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    SYSTEMS AND METHODS OF MACHINE LEARNING-BASED SAMPLE CLASSIFIERS FOR PHYSICAL SAMPLES
    1.
    发明公开
    SYSTEMS AND METHODS OF MACHINE LEARNING-BASED SAMPLE CLASSIFIERS FOR PHYSICAL SAMPLES 审中-公开

    公开(公告)号：US20240362462A1

    公开(公告)日：2024-10-31

    申请号：US18648216

    申请日：2024-04-26

    Applicant: ThinkCyte K.K.

    Inventor： Hirofumi Nakayama ,  Ryo Tamoto ,  Yuichi Yanagihashi

    IPC: G06N3/0455 ,  G06N3/09

    CPC classification number: G06N3/0455 ,  G06N3/09

    Abstract: Systems and methods are provided to implement classification of objects, based on sensor data regarding the objects, without labels assigned to the sensor data. A system can include one or more processors. The one or more processors can retrieve sensor data regarding an object. The one or more processors can apply the sensor data as input to a classification model to cause the classification model to determine a classification of the object. The classification model can be configured based on training data that includes a plurality of clusters generated by dimensionality reduction of example data regarding example objects. At least one cluster of the plurality of clusters can be associated with the classification. The one or more processors can output the classification of the object.

    SYSTEMS AND METHODS OF MACHINE LEARNING-BASED PHYSICAL SAMPLE CLASSIFICATION WITH SAMPLE VARIATION CONTROL
    2.
    发明公开
    SYSTEMS AND METHODS OF MACHINE LEARNING-BASED PHYSICAL SAMPLE CLASSIFICATION WITH SAMPLE VARIATION CONTROL 审中-公开

    公开(公告)号：US20240362454A1

    公开(公告)日：2024-10-31

    申请号：US18648220

    申请日：2024-04-26

    Applicant: ThinkCyte K.K.

    Inventor： Hirofumi Nakayama ,  Ryo Tamoto

    IPC: G06N3/042

    CPC classification number: G06N3/042

    Abstract: Systems and methods are provided to implement classification of objects, based on sensor data regarding the objects, in a manner that addresses variations in the sensor data, including measurement variables among the objects. A system can include one or more processors to retrieve sensor data regarding an object that is at least one of cellular material from one or more cells, nucleic acid material, biological material, or chemical material. The one or more processors can apply the sensor data as input to a classifier to cause the classifier to determine a classification of the object, the classifier configured based on feature data from a first example of object data and a second example of object data associated with at least one of a different time of detection or a different subject than the first example.

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