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    SYSTEMS, METHODS, AND APPARATUS TO IMPROVE MEDIA IDENTIFICATION
    1.
    发明公开
    SYSTEMS, METHODS, AND APPARATUS TO IMPROVE MEDIA IDENTIFICATION 审中-公开

    公开(公告)号：US20240311426A1

    公开(公告)日：2024-09-19

    申请号：US18677371

    申请日：2024-05-29

    申请人： GRACENOTE, INC.

    发明人： Jeffrey Scott ,  Matthew James Wilkinson ,  Robert Coover ,  Shashank Merchant

    IPC分类号： G06F16/901 ,  G06F16/65 ,  G06F16/683 ,  G06F18/2115

    CPC分类号： G06F16/9014 ,  G06F16/65 ,  G06F16/683 ,  G06F18/2115 ,  G06F2218/16

    摘要： Methods, apparatus, systems, and articles of manufacture are disclosed to improve media identification. An example apparatus includes a hash handler to generate a first set of reference matches by performing hash functions on a subset of media data associated with media to generate hashed media data based on a first bucket size, a candidate determiner to identify a second set of reference matches that include ones of the first set, the second set including ones having first quantities of hits that did not satisfy a threshold, determine second quantities of hits for ones of the second set by matching ones to the hash tables based on a second bucket size, and identify one or more candidate matches based on at least one of (1) ones of the first set or (2) ones of the second set, and a report generator to generate a report including a media identification.

    SYSTEMS AND METHODS FOR DETERMINING WHETHER A SUBJECT HAS A CANCER CONDITION USING TRANSFER LEARNING
    2.
    发明公开
    SYSTEMS AND METHODS FOR DETERMINING WHETHER A SUBJECT HAS A CANCER CONDITION USING TRANSFER LEARNING 审中-公开

    公开(公告)号：US20240212848A1

    公开(公告)日：2024-06-27

    申请号：US18523660

    申请日：2023-11-29

    申请人： GRAIL, LLC

    发明人： M. Cyrus MAHER

    IPC分类号： G16H50/20 ,  C12Q1/6886 ,  G06F18/2115 ,  G16H50/30 ,  G16H50/70

    CPC分类号： G16H50/20 ,  G06F18/2115 ,  G16H50/30 ,  G16H50/70 ,  C12Q1/6886

    摘要： Systems and methods for classifier training are provided. A first dataset is obtained that comprises, for each first subject, a corresponding plurality of bin values, each for a bin in a plurality of bins, and subject cancer condition. A feature extraction technique is applied to the first dataset thereby obtaining feature extraction functions, each of which is an independent linear or nonlinear function of bin values of the bins. A second dataset is obtained comprising, for each second subject, a corresponding plurality of bin values, each for a bin in the plurality of bins and subject cancer condition. The plurality of bin values of each corresponding subject in the second plurality are projected onto the respective feature extraction functions, thereby forming a transformed second dataset comprising feature values for each subject. The transformed second dataset and subject cancer condition serves to train a classifier on the cancer condition set.

    Simultaneous hyper parameter and feature selection optimization using evolutionary boosting machines
    3.
    发明授权
    Simultaneous hyper parameter and feature selection optimization using evolutionary boosting machines 有权

    公开(公告)号：US12001931B2

    公开(公告)日：2024-06-04

    申请号：US16219242

    申请日：2018-12-13

    申请人： Allstate Insurance Company

    发明人： Ousef Kuruvilla

    IPC分类号： G06N20/20 ,  G06F18/2115 ,  G06F18/214 ,  G06N3/126 ,  G06N20/00

    CPC分类号： G06N20/20 ,  G06F18/2115 ,  G06F18/214 ,  G06N3/126 ,  G06N20/00

    摘要： Aspects relate to a machine learning system implementing an evolutionary boosting machine. The system may initially select randomized feature sets for an initial generation of candidate models. Evolutionary algorithms may be applied to the system to create later generations of the cycle, combining and mutating the feature selections of the candidate models. The system may determine optimal number of boosting iterations for each candidate model in a generation by building boosting iterations from an initial value up to a predetermined maximum number of boosting iterations. When a final generation is achieved, the system may evaluate the optimal model of the generation. If the optimal boosting iterations of the optimal model does not meet solution constraints on the optimal boosting iterations, the system may adjust a learning rate parameter and then proceed to the next cycle. Based on termination criteria, the system may determine a resulting/final optimal mode.

    Systems and methods for determining whether a subject has a cancer condition using transfer learning
    6.
    发明授权
    Systems and methods for determining whether a subject has a cancer condition using transfer learning 有权

    公开(公告)号：US11869661B2

    公开(公告)日：2024-01-09

    申请号：US16881928

    申请日：2020-05-22

    申请人： GRAIL, LLC

    发明人： M. Cyrus Maher

    IPC分类号： G16H50/20 ,  G16H50/70 ,  G16H50/30 ,  G06F18/2115 ,  C12Q1/6886

    CPC分类号： G16H50/20 ,  G06F18/2115 ,  G16H50/30 ,  G16H50/70 ,  C12Q1/6886

    摘要： Systems and methods for classifier training are provided. A first dataset is obtained that comprises, for each first subject, a corresponding plurality of bin values, each for a bin in a plurality of bins, and subject cancer condition. A feature extraction technique is applied to the first dataset thereby obtaining feature extraction functions, each of which is an independent linear or nonlinear function of bin values of the bins. A second dataset is obtained comprising, for each second subject, a corresponding plurality of bin values, each for a bin in the plurality of bins and subject cancer condition. The plurality of bin values of each corresponding subject in the second plurality are projected onto the respective feature extraction functions, thereby forming a transformed second dataset comprising feature values for each subject. The transformed second dataset and subject cancer condition serves to train a classifier on the cancer condition set.