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    Method of malicious social activity prediction using spatial-temporal social network data
    52.
    发明授权
    Method of malicious social activity prediction using spatial-temporal social network data 有权

    公开(公告)号：US11195107B1

    公开(公告)日：2021-12-07

    申请号：US16705219

    申请日：2019-12-05

    Applicant: HRL Laboratories, LLC

    Inventor： Qin Jiang ,  Kang-Yu Ni ,  Tsai-Ching Lu

    IPC: G06N5/04 ,  G06N5/02 ,  H04L29/08 ,  G06Q50/26 ,  G06Q50/00

    Abstract: Described is a system for predicting future social activity. The system extracts social activities from spatial-temporal social network data collected in a first time period ranging from hours to days to capture spatial structures of social activities in a graph network representation. A graph matching technique is applied over a set of spatial-temporal social network data collected in a second time period ranging from weeks to months to capture temporal structures of the social activities. A spatial-temporal structure of each social activity is represented as an activity core, where each activity core is defined as active nodes that participate in the social activity with a frequency over a predetermined threshold over the second time period. For each activity core, the system computes statistics of the social activity and uses the statistics to generate a prediction of future behaviors of the social activity.

    SYSTEM AND METHOD FOR LEARNING CONTEXTUALLY AWARE PREDICTIVE KEY PHRASES
    53.
    发明申请
    SYSTEM AND METHOD FOR LEARNING CONTEXTUALLY AWARE PREDICTIVE KEY PHRASES 审中-公开

    公开(公告)号：US20200258120A1

    公开(公告)日：2020-08-13

    申请号：US16710640

    申请日：2019-12-11

    Applicant: HRL Laboratories, LLC

    Inventor： Victor Ardulov ,  Aruna Jammalamadaka ,  Tsai-Ching Lu

    IPC: G06Q30/02 ,  G06Q50/00 ,  G06N20/00 ,  G06F16/903 ,  G06F40/40 ,  G06F40/166

    Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

    System to identify unknown communication behavior relationships from time series
    54.
    发明授权
    System to identify unknown communication behavior relationships from time series 有权

    公开(公告)号：US10528600B1

    公开(公告)日：2020-01-07

    申请号：US16034139

    申请日：2018-07-12

    Applicant: HRL Laboratories, LLC

    Inventor： Kang-Yu Ni ,  Tsai-Ching Lu ,  Qin Jiang ,  David J. Huber

    IPC: H04W16/18 ,  G06F16/28 ,  G06F16/22

    Abstract: Described is a system for identifying communication behavior patterns in communication activity time series. For each pair of variables in the communication activity time series, the system determines a transfer entropy measure, an effective transfer entropy measure from a randomly reordered version of the communication activity time series, and a partial effective transfer entropy measure. A dependency matrix is generated using pair-wised effective transfer entropy measures and partial effective transfer entropy measures, where each element in the matrix represents a total influence of a communication activity time series on another communication activity time series in the future. The dependency matrix is compared with dependency matrices generated from a predefined set of communication patterns to identify the communication behavior pattern. The system generates instructions regarding positioning of a sensor, such that the instructions provide guidance regarding placement of the sensor at a geographical region related to the identified communication pattern.

    INCREASE CHOICE SHARES WITH PERSONALIZED INCENTIVES USING SOCIAL MEDIA DATA
    57.
    发明申请
    INCREASE CHOICE SHARES WITH PERSONALIZED INCENTIVES USING SOCIAL MEDIA DATA 审中-公开

    公开(公告)号：US20170316442A1

    公开(公告)日：2017-11-02

    申请号：US15429125

    申请日：2017-02-09

    Applicant: HRL Laboratories, LLC

    Inventor： Kang-Yu Ni ,  Tsai-Ching Lu ,  John Cafeo

    IPC: G06Q30/02 ,  G06Q50/00

    Abstract: Described is a system for using social media data to supplement survey data for discrete choice analysis. Survey data from consumers is segmented into demographic groups. Individual demographic attributes and consumer product attribute preferences are extracted from a set of social media data. Consumer product attribute preferences are determined for each demographic group using the set of social media data. Consumers' preference coefficients are generated for each demographic group. Finally, individualized incentives for a target consumer product are determined using the consumers' preference coefficients.

    System and methods for digital artifact genetic modeling and forensic analysis
    60.
    发明授权
    System and methods for digital artifact genetic modeling and forensic analysis 有权
    数字神经遗传建模和法医分析的系统和方法

    公开(公告)号：US09224067B1

    公开(公告)日：2015-12-29

    申请号：US13748316

    申请日：2013-01-23

    Applicant: HRL Laboratories, LLC

    Inventor： Tsai-Ching Lu ,  Hankyu Moon ,  Gavin D. Holland ,  David L. Allen ,  Aleksey Nogin ,  Michael D. Howard

    IPC: H04L29/06 ,  G06K9/62

    CPC classification number: H04L63/1408

    Abstract: Described is a cyber security system for digital artifact genetic modeling and forensic analysis. The system identifies the provenance (origin) of a digital artifact by first receiving a plurality of digital artifacts, each digital artifact possessing features. Raw features are extracted from the digital artifacts. The raw features are classified into descriptive genotype-phonotype structures. Finally, lineage, heredity, and provenance of the digital artifacts are determined based on mapping of the genotype-phenotype structures.

    Abstract translation: 描述了一种用于数字神器遗传建模和法医分析的网络安全系统。 该系统通过首先接收多个数字人造物来识别数字人造物的来源（原点），每个数字人造物具有特征。 原始特征是从数字工件中提取的。 原始特征分为描述性基因型 - 语音结构。 最后，根据基因型 - 表型结构的映射确定数字人工制品的谱系，遗传和来源。

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