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    ISOLATING UNIQUE AND REPRESENTATIVE PATTERNS OF A CONCEPT STRUCTURE
    113.
    发明申请
    ISOLATING UNIQUE AND REPRESENTATIVE PATTERNS OF A CONCEPT STRUCTURE 有权

    公开(公告)号：US20220392197A1

    公开(公告)日：2022-12-08

    申请号：US17805855

    申请日：2022-06-07

    Applicant: Cortica Ltd.

    Inventor： Karina Odinaev

    IPC: G06V10/762 ,  G06V10/74

    Abstract: Systems, and method and computer readable media that store instructions for obtaining a first group concept structure that comprises first identifiers of first objects that belong to a first group; obtaining a second group concept structure that comprises second identifiers of second objects that belong to a second group; wherein the first identifiers were generated by processing media units that captured the first objects; wherein the second identifiers were generated by processing media units that captured the second objects; searching for shared pattern segments, each shared pattern segment appears in at least one first identifier and at least one second identifier; wherein a single shared pattern segment is indicative of a match; wherein a single non-shared pattern segment is suffice to represent a match; and responding to a finding of one or more shared pattern segments.

    System and method for determining common patterns in multimedia content elements based on key points
    114.
    发明授权
    System and method for determining common patterns in multimedia content elements based on key points 有权

    公开(公告)号：US11195043B2

    公开(公告)日：2021-12-07

    申请号：US15455363

    申请日：2017-03-10

    Applicant: Cortica, Ltd.

    Inventor： Dan Elharrar ,  Marco Rossi

    IPC: G06K9/46 ,  G06K9/00

    Abstract: A system and method for method for determining common patterns based on key points in multimedia data elements (MMDEs). The method includes: identifying a plurality of candidate key points in each of the plurality of MMDEs, wherein a size of each candidate key point is equal to a predetermined size and a scale of each candidate key point is equal to a predetermined scale; analyzing the identified candidate key points to determine a set of properties for each candidate key point; comparing the sets of properties of the plurality of candidate key points of each MMDE; selecting, for each MMDE, a plurality of key points from among the candidate key points based on the comparison; generating, based on the key points for each MMDE, a signature for the MMDE; and comparing the signatures of the plurality of MMDEs to output at least one common pattern among the plurality of MMDEs.

    Method for object detection using shallow neural networks
    117.
    发明授权
    Method for object detection using shallow neural networks 有权

    公开(公告)号：US10789527B1

    公开(公告)日：2020-09-29

    申请号：US16681885

    申请日：2019-11-13

    Applicant: Cortica LTD.

    Inventor： Igal Raichelgauz ,  Roi Saida

    IPC: G06N3/04 ,  G06T11/20 ,  G06K9/00 ,  G06N20/00

    Abstract: A method that may include feeding an input image and downscaled versions of the input image to multiple branches of an object detector calculating, by the multiple branches, candidate bounding boxes; and selecting bounding boxes. The multiple branches comprise multiple shallow neural networks that are followed by multiple region units. Each branch includes a shallow neural network and a region unit. The multiple shallow neural networks are multiple instances of a single trained shallow neural network. The single trained shallow neural network is trained to detect objects having a size that is within a predefined size range and to ignore objects having a size that is outside the predefined size range.

    DETERMINING OBJECT INFORMATION THAT DOES NOT EXPLICITLY APPEAR IN A MEDIA UNIT SIGNATURE
    118.
    发明申请
    DETERMINING OBJECT INFORMATION THAT DOES NOT EXPLICITLY APPEAR IN A MEDIA UNIT SIGNATURE 审中-公开

    公开(公告)号：US20200302174A1

    公开(公告)日：2020-09-24

    申请号：US16808405

    申请日：2020-03-04

    Applicant: CORTICA LTD.

    Inventor： Igal Raichelgauz ,  Kirill Dyagilev

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

    Abstract: There may be provided a method for determining object information related to an object that appears in a media element, the method may include (a) receiving or generating a signature of the media element, wherein the signature may include identifiers, wherein the identifiers may include an object identifier related to the object, wherein the object identifier points to locations of output elements, within an output of a convolutional neural network, of elements of significance in relation to the object; (b) finding a group of contributing portions of the media element that had a significant contribution to the generation, by the convolutional neural network, of the output elements of significance in relation to the object; and (c) determining object information related to the object based on a distribution of the group of the contributing portions of the media element.

    SYSTEM AND METHOD FOR CLUSTERING MULTIMEDIA CONTENT ELEMENTS
    120.
    发明申请
    SYSTEM AND METHOD FOR CLUSTERING MULTIMEDIA CONTENT ELEMENTS 审中-公开

    公开(公告)号：US20200233891A1

    公开(公告)日：2020-07-23

    申请号：US16843447

    申请日：2020-04-08

    Applicant: CORTICA LTD.

    Inventor： Igal Raichelgauz ,  Karina Odinaev ,  Yehoshua Y. ZEEVI

    IPC: G06F16/41

    Abstract: A system and method for clustering multimedia content. The method includes: detecting at least one clustering trigger event related to at least one multimedia content element to be clustered; generating at least one signature for the at least one multimedia content element, each signature representing at least a portion of the at least one multimedia content element; determining, based on the generated at least one signature, at least one multimedia content element cluster, wherein each multimedia content element cluster includes a plurality of clustered multimedia content elements sharing at least one common concept with the at least one multimedia content element; and adding, to each determined cluster, the at least one multimedia content element.

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