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    Determining object information that does not explicitly appear in a media unit signature
    64.
    发明授权
    Determining object information that does not explicitly appear in a media unit signature 有权

    公开(公告)号：US11132548B2

    公开(公告)日：2021-09-28

    申请号：US16808405

    申请日：2020-03-04

    Applicant: CORTICA LTD.

    Inventor： Igal Raichelgauz ,  Kirill Dyagilev

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

    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.

    Autonomous Grading of Gemstones
    65.
    发明申请
    Autonomous Grading of Gemstones 有权

    公开(公告)号：US20210148831A1

    公开(公告)日：2021-05-20

    申请号：US16942727

    申请日：2020-07-29

    Applicant: Cortica Ltd.

    Inventor： Igal Raichelgauz ,  Karina Odinaev

    IPC: G01N21/87 ,  G01J3/46 ,  G01N21/95 ,  G01N33/38 ,  G06K9/46

    Abstract: A method for grading a precious gem. The method may include generating, by a signature generator, a signature of the precious gem; searching, out of multiple concept structures, for a matching concept structures that comprises at least one reference signature that matches the signature of the precious gem; wherein each concept structure comprises precious gem signatures of the same grade; wherein at least two concept structures differ from each other by grade; wherein each concept structure is generated by applying an unsupervised learning process and associating a grade with the cluster; and determining the grade of the precious gem based on a grade associated with a matching concept structure.

    System and method for linking multimedia data elements to web pages
    67.
    发明授权
    System and method for linking multimedia data elements to web pages 有权

    公开(公告)号：US10831814B2

    公开(公告)日：2020-11-10

    申请号：US14321231

    申请日：2014-07-01

    Applicant: Cortica, Ltd.

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

    IPC: G06N5/02 ,  G06F16/43 ,  G06F16/955

    Abstract: A method and system for linking a multimedia data element (MMDE) and a webpage are provided. The method includes receiving a MMDE from a source; generating a signature representative of the MMDE using a plurality of computational cores; matching the generated signature with a plurality of signatures stored in a database to find at least one matching signature, wherein at least one of the stored signatures has at least one corresponding universal resource locator (URL) of a web page stored therein as metadata of the at least one of the stored signatures; and providing to the source at least a URL that is a metadata of a matched signature upon determination of a match between the generated signature and at least one of the plurality of signatures stored in the database.

    METHOD FOR OBJECT DETECTION USING SHALLOW NEURAL NETWORKS
    69.
    发明申请
    METHOD FOR OBJECT DETECTION USING SHALLOW NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200311517A1

    公开(公告)日：2020-10-01

    申请号：US16681885

    申请日：2019-11-13

    Applicant: Cortica LTD.

    Inventor： Igal Raichelgauz ,  Roi Saida

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

    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.

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