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    LOCATION ALLOCATION PLANNING
    11.
    发明申请
    LOCATION ALLOCATION PLANNING 审中-公开

    公开(公告)号：US20200334582A1

    公开(公告)日：2020-10-22

    申请号：US16390155

    申请日：2019-04-22

    Applicant: International Business Machines Corporation

    Inventor： Ying-Chen Yu ,  June-Ray Lin ,  Yi-Chun Tsai ,  Tzu-Ching Kuo

    IPC: G06Q10/02 ,  G06Q10/04

    Abstract: In an approach location allocation planning, one or more computing units determine at least one location matching model for a first current participating entity of a plurality of current participating entities of a current event, wherein an output of the location matching model indicates a matching degree between the first current participating entity and a current event location. The one or more computing units create at least one initial location allocation plan for the plurality of current participating entities of the event based, at least in part, on the at least one location matching model. The one or more computing units receive feedback from at least one of the plurality of current participating entities. Responsive to the feedback indicating acceptance of the initial location allocation plan, the one or more computing units determine a final location allocation plan based on the initial location allocation plan.

    Automated identification of malware families based on shared evidences
    13.
    发明授权
    Automated identification of malware families based on shared evidences 有权

    公开(公告)号：US12147537B2

    公开(公告)日：2024-11-19

    申请号：US18536736

    申请日：2023-12-12

    Applicant: International Business Machines Corporation

    Inventor： Yu-Siang Chen ,  Ci-Hao Wu ,  Ying-Chen Yu ,  Pao-Chuan Liao ,  June-Ray Lin

    IPC: G06F21/00 ,  G06F21/56 ,  G06N5/02 ,  G06N5/04

    Abstract: A malware family identification engine constructs a graph data structure of direct relationships between malware instances and malware families, direct relationships between malware instances and detected tags, and indirect relationships between detected tags and malware families. The engine builds a dictionary data structure comprising detected tag entries linking each detected tag to one or more malware family nodes based on the graph data structure. The engine identifies significant indirect entities (SIEs) within the detected tag entries of the dictionary data structure and selects a SIE with a highest number of out-going links (OGLs) as a root node in a family tree data structure, recursively connects SIEs with a number of OGLs less than the highest number of OGLs to the root node in the family tree data structure, and converts each SIE name in the family tree data structure to a chained family entity name in the family tree data structure.

    Identifying advertisements embedded in videos
    16.
    发明授权
    Identifying advertisements embedded in videos 有权

    公开(公告)号：US11074457B2

    公开(公告)日：2021-07-27

    申请号：US16386300

    申请日：2019-04-17

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Ying-Chen Yu ,  Jeff Hsueh-Chang Kuo ,  Chih-Wen Su ,  June-Ray Lin

    IPC: G06K9/00 ,  G06K9/62

    Abstract: Embodiments of the present invention are directed to a computer-implemented method for identifying advertisements in a video. The method includes obtaining a plurality of copies of the video and processing each of the plurality of copies of the video, wherein the processing identifies a plurality of iframes in each of the plurality of copies of the video. The method also includes comparing the plurality of iframes of each of the plurality of copies of the video. The method further includes identifying one or more common portions of each of the plurality of copies of the video one or more advertisement in the video, based on the comparison.

    Data migration for applications on a mobile device
    17.
    发明授权
    Data migration for applications on a mobile device 有权

    公开(公告)号：US10929045B2

    公开(公告)日：2021-02-23

    申请号：US16431817

    申请日：2019-06-05

    Applicant: International Business Machines Corporation

    Inventor： Kai Yao Cao ,  Ming Qiao Shang Guan ,  Hui Wang ,  Ying-Chen Yu ,  Jia Wei Zhou

    IPC: G06F3/06 ,  H04L29/08 ,  G06F16/21 ,  G06F16/10 ,  H04W4/38

    Abstract: In various embodiments, a computer-implemented method includes identifying data files in external storage, where the data files correspond to a computer software application (application) on a mobile device. The method may also include sorting the one or more data files into different access levels. The method may also include predicting the sorted one or more data files that will be accessed on the mobile device using a prediction engine. The method may also include locating the predicted one or more data files in the external storage using a migration map. The method may also include determining whether the predicted one or more data files were previously migrated to the external storage from the mobile device. The method may also include migrating the predicted one or more data files from the external storage to the mobile device.

    Automated Identification of Malware Families Based on Shared Evidences
    20.
    发明公开
    Automated Identification of Malware Families Based on Shared Evidences 审中-公开

    公开(公告)号：US20240176880A1

    公开(公告)日：2024-05-30

    申请号：US18536736

    申请日：2023-12-12

    Applicant: International Business Machines Corporation

    Inventor： Yu-Siang Chen ,  Ci-Hao Wu ,  Ying-Chen Yu ,  Pao-Chuan Liao ,  June-Ray Lin

    IPC: G06F21/56 ,  G06N5/02 ,  G06N5/04

    CPC classification number: G06F21/561 ,  G06F21/568 ,  G06N5/02 ,  G06N5/04

    Abstract: A malware family identification engine constructs a graph data structure of direct relationships between malware instances and malware families, direct relationships between malware instances and detected tags, and indirect relationships between detected tags and malware families. The engine builds a dictionary data structure comprising detected tag entries linking each detected tag to one or more malware family nodes based on the graph data structure. The engine identifies significant indirect entities (SIEs) within the detected tag entries of the dictionary data structure and selects a SIE with a highest number of out-going links (OGLs) as a root node in a family tree data structure, recursively connects SIEs with a number of OGLs less than the highest number of OGLs to the root node in the family tree data structure, and converts each SIE name in the family tree data structure to a chained family entity name in the family tree data structure.

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