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    Internal malware data item clustering and analysis
    42.
    发明授权
    Internal malware data item clustering and analysis 有权
    内部恶意软件数据项集群和分析

    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

    IPC: G06Q40/00 ,  H04L29/06 ,  G06F17/30

    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。

    Data item clustering and analysis
    43.
    发明授权
    Data item clustering and analysis 有权
    数据项聚类分析

    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。

    PROVIDER PORTAL
    44.
    发明申请
    PROVIDER PORTAL 有权
    PROVIDER门户

    公开(公告)号：US20150269334A1

    公开(公告)日：2015-09-24

    申请号：US14222364

    申请日：2014-03-21

    Applicant: Palantir Technologies, Inc.

    Inventor： Sam Fendell ,  James Thompson

    IPC: G06F19/00

    CPC classification number: G06F19/328 ,  G06F19/00 ,  G06Q40/08 ,  G06Q50/22 ,  G06Q50/24 ,  G16H10/60

    Abstract: Various systems and methods are provided that graphically allow health insurance company personnel to identify patient diagnoses that are not accounted for by the health insurance company. Furthermore, the various systems and methods graphically allow health insurance company personnel to identify patients that have not submitted claims for documented ailments or conditions. Thus, the health insurance company may be able to improve its chances of receiving transfer payments from other health insurance companies and/or receiving higher star ratings.

    Abstract translation: 提供了各种系统和方法，以图形方式让健康保险公司人员识别健康保险公司没有考虑的患者诊断。 此外，各种系统和方法以图形方式允许健康保险公司人员识别尚未提交记录病情或病情的索赔的病人。 因此，健康保险公司可以提高从其他健康保险公司接收转移支付的机会和/或获得更高的星级。

    Space-optimized display of multi-column tables with selective text truncation based on a combined text width
    45.
    发明授权
    Space-optimized display of multi-column tables with selective text truncation based on a combined text width 有权
    基于组合文本宽度的空间优化显示多列表，具有选择性文本截断

    公开(公告)号：US08832594B1

    公开(公告)日：2014-09-09

    申请号：US14137120

    申请日：2013-12-20

    Applicant: Palantir Technologies, Inc.

    Inventor： James Thompson ,  Joshua Goldenberg

    IPC: G06F3/048 ,  G06F17/24 ,  G06F3/0481

    CPC classification number: G06F17/245 ,  G06F3/0481 ,  G06F3/04842 ,  G06F17/211 ,  G06F17/2294 ,  G06F17/25 ,  G06F17/30905

    Abstract: The display of a multi-column table can be optimized. For example, a container, such as a multi-column table, can have a first container width. The container includes first text, second text, and a divider, such as an icon, whitespace, or text, between the first text and the second text. The first text, the second text, and the divider can have a combined text width. The container can be resized to a second container width that is smaller than the first container width. If it is determined that the combined text width is then greater than the second container width, the first text, the second text, or both can be abbreviated until the combined text width is less than the second container width.

    Abstract translation: 可以优化多列表的显示。 例如，诸如多列表的容器可以具有第一容器宽度。 容器包括第一文本和第二文本之间的第一文本，第二文本和分隔符，诸如图标，空格或文本。 第一个文本，第二个文本和分隔符可以具有组合的文本宽度。 容器可以调整到小于第一容器宽度的第二容器宽度。 如果确定组合的文本宽度大于第二容器宽度，则可以缩短第一文本，第二文本或两者，直到组合的文本宽度小于第二容器宽度。

    PROPAGATED DELETION OF DATABASE RECORDS AND DERIVED DATA
    50.
    发明申请
    PROPAGATED DELETION OF DATABASE RECORDS AND DERIVED DATA 有权

    公开(公告)号：US20210271670A1

    公开(公告)日：2021-09-02

    申请号：US17208914

    申请日：2021-03-22

    Applicant: Palantir Technologies Inc.

    Inventor： Jeppe Hallgren ,  Ammaar Reshi ,  James Thompson

    IPC: G06F16/23 ,  G06F16/242 ,  G06F16/25

    Abstract: Techniques for propagation of deletion operations among a plurality of related datasets are described herein. In an embodiment, a data processing method comprises, using a distributed database system that is programmed to manage a plurality of different raw datasets and a plurality of derived datasets that have been derived from the raw datasets based on a plurality of derivation relationships that link the raw datasets to the derived datasets: from a first dataset that is stored in the distributed database system, determining a subset of records that are candidates for propagated deletion of specified data values; determining one or more particular raw datasets that contain the subset of records; deleting the specified data values from the particular raw datasets; based on the plurality of derivation relationships and the particular raw datasets, identifying one or more particular derived datasets that have been derived from the particular raw datasets; generating and executing a build of the one or more particular derived datasets to result in creating and storing the one or more particular derived datasets without the specified data values that were deleted from the particular raw datasets; repeating the generating and executing for all derived datasets that have derivation relationships to the particular raw datasets; wherein the method is performed using one or more processors.

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