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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

    申请人： Adobe Inc.

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

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

    摘要： 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

    申请人： Adobe Inc.

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

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

    摘要： 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.

    Latency optimization for digital asset compression
    4.
    发明授权
    Latency optimization for digital asset compression 有权

    公开(公告)号：US10942914B2

    公开(公告)日：2021-03-09

    申请号：US15788146

    申请日：2017-10-19

    申请人： ADOBE INC.

    发明人： Viswanathan Swaminathan ,  Saayan Mitra

    IPC分类号： G06F16/00 ,  G06F16/23 ,  G06F16/583 ,  G06F16/174

    摘要： Embodiments of the present disclosure provide systems, methods, and computer storage media for mitigating delays typically experienced when training codebooks during the encoding process. Instead of training a codebook based on a single digital asset, multiple digital assets determined to have asset characteristics in common can be grouped together to form a group of digital assets, from which a single codebook can be trained. The group of digital assets together form a codebook training set, such that each digital asset therein can be analyzed, in parallel, to expeditiously train a single codebook. A codebook trained in this manner can be employed to encode other digital assets sharing the asset characteristics as those in the codebook training set.

    UTILIZING MACHINE LEARNING TO GENERATE PARAMETRIC DISTRIBUTIONS FOR DIGITAL BIDS IN A REAL-TIME DIGITAL BIDDING ENVIRONMENT
    5.
    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

    申请人： Adobe Inc.

    发明人： Saayan Mitra ,  Aritra Ghosh ,  Somdeb Sarkhel ,  Jiatong Xie

    IPC分类号： G06Q30/08 ,  G06N20/00

    摘要： 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).

    OPEN-DOMAIN TRENDING HASHTAG RECOMMENDATIONS
    7.
    发明公开
    OPEN-DOMAIN TRENDING HASHTAG RECOMMENDATIONS 审中-公开

    公开(公告)号：US20240037149A1

    公开(公告)日：2024-02-01

    申请号：US17877469

    申请日：2022-07-29

    申请人： Adobe Inc.

    发明人： Somdeb Sarkhel ,  Xiang Chen ,  Viswanathan Swaminathan ,  Swapneel Mehta ,  Saayan Mitra ,  Ryan Rossi ,  Han Guo ,  Ali Aminian ,  Kshitiz Garg

    IPC分类号： G06F16/901 ,  G06N3/04 ,  G06Q50/00

    CPC分类号： G06F16/9024 ,  G06N3/0454 ,  G06Q50/01

    摘要： Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.

    TECHNIQUES FOR CUSTOMIZED TOPIC DETERMINATION FOR HIGH-VOLUME DOCUMENT COLLECTIONS
    8.
    发明公开
    TECHNIQUES FOR CUSTOMIZED TOPIC DETERMINATION FOR HIGH-VOLUME DOCUMENT COLLECTIONS 审中-公开

    公开(公告)号：US20230409621A1

    公开(公告)日：2023-12-21

    申请号：US17845437

    申请日：2022-06-21

    申请人： Adobe Inc.

    发明人： Xiang Chen ,  Viswanathan Swaminathan ,  Saayan Mitra ,  Camille Girabawe ,  Sreekanth Reddy

    IPC分类号： G06F16/35 ,  G06F40/279

    CPC分类号： G06F16/35 ,  G06F40/279

    摘要： A topic mapping system generates customized mapping schemas for multiple topic sets. The topic mapping system generates document clusters that represent groups of digital documents. The topic mapping system also generates, for each topic set, a document-topic mapping data object (“DTM data object”) that describes a customized mapping schema of the document clusters to labels in the topic set. The topic mapping system identifies customized groups of documents for responding to multiple requests that have a particular keyword. For each request, the topic mapping system identifies a particular topic set and DTM data object associated with a computing system that provided the request. Based on the keyword, the topic mapping system identifies documents that are categorized according to the customized mapping schema in the DTM data object. The topic mapping system can provide customized groups of documents to respective computing systems that provided the multiple requests.

    SYSTEMS AND METHODS FOR CONTENT DISTRIBUTION WITHOUT TRACKING
    9.
    发明公开
    SYSTEMS AND METHODS FOR CONTENT DISTRIBUTION WITHOUT TRACKING 审中-公开

    公开(公告)号：US20230281642A1

    公开(公告)日：2023-09-07

    申请号：US17653157

    申请日：2022-03-02

    申请人： ADOBE INC.

    发明人： Shiv Shankar ,  Sridhar Mahadevan ,  Moumita Sinha ,  Ritwik Sinha ,  Saayan Mitra ,  Viswanathan Swaminathan ,  Erin Davis

    IPC分类号： G06Q30/02

    CPC分类号： G06Q30/0201

    摘要： A system and method for content distribution without tracking is described. The system and method includes determining that device identifiers are not available for a first digital content channel; identifying a first cluster of users and a second cluster of users based on the determination that device identifiers are not available; providing first content and second content via the first digital content channel; monitoring user interactions on the first digital content channel to obtain a first conversion rate for users in the first cluster that receive the first content and a second conversion rate for users in the second cluster that receive the second content; computing a cross-cluster treatment effect based on the first conversion rate and the second conversion rate; computing a treatment effect for the first content based on the cross-cluster treatment effect; and providing the first content to a subsequent user based on the treatment effect.

    SPARSE EMBEDDING INDEX FOR SEARCH
    10.
    发明公开
    SPARSE EMBEDDING INDEX FOR SEARCH 审中-公开

    公开(公告)号：US20230153338A1

    公开(公告)日：2023-05-18

    申请号：US17527001

    申请日：2021-11-15

    申请人： ADOBE INC.

    发明人： Tung Mai ,  Saayan Mitra ,  Ryan A. Rossi ,  Gaurav Gupta ,  Anup Rao ,  Xiang Chen

    IPC分类号： G06F16/33 ,  G06F16/31

    CPC分类号： G06F16/3338 ,  G06F16/325 ,  G06F16/319 ,  G06F16/3347

    摘要： A search system facilitates efficient and fast near neighbor search given item vector representations of items, regardless of item type or corpus size. To index an item, the search system expands an item vector for the item to generate an expanded item vector and selects elements of the expanded item vector. The item is index by storing an identifier of the item in posting lists of an index corresponding to the position of each selected element in the expanded item vector. When a query is received, a query vector for the item is expanded to generate an expanded query vector, and elements of the expanded query vector are selected. Candidate items are identified based on posting lists corresponding to the position of each selected element in the expand query vector. The candidate items may be ranked, and a result set is returned as a response to the query.