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    Systems for Predicting a Terminal Event
    1.
    发明申请
    Systems for Predicting a Terminal Event 有权

    公开(公告)号：US20210342649A1

    公开(公告)日：2021-11-04

    申请号：US16866261

    申请日：2020-05-04

    Applicant: Adobe Inc.

    Inventor： Amit Doda ,  Gaurav Sinha ,  Kai Yeung Lau ,  Akangsha Sunil Bedmutha ,  Shiv Kumar Saini ,  Ritwik Sinha ,  Vaidyanathan Venkatraman ,  Niranjan Shivanand Kumbi ,  Omar Rahman ,  Atanu R. Sinha

    IPC: G06K9/62 ,  G06F17/18 ,  G06N20/00 ,  G06F3/0481

    Abstract: In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time.

    METHOD TO EXPLAIN FACTORS INFLUENCING AI PREDICTIONS WITH DEEP NEURAL NETWORKS
    2.
    发明申请
    METHOD TO EXPLAIN FACTORS INFLUENCING AI PREDICTIONS WITH DEEP NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200320381A1

    公开(公告)日：2020-10-08

    申请号：US16375037

    申请日：2019-04-04

    Applicant: ADOBE INC.

    Inventor： Vaidyanathan Venkatraman ,  Rajan Madhavan ,  Omar Rahman ,  Niranjan Shivanand Kumbi ,  Brajendra Kumar Bhujabal ,  Ajay Awatramani

    IPC: G06N3/08 ,  G06Q10/06

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of trained machine learning models with similar results to the trained DNN are selected for further evaluation. The preliminary set of machine learning models may be evaluated using a distribution analysis to select a reduced set of machine learning models. Results produced by the reduced set of machine learning models are compared, point-by-point, to the results produced by the DNN. The best performing machine learning model with generated results that performs closest to the DNN generated results may be selected. One or more factors used by the selected machine learning model are determined. Those one or more factors from the selected best performing machine learning model may be provided to explain the results of the DNN and increase confidence in the understanding and accuracy of the results generated by the DNN.

    Method to explain factors influencing AI predictions with deep neural networks
    6.
    发明授权
    Method to explain factors influencing AI predictions with deep neural networks 有权

    公开(公告)号：US11501161B2

    公开(公告)日：2022-11-15

    申请号：US16375037

    申请日：2019-04-04

    Applicant: ADOBE INC.

    Inventor： Vaidyanathan Venkatraman ,  Rajan Madhavan ,  Omar Rahman ,  Niranjan Shivanand Kumbi ,  Brajendra Kumar Bhujabal ,  Ajay Awatramani

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06Q10/06

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of trained machine learning models with similar results to the trained DNN are selected for further evaluation. The preliminary set of machine learning models may be evaluated using a distribution analysis to select a reduced set of machine learning models. Results produced by the reduced set of machine learning models are compared, point-by-point, to the results produced by the DNN. The best performing machine learning model with generated results that performs closest to the DNN generated results may be selected. One or more factors used by the selected machine learning model are determined. Those one or more factors from the selected best performing machine learning model may be provided to explain the results of the DNN and increase confidence in the understanding and accuracy of the results generated by the DNN.

    SELECTING TARGET AUDIENCES FOR MARKETING CAMPAIGNS
    7.
    发明申请
    SELECTING TARGET AUDIENCES FOR MARKETING CAMPAIGNS 有权

    公开(公告)号：US20210342866A1

    公开(公告)日：2021-11-04

    申请号：US16861756

    申请日：2020-04-29

    Applicant: Adobe Inc.

    Inventor： Camille Girabawe ,  Richard Yang ,  Goutham Srivatsav Arra ,  Akangsha Sunil Bedmutha ,  Omar Rahman ,  Niranjan Kumbi ,  Vaidyanathan Venkatraman

    IPC: G06Q30/02 ,  G06F16/28 ,  G06F16/901 ,  G06F16/9535

    Abstract: Techniques are disclosed for selecting audience members for a marketing campaign. A list of potential members is accessed, where each member is associated with a corresponding feature vector comprising features. A subset of the features is selected, and used to select a first group from the list for inclusion in the campaign, thereby also defining a second group from the list for exclusion from the campaign. A first similarity among the members in the first group is compared to a second similarity between the members in the first and second groups. If the first similarity is equal to or lower than the second similarity, the subset of features is updated to form a new subset of features, and the selection process of target audience member is repeated, until the first similarity becomes higher than the second similarity. Subsequently, the marketing campaign is launched with the first group of members.

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