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    Rule Determination for Black-Box Machine-Learning Models
    4.
    发明申请
    Rule Determination for Black-Box Machine-Learning Models 审中-公开

    公开(公告)号：US20190147369A1

    公开(公告)日：2019-05-16

    申请号：US15812991

    申请日：2017-11-14

    Applicant: Adobe Inc.

    Inventor： Piyush Gupta ,  Sukriti Verma ,  Pratiksha Agarwal ,  Nikaash Puri ,  Balaji Krishnamurthy

    IPC: G06N99/00 ,  G06N5/02

    Abstract: Rule determination for black-box machine-learning models (BBMLMs) is described. These rules are determined by an interpretation system to describe operation of a BBMLM to associate inputs to the BBMLM with observed outputs of the BBMLM and without knowledge of the logic used in operation by the BBMLM to make these associations. To determine these rules, the interpretation system initially generates a proxy black-box model to imitate the behavior of the BBMLM based solely on data indicative of the inputs and observed outputs—since the logic actually used is not available to the system. The interpretation system generates rules describing the operation of the BBMLM by combining conditions—identified based on output of the proxy black-box model—using a genetic algorithm. These rules are output as if-then statements configured with an if-portion formed as a list of the conditions and a then-portion having an indication of the associated observed output.

    Customer journey management using machine learning
    6.
    发明授权
    Customer journey management using machine learning 有权

    公开(公告)号：US12205127B2

    公开(公告)日：2025-01-21

    申请号：US17232591

    申请日：2021-04-16

    Applicant: ADOBE INC.

    Inventor： Sukriti Verma ,  Shripad Deshmukh ,  Jayakumar Subramanian ,  Piyush Gupta ,  Nikaash Puri

    IPC: G06Q30/0201 ,  G06N3/047 ,  G06N3/08

    Abstract: Interactions between a user and an e-commerce platform are automatically guided to increase the chances of a conversion. Previous sequences of interactions (e.g., conversion journeys and non-conversion journeys) with the e-commerce platform are collected, an artificial neural network (ANN) learns how to estimate a safety value a current user state by learning from previous user interactions (e.g., conversion and non-conversion journeys), a software agent of the e-commerce platform applies a current user state of the user to the ANN to determine a current safety value, and the software agent provides content to the user based on the current safety value and the current user state.

    CUSTOMER JOURNEY MANAGEMENT USING MACHINE LEARNING
    9.
    发明申请
    CUSTOMER JOURNEY MANAGEMENT USING MACHINE LEARNING 有权

    公开(公告)号：US20220335508A1

    公开(公告)日：2022-10-20

    申请号：US17232591

    申请日：2021-04-16

    Applicant: ADOBE INC.

    Inventor： Sukriti Verma ,  Shripad Deshmukh ,  Jayakumar Subramanian ,  Piyush Gupta ,  Nikaash Puri

    IPC: G06Q30/06 ,  G06N3/08 ,  G06F9/451

    Abstract: Interactions between a user and an e-commerce platform are automatically guided to increase the chances of a conversion. Previous sequences of interactions (e.g., conversion journeys and non-conversion journeys) with the e-commerce platform are collected, an artificial neural network (ANN) learns how to estimate a safety value a current user state by learning from previous user interactions (e.g., conversion and non-conversion journeys), a software agent of the e-commerce platform applies a current user state of the user to the ANN to determine a current safety value, and the software agent provides content to the user based on the current safety value and the current user state.

    Rule determination for black-box machine-learning models
    10.
    发明授权
    Rule determination for black-box machine-learning models 有权

    公开(公告)号：US11354590B2

    公开(公告)日：2022-06-07

    申请号：US15812991

    申请日：2017-11-14

    Applicant: Adobe Inc.

    Inventor： Piyush Gupta ,  Sukriti Verma ,  Pratiksha Agarwal ,  Nikaash Puri ,  Balaji Krishnamurthy

    IPC: G06N20/00 ,  G06N5/04 ,  G06N3/12 ,  G06N3/08 ,  G06N5/00 ,  G06N20/20 ,  G06N5/02

    Abstract: Rule determination for black-box machine-learning models (BBMLMs) is described. These rules are determined by an interpretation system to describe operation of a BBMLM to associate inputs to the BBMLM with observed outputs of the BBMLM and without knowledge of the logic used in operation by the BBMLM to make these associations. To determine these rules, the interpretation system initially generates a proxy black-box model to imitate the behavior of the BBMLM based solely on data indicative of the inputs and observed outputs—since the logic actually used is not available to the system. The interpretation system generates rules describing the operation of the BBMLM by combining conditions—identified based on output of the proxy black-box model—using a genetic algorithm. These rules are output as if-then statements configured with an if-portion formed as a list of the conditions and a then-portion having an indication of the associated observed output.

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