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    Providing insights and suggestions for journeys
    43.
    发明授权
    Providing insights and suggestions for journeys 有权

    公开(公告)号：US11861636B2

    公开(公告)日：2024-01-02

    申请号：US16910357

    申请日：2020-06-24

    Applicant: ADOBE INC.

    Inventor： Pankhri Singhai ,  Piyush Gupta ,  Balaji Krishnamurthy ,  Jayakumar Subramanian ,  Nikaash Puri

    IPC: G06Q30/02 ,  G06Q30/0204 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q10/0633

    CPC classification number: G06Q30/0205 ,  G06N20/00 ,  G06Q10/0633 ,  G06Q30/0201

    Abstract: Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained. A journey can include journey paths indicating workflows through which audience members can traverse. The journey data can include audience member attributes (e.g., demographics) and labels indicating journey paths traversed by audience members. A set of audience segments are determined that describe a set of audience members traversing a particular journey path. The set of audience segments can be determined using the journey data to train a segmentation model and, thereafter, analyzing the segmentation model to identify patterns that indicate audience segments associated with the particular journey path. An indication of the set of audience segments that describe the set of audience members traversing the particular journey path can be provided for display.

    CUSTOMER JOURNEY MANAGEMENT USING MACHINE LEARNING
    46.
    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.

    Entropy based synthetic data generation for augmenting classification system training data
    47.
    发明授权
    Entropy based synthetic data generation for augmenting classification system training data 有权

    公开(公告)号：US11423264B2

    公开(公告)日：2022-08-23

    申请号：US16659147

    申请日：2019-10-21

    Applicant: Adobe Inc.

    Inventor： Pinkesh Badjatiya ,  Nikaash Puri ,  Ayush Chopra ,  Anubha Kabra

    IPC: G06K9/62 ,  G06N20/10

    Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.

    Rule determination for black-box machine-learning models
    48.
    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.

    Machine-Learning Based Multi-Step Engagement Strategy Modification
    49.
    发明申请
    Machine-Learning Based Multi-Step Engagement Strategy Modification 有权

    公开(公告)号：US20210319473A1

    公开(公告)日：2021-10-14

    申请号：US17355907

    申请日：2021-06-23

    Applicant: Adobe Inc.

    Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

    IPC: G06Q30/02 ,  G06N20/00

    Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

    Collaborative-filtered content recommendations with justification in real-time
    50.
    发明授权
    Collaborative-filtered content recommendations with justification in real-time 有权

    公开(公告)号：US10762153B2

    公开(公告)日：2020-09-01

    申请号：US15823331

    申请日：2017-11-27

    Applicant: Adobe Inc.

    Inventor： Nikaash Puri ,  Piyush Gupta

    IPC: G06F16/9535 ,  G06Q30/02 ,  G06F16/9536 ,  H04L29/08

    Abstract: Collaborative-filtered content recommendations with justification in real-time is described. A recommendation system determines these recommendations, in part, by identifying digital content items of a catalog that are associated with a single attribute used to describe digital content. The attribute used for the identification is based on affinity scores computed for a client device user to which the recommendations are being provided. These affinity scores indicate the client device user's affinity for different attributes used to describe the digital content. Once the digital content items are identified based on the one attribute, the recommendation system is then limited to ranking and selecting from the identified digital content items to provide the recommendations. The recommendation system does not process the entire catalog of digital content items at once to rank and select the items. Due to this, the described recommendation system performs less computing and is therefore faster than conventional recommendation systems.

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