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    Tax data clustering
    3.
    发明授权
    Tax data clustering 有权
    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

    Applicant: Palantir Technologies, Inc.

    Inventor： Alexander Visbal ,  Adam Borochoff ,  Jacob Albertson ,  Trevor Austin ,  Christopher Rogers ,  Daniel Campos ,  Matthew Sprague ,  Michael Kross ,  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。

    SYSTEMS AND USER INTERFACES FOR HOLISTIC, DATA-DRIVEN INVESTIGATION OF BAD ACTOR BEHAVIOR BASED ON CLUSTERING AND SCORING OF RELATED DATA
    4.
    发明申请
    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

    Applicant: Palantir Technologies Inc.

    Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

    IPC: G06Q40/04 ,  G06F3/0482 ,  G06F16/28 ,  G06F16/2457 ,  G06Q40/00

    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, 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.

    SYSTEMS AND USER INTERFACES FOR HOLISTIC, DATA-DRIVEN INVESTIGATION OF BAD ACTOR BEHAVIOR BASED ON CLUSTERING AND SCORING OF RELATED DATA
    5.
    发明申请
    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

    Applicant: Palantir Technologies Inc.

    Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

    IPC: G06Q40/04 ,  G06Q40/00 ,  G06F3/0482 ,  G06F16/2457 ,  G06F16/28

    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, 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.

    Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data
    6.
    发明授权
    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

    Applicant: Palantir Technologies Inc.

    Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

    IPC: G06Q40/00 ,  G06Q40/04 ,  G06F17/30 ,  G06F3/0482

    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, 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.

    SYSTEMS AND USER INTERFACES FOR HOLISTIC, DATA-DRIVEN INVESTIGATION OF BAD ACTOR BEHAVIOR BASED ON CLUSTERING AND SCORING OF RELATED DATA
    7.
    发明申请
    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
    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

    Applicant: Palantir Technologies Inc.

    Inventor： Sean Hunter ,  Aditya Kumar ,  Jacob Albertson

    IPC: G06Q40/04 ,  G06F17/30

    CPC classification number: G06Q40/04 ,  G06F3/0482 ,  G06F17/30522 ,  G06F17/30598 ,  G06Q40/00

    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, 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.

    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，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。

    ENHANCED MACHINE LEARNING REFINEMENT AND ALERT GENERATION SYSTEM
    9.
    发明申请
    ENHANCED MACHINE LEARNING REFINEMENT AND ALERT GENERATION SYSTEM 有权

    公开(公告)号：US20220201030A1

    公开(公告)日：2022-06-23

    申请号：US17445172

    申请日：2021-08-16

    Applicant: Palantir Technologies Inc.

    Inventor： Corentin Petit ,  Jacob Albertson ,  Marissa Kimball ,  Paul Baseotto ,  Pierre Cholet ,  Timur Iskhakov ,  Victoria Galano

    IPC: H04L29/06 ,  G06F9/451 ,  G06N20/00

    Abstract: Systems and methods are provided for enhanced machine learning refinement and alert generation. An example method includes accessing datasets storing customer information reflecting transactions of customers. Individual risk scores are generated for the customers based on the customer information. Generating the risk score includes providing identified occurrences of scenario definitions and customer information as input to one or more machine learning models, the scenario definitions identifying occurrences of specific information reflected in the datasets, with the machine learning models assign respective risk scores to the customers. An interactive user interface is presented. The interactive user presents summary information associated with the risk scores, with the interactive user interfaces enabling an investigation into whether a particular customer is exhibiting risky behavior, responds to user input indicating feedback usable to update the one or more machine learning models or scenario definitions, with the feedback triggering updating of the machine learning models.

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