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    Reactively identifying software products exhibiting anomalous behavior
    11.
    发明授权
    Reactively identifying software products exhibiting anomalous behavior 有权

    公开(公告)号：US10380339B1

    公开(公告)日：2019-08-13

    申请号：US14727495

    申请日：2015-06-01

    Applicant: Amazon Technologies, Inc.

    Inventor： Srikar Appalaraju ,  Amol Wanjari ,  Amit Arora ,  Vipul Bhargava ,  Ashish Hari Chiplunkar ,  Vineet Khare ,  Chellappan Lakshmanan

    IPC: G06F12/14 ,  G06F21/56

    Abstract: Techniques are disclosed herein for reactively identifying software products, available from an electronic marketplace, that are exhibiting anomalous behavior. Data associated with software products is accessed and analyzed to determine anomalous behavior. The data analyzed may include, but is not limited to, crash data, ratings data, marketplace data, usage data, and the like. A machine learning mechanism may be used to classify the application into a category relating to whether a potential anomaly is identified for the software product. A score may also be calculated for the software applications that indicates a severity of the anomalous behavior. The classification and/or the score may be used to determine whether to perform further analysis or testing with regard to a software product. For instance, the score may be used to determine that the software product is to be tested by a testing service.

    Item attribute based data mining system
    12.
    发明授权
    Item attribute based data mining system 有权

    公开(公告)号：US10073892B1

    公开(公告)日：2018-09-11

    申请号：US14738097

    申请日：2015-06-12

    Applicant: Amazon Technologies, Inc.

    Inventor： Vineet Khare ,  Aswin Natarajan

    IPC: G06F17/30

    CPC classification number: G06F16/2465 ,  G06F16/26

    Abstract: Data mining systems and methods are disclosed for item recommendation based on frequent attribute-values associated with items. The system may determine commonalities in item attribute-values based on user transactions and identify frequent attribute-value tuples that include attribute-values that frequently co-occur in user transactions. The system may associate user interests with the frequent attribute-value tuples and recommend items to target users based thereon. A user-interface for presenting the recommendation allows users to explore item recommendations based on modifications to one or more frequent attribute-value tuples initially recommended to the user

    Integrated machine learning training
    13.
    发明授权
    Integrated machine learning training 有权

    公开(公告)号：US12118456B1

    公开(公告)日：2024-10-15

    申请号：US16198730

    申请日：2018-11-21

    Applicant: Amazon Technologies, Inc.

    Inventor： Sahika Genc ,  Bharathan Balaji ,  Urvashi Chowdhary ,  Leo Parker Dirac ,  Saurabh Gupta ,  Vineet Khare ,  Sunil Mallya Kasaragod

    IPC: G06N3/08 ,  G06F30/20

    CPC classification number: G06N3/08 ,  G06F30/20

    Abstract: A machine learning environment utilizing training data generated by customer networks. A reinforcement learning machine learning environment receives and processes training data generated by simulated hosted, or integrated, customer networks. The reinforcement learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the integrated customer networks. The customer networks include an agent process that collects training data and forwards the training data to the machine learning clusters. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configure the application of the reinforcement learning machine learning processes.

    Decoupled machine learning training
    15.
    发明授权
    Decoupled machine learning training 有权

    公开(公告)号：US11861490B1

    公开(公告)日：2024-01-02

    申请号：US16198726

    申请日：2018-11-21

    Applicant: Amazon Technologies, Inc.

    Inventor： Saurabh Gupta ,  Bharathan Balaji ,  Leo Parker Dirac ,  Sahika Genc ,  Vineet Khare ,  Ragav Venkatesan ,  Gurumurthy Swaminathan

    IPC: G06N3/08 ,  G06N3/04 ,  G06F18/214 ,  G06F18/21

    CPC classification number: G06N3/08 ,  G06F18/214 ,  G06F18/2178 ,  G06N3/04

    Abstract: A machine learning environment utilizing training data generated by customer environments. A reinforced learning machine learning environment receives and processes training data generated by independently hosted, or decoupled, customer environments. The reinforced learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the decoupled customer environments. The customer environments include an agent process that collects training data and forwards the training data to the machine learning clusters without exposing the customer environment. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configured the application of the reinforced learning machine learning processes.

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