下文中有效地评估那些数据集群例如，有风险的交易调查。

8. 发明申请

US20150235334A1 HEALTHCARE FRAUD SHARING SYSTEM 审中-公开
标题翻译：医疗保健共享系统
公开(公告)号：US20150235334A1
公开(公告)日：2015-08-20
申请号：US14518757
申请日：2014-10-20
申请人： Palantir Technologies Inc.
发明人： Lekan Wang , Melody Hildebrandt , Tayler Cox , Chris Burchhardt , Casey Ketterling , Ajay Sudan , Bob McGrew , Jacob Albertson , Harkirat Singh , Shyam Sankar , Rick Ducott , Peter Maag , Marissa Kimball
IPC分类号： G06Q50/22 , G06Q30/00
CPC分类号： G06Q50/22 , G06F21/50 , G06Q10/10 , G06Q30/018 , G06Q40/08
摘要： Systems and techniques for sharing healthcare fraud data are described herein. Healthcare fraud detection schemes and/or fraud data may be automatically shared, investigated, enabled, and/or used by entities. A healthcare fraud detection scheme may be enabled on different entities comprising different computing systems to combat similar healthcare fraud threats, instances, and/or attacks. Healthcare fraud detection schemes and/or fraud data may be modified to redact sensitive information and/or configured through access controls for sharing.
摘要翻译：本文描述了用于共享医疗欺诈数据的系统和技术。医疗欺诈检测方案和/或欺诈数据可以被实体自动共享，调查，启用和/或使用。可以在包括不同计算系统的不同实体上实现医疗欺诈检测方案，以对抗类似的医疗欺诈威胁，实例和/或攻击。可以修改医疗保健欺诈检测方案和/或欺诈数据以修正敏感信息和/或通过用于共享的访问控制来配置。

9. 发明授权

US11587177B2 Joined and coordinated detection, handling, and prevention of cyberattacks 有权
公开(公告)号：US11587177B2
公开(公告)日：2023-02-21
申请号：US14919506
申请日：2015-10-21
申请人： Palantir Technologies Inc.
发明人： Shyam Sankar , Jacob Albertson , Melody Hildebrandt , Harkirat Singh , Rick Ducott , Peter Maag , Marissa Kimball
IPC分类号： H04L29/06 , G06Q40/08 , H04L9/40
摘要： Methods, devices, systems and computer program products enable monitoring and responding to cyber security attacks. One such system relates to a consortium of monitoring companies and an infrastructure including one or more central monitoring stations or local handling stations for a monitoring company are provided. A central monitoring station of a monitoring company detects a cyberattack that has been launched against a client computer system, and requests a local station to respond to the cyberattack via onsite visits or requests additional resources from other monitoring companies through the consortium system. The central monitoring station also sends to the consortium system updates on a cyberattack that is detected or mitigated by a central monitoring station or local handling station of the monitoring company. The monitoring consortium enables stronger capabilities than any individual monitoring company can offer by the combination and coordination of the efforts and resources of the members.

10. 发明授权

US11501369B2 Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data 有权
公开(公告)号：US11501369B2
公开(公告)日：2022-11-15
申请号：US16264983
申请日：2019-02-01
申请人： Palantir Technologies Inc.
发明人： Sean Hunter , Aditya Kumar , Jacob Albertson
IPC分类号： G06Q40/00 , G06Q40/04 , G06F3/0482 , G06F16/28 , G06F16/2457
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, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to 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 risky trading investigation.

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