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    Provisioning interactive content based on predicted user-engagement levels
    1.
    发明授权
    Provisioning interactive content based on predicted user-engagement levels 有权

    公开(公告)号：US11886964B2

    公开(公告)日：2024-01-30

    申请号：US17322108

    申请日：2021-05-17

    Applicant: Adobe Inc.

    Inventor： Atanu R. Sinha ,  Xiang Chen ,  Sungchul Kim ,  Omar Rahman ,  Jean Bernard Hishamunda ,  Goutham Srivatsav Arra ,  Shiv Kumar Saini

    IPC: G06N20/00 ,  G06F3/0484 ,  H04L67/50

    CPC classification number: G06N20/00 ,  G06F3/0484 ,  H04L67/535

    Abstract: Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.

    ARTIFICIAL INTELLIGENCE APPROACHES FOR PREDICTING CONVERSION ACTIVITY PROBABILITY SCORES AND KEY PERSONAS FOR TARGET ENTITIES
    2.
    发明申请
    ARTIFICIAL INTELLIGENCE APPROACHES FOR PREDICTING CONVERSION ACTIVITY PROBABILITY SCORES AND KEY PERSONAS FOR TARGET ENTITIES 有权

    公开(公告)号：US20220394337A1

    公开(公告)日：2022-12-08

    申请号：US17339700

    申请日：2021-06-04

    Applicant: Adobe Inc.

    Inventor： Liana Vagharshakian ,  Atanu R. Sinha ,  Camille Girabawe ,  Gautam Choudhary ,  Omar Rahman ,  Scott Trafton ,  Vivek Sinha

    IPC: H04N21/466 ,  H04N21/45 ,  H04N21/258

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.

    OPTIMIZING SEND TIME FOR ELECTRONIC COMMUNICATIONS
    4.
    发明申请
    OPTIMIZING SEND TIME FOR ELECTRONIC COMMUNICATIONS 有权

    公开(公告)号：US20220245446A1

    公开(公告)日：2022-08-04

    申请号：US17164111

    申请日：2021-02-01

    Applicant: ADOBE INC.

    Inventor： Saayan Mitra ,  Xiang Chen ,  Akangsha Sunil Bedmutha ,  Viswanathan Swaminathan ,  Omar Rahman ,  Camille Girabawe

    IPC: G06N3/08 ,  G06N3/04

    Abstract: An improved electronic communication system schedules transmission of electronic communications based on a predicted open time and click time. The open and click times are predicted from a machine learning model that is trained to optimize for both tasks. Additionally, when training the machine learning model, the loss used for adjusting the system to achieve a desired accuracy may be a biased loss determined from a function that penalizes overpredicting the open time. As such, the loss value may be determined by different set of rules depending on whether the predicted time is greater than the actual time or not.

    Method to explain factors influencing AI predictions with deep neural networks
    7.
    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
    8.
    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.

    PROVISIONING INTERACTIVE CONTENT BASED ON PREDICTED USER-ENGAGEMENT LEVELS
    9.
    发明申请
    PROVISIONING INTERACTIVE CONTENT BASED ON PREDICTED USER-ENGAGEMENT LEVELS 有权

    公开(公告)号：US20220366299A1

    公开(公告)日：2022-11-17

    申请号：US17322108

    申请日：2021-05-17

    Applicant: Adobe Inc.

    Inventor： Atanu R. Sinha ,  Xiang Chen ,  Sungchul Kim ,  Omar Rahman ,  Jean Bernard Hishamunda ,  Goutham Srivatsav Arra

    IPC: G06N20/00 ,  G06F3/0484 ,  H04L29/08

    Abstract: Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.

    Systems for Predicting a Terminal Event
    10.
    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.

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