Invention Grant
US09589299B2 Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures
有权
基于各种数据结构中相关数据的自动聚类,对不良行为行为进行动态和交互式调查的系统和用户界面
- Patent Title: Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures
- Patent Title (中): 基于各种数据结构中相关数据的自动聚类,对不良行为行为进行动态和交互式调查的系统和用户界面
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Application No.: US15151904Application Date: 2016-05-11
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Publication No.: US09589299B2Publication Date: 2017-03-07
- Inventor: Alexander Visbal , James Thompson , Marvin Sum , Jason Ma , Bing Jie Fu , Ilya Nepomnyashchiy , Devin Witherspoon , Victoria Lai , Steven Berler , Alexei Smaliy , Suchan Lee
- Applicant: Palantir Technologies Inc.
- Applicant Address: US CA Palo Alto
- Assignee: PALANTIR TECHNOLOGIES INC.
- Current Assignee: PALANTIR TECHNOLOGIES INC.
- Current Assignee Address: US CA Palo Alto
- Agency: Knobbe, Martens, Olson & Bear LLP
- Main IPC: G06Q40/00
- IPC: G06Q40/00 ; G06F3/0482 ; G06F3/0484 ; G06F12/10 ; G06F17/30 ; G06K9/62

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 or rules so as to generate a tiled display of the groups of related data clusters such that the analyst may quickly and efficiently 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 fraud investigation.
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