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    GENERATING AND EXECUTING AUTOMATIC SUGGESTIONS TO MODIFY DATA OF INGESTED DATA COLLECTIONS WITHOUT ADDITIONAL DATA INGESTION
    2.
    发明申请
    GENERATING AND EXECUTING AUTOMATIC SUGGESTIONS TO MODIFY DATA OF INGESTED DATA COLLECTIONS WITHOUT ADDITIONAL DATA INGESTION 有权

    公开(公告)号：US20220398230A1

    公开(公告)日：2022-12-15

    申请号：US17347133

    申请日：2021-06-14

    Applicant: Adobe Inc.

    Inventor： Ritwik Sinha ,  Saayan Mitra ,  Handong Zhao ,  Somdeb Sarkhel ,  Trevor Paulsen ,  William Brandon George

    IPC: G06F16/215 ,  G06F16/242 ,  G06N5/04 ,  G06N20/00

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.

    Online training and update of factorization machines using alternating least squares optimization
    3.
    发明授权
    Online training and update of factorization machines using alternating least squares optimization 有权

    公开(公告)号：US11049041B2

    公开(公告)日：2021-06-29

    申请号：US15963737

    申请日：2018-04-26

    Applicant: Adobe Inc.

    Inventor： Saayan Mitra ,  Xueyu Mao ,  Viswanathan Swaminathan ,  Somdeb Sarkhel ,  Sheng Li

    IPC: G06N20/00 ,  G06F17/16 ,  G06N5/04

    Abstract: Techniques are disclosed for training of factorization machines (FMs) using a streaming mode alternating least squares (ALS) optimization. A methodology implementing the techniques according to an embodiment includes receiving a datapoint that includes a feature vector and an associated target value. The feature vector includes user identification, subject matter identification, and a context. The target value identifies an opinion of the user relative to the subject matter. The method further includes applying an FM to the feature vector to generate an estimate of the target value, and updating parameters of the FM for training of the FM. The parameter update is based on application of a streaming mode ALS optimization to: the datapoint; the estimate of the target value; and to an updated summation of intermediate calculated terms generated by application of the streaming mode ALS optimization to previously received datapoints associated with prior parameter updates of the FM.

    UTILIZING MACHINE LEARNING TO GENERATE PARAMETRIC DISTRIBUTIONS FOR DIGITAL BIDS IN A REAL-TIME DIGITAL BIDDING ENVIRONMENT
    4.
    发明申请
    UTILIZING MACHINE LEARNING TO GENERATE PARAMETRIC DISTRIBUTIONS FOR DIGITAL BIDS IN A REAL-TIME DIGITAL BIDDING ENVIRONMENT 审中-公开

    公开(公告)号：US20200226675A1

    公开(公告)日：2020-07-16

    申请号：US16248287

    申请日：2019-01-15

    Applicant: Adobe Inc.

    Inventor： Saayan Mitra ,  Aritra Ghosh ,  Somdeb Sarkhel ,  Jiatong Xie

    IPC: G06Q30/08 ,  G06N20/00

    Abstract: The present disclosure relates to generating digital bids for providing digital content to remote client devices based on parametric bid distributions generated using a machine learning model (e.g., a mixture density network). For example, in response to identifying a digital bid request in a real-time bidding environment, the disclosed systems can utilize a trained parametric censored machine learning model to generate a parametric bid distribution. To illustrate, the disclosed systems can utilize a parametric censored, mixture density machine learning model to analyze bid request characteristics and generate a parametric, multi-modal distribution reflecting a plurality of parametric means, parametric variances, and combination weights. The disclosed systems can then utilize the parametric, multi-modal distribution to generate digital bids in response to the digital bid request in real-time (e.g., while a client device accesses digital assets corresponding to the bid request).

    Automatic Item Placement Recommendations Based on Entity Similarity
    7.
    发明申请
    Automatic Item Placement Recommendations Based on Entity Similarity 有权

    公开(公告)号：US20210110432A1

    公开(公告)日：2021-04-15

    申请号：US16598933

    申请日：2019-10-10

    Applicant: Adobe Inc.

    Inventor： Xiang Chen ,  Viswanathan Swaminathan ,  Somdeb Sarkhel

    IPC: G06Q30/02 ,  G06F16/28 ,  G06F16/22

    Abstract: Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.

    Generating and executing automatic suggestions to modify data of ingested data collections without additional data ingestion
    9.
    发明授权
    Generating and executing automatic suggestions to modify data of ingested data collections without additional data ingestion 有权

    公开(公告)号：US12182086B2

    公开(公告)日：2024-12-31

    申请号：US17347133

    申请日：2021-06-14

    Applicant: Adobe Inc.

    Inventor： Ritwik Sinha ,  Saayan Mitra ,  Handong Zhao ,  Somdeb Sarkhel ,  Trevor Paulsen ,  William Brandon George

    IPC: G06F16/215 ,  G06F16/242 ,  G06N5/04 ,  G06N20/00

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.

    DOCUMENT RECOMMENDATION USING CONTEXTUAL EMBEDDINGS
    10.
    发明申请
    DOCUMENT RECOMMENDATION USING CONTEXTUAL EMBEDDINGS 有权

    公开(公告)号：US20240403339A1

    公开(公告)日：2024-12-05

    申请号：US18328925

    申请日：2023-06-05

    Applicant: ADOBE INC.

    Inventor： Vahid Azizi ,  Chun Hao Wang ,  Somdeb Sarkhel ,  Saayan Mitra ,  Richard Pong Nam Sinn

    IPC: G06F16/33 ,  G06F40/205 ,  G06F40/30

    Abstract: Systems and methods for generating contextual document embeddings and recommending similar articles based on the document embeddings are described. Embodiments are configured to receive a document query and encode a plurality of candidate sentences from a candidate document to obtain a plurality of contextual sentence embeddings. The contextual sentence embeddings each represent a semantic context of a corresponding sentence from the plurality of candidate sentences. Embodiments then generate a candidate document embedding by combining the plurality of contextual sentence embeddings and provide the candidate document in response to the document query based on the candidate document embedding.

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