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    SYSTEM AND METHOD FOR IDENTIFYING CONTACTS OF A TARGET USER IN A SOCIAL NETWORK
    1.
    发明申请
    SYSTEM AND METHOD FOR IDENTIFYING CONTACTS OF A TARGET USER IN A SOCIAL NETWORK 有权
    用于识别目标用户在社交网络中的联系的系统和方法

    公开(公告)号：US20140229406A1

    公开(公告)日：2014-08-14

    申请号：US14065505

    申请日：2013-10-29

    Applicant: Verint Systems Ltd.

    Inventor： Rami Puzis ,  Roni Stern

    IPC: G06N5/00 ,  G06N99/00

    CPC classification number: G06F17/30867 ,  G06F17/30377 ,  G06F17/30528 ,  G06N5/003 ,  G06N99/005 ,  G06Q50/01 ,  H04L67/306

    Abstract: When using Web intelligence (“Webint”) to collect information regarding a target social network user, one of the most valuable pieces of information is the target user's List-Of-Friends (LOF). In some cases, however, the LOF of the target user is not accessible in his profile. Herein are described methods and systems for identifying the LOF of a target user. An analysis system crawls the profiles of social network users, other than the target user, and reconstructs the LOF of the target user from the crawled profiles.

    Abstract translation: 当使用Web Intelligence（“Webint”）收集关于目标社交网络用户的信息时，最有价值的信息之一是目标用户的“List of Of Friends”（LOF）。 然而，在某些情况下，目标用户的LOF在他的配置文件中是不可访问的。 这里描述了用于识别目标用户的LOF的方法和系统。 分析系统抓取目标用户以外的社交网络用户的配置文件，并从爬网的配置文件重建目标用户的LOF。

    SYSTEM AND METHOD FOR APPLYING TRANSFER LEARNING TO IDENTIFICATION OF USER ACTIONS
    8.
    发明申请
    SYSTEM AND METHOD FOR APPLYING TRANSFER LEARNING TO IDENTIFICATION OF USER ACTIONS 审中-公开

    公开(公告)号：US20180260705A1

    公开(公告)日：2018-09-13

    申请号：US15911223

    申请日：2018-03-05

    Applicant: Verint Systems Ltd.

    Inventor： Rami Puzis ,  Asaf Shabtai ,  Gershon Celniker ,  Liron Rosenfeld ,  Ziv Katzir ,  Edita Grolman

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/0454 ,  G06Q30/02 ,  H04L67/22 ,  H04W4/21

    Abstract: Methods and systems for analyzing encrypted traffic, such as to identify, or “classify,” the user actions that generated the traffic. Such classification is performed, even without decrypting the traffic, based on features of the traffic. Such features may include statistical properties of (i) the times at which the packets in the traffic were received, (ii) the sizes of the packets, and/or (iii) the directionality of the packets. To classify the user actions, a processor receives the encrypted traffic and ascertains the types (or “classes”) of user actions that generated the traffic. Unsupervised or semi-supervised transfer-learning techniques may be used to perform the classification process. Using transfer-learning techniques facilitates adapting to different runtime environments, and to changes in the patterns of traffic generated in these runtime environments, without requiring the large amount of time and resources involved in conventional supervised-learning techniques.

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