公开(公告)号：US11640617B2

公开(公告)日：2023-05-02

申请号：US15465449

申请日：2017-03-21

Applicant: Adobe Inc.

Inventor： Chunyuan Li ,  Hung Hai Bui ,  Mohammad Ghavamzadeh ,  Georgios Theocharous

IPC: G06Q30/0202 ,  G06N3/08 ,  G06N3/044 ,  G06Q30/0204

Abstract: Metric forecasting techniques and systems in a digital medium environment are described that leverage similarity of elements, one to another, in order to generate a forecast value for a metric for a particular element. In one example, training data is received that describes a time series of values of the metric for a plurality of elements. The model is trained to generate the forecast value of the metric, the training using machine learning of a neural network based on the training data. The training includes generating dimensional-transformation data configured to transform the training data into a simplified representation to determine similarity of the plurality of elements, one to another, with respect to the metric over the time series. The training also includes generating model parameters of the neural network based on the simplified representation to generate the forecast value of the metric.

公开(公告)号：US20200320329A1

公开(公告)日：2020-10-08

申请号：US16904881

申请日：2020-06-18

Applicant: ADOBE INC.

Inventor： Trung Huu Bui ,  Hung Hai Bui ,  Shawn Alan Gaither ,  Walter Wei-Tuh Chang ,  Michael Frank Kraley ,  Pranjal Daga

IPC: G06K9/34 ,  G06K9/00 ,  G06K9/72 ,  G06Q10/10 ,  G06Q10/06 ,  G06F40/10

Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.

公开(公告)号：US10558852B2

公开(公告)日：2020-02-11

申请号：US15814979

申请日：2017-11-16

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Deepali Jain ,  Deepali Gupta ,  Eunyee Koh ,  Branislav Kveton ,  Nikhil Sheoran ,  Atanu Sinha ,  Hung Hai Bui ,  Charles Li Chen

IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06F16/954 ,  G06K9/62

Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

公开(公告)号：US10783361B2

公开(公告)日：2020-09-22

申请号：US16723619

申请日：2019-12-20

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Deepali Jain ,  Deepali Gupta ,  Eunyee Koh ,  Branislav Kveton ,  Nikhil Sheoran ,  Atanu Sinha ,  Hung Hai Bui ,  Charles Li Chen

IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06F16/954 ,  G06K9/62

Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

公开(公告)号：US10713519B2

公开(公告)日：2020-07-14

申请号：US15630779

申请日：2017-06-22

Applicant: ADOBE INC.

Inventor： Trung Huu Bui ,  Hung Hai Bui ,  Shawn Alan Gaither ,  Walter Wei-Tuh Chang ,  Michael Frank Kraley ,  Pranjal Daga

IPC: G06K9/34 ,  G06K9/00 ,  G06K9/72 ,  G06Q10/10 ,  G06Q10/06 ,  G06F40/10

Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.

公开(公告)号：US11769111B2

公开(公告)日：2023-09-26

申请号：US16904881

申请日：2020-06-18

Applicant: ADOBE INC.

Inventor： Trung Huu Bui ,  Hung Hai Bui ,  Shawn Alan Gaither ,  Walter Wei-Tuh Chang ,  Michael Frank Kraley ,  Pranjal Daga

IPC: G06F17/00 ,  G06Q10/10 ,  G06Q10/06 ,  G06F40/10 ,  G06V30/148 ,  G06V30/413 ,  G06F40/103

CPC classification number: G06Q10/10 ,  G06F40/10 ,  G06F40/103 ,  G06Q10/06 ,  G06V30/153 ,  G06V30/413

Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.