公开(公告)号：US20210397986A1

公开(公告)日：2021-12-23

申请号：US16904263

申请日：2020-06-17

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06N5/04 ,  G06N20/00

Abstract: Techniques described herein extract form structures from a static form to facilitate making that static form reflowable. A method described herein includes accessing low-level form elements extracted from a static form. The method includes determining, using a first set of prediction models, second-level form elements based on the low-level form elements. Each second-level form element includes a respective one or more low-level form elements. The method further includes determining, using a second set of prediction models, high-level form elements based on the second-level form elements and the low-level form elements. Each high-level form element includes a respective one or more second-level form elements or low-level form elements. The method further includes generating a reflowable form based on the static form by, for each high-level form element, linking together the respective one or more second-level form elements or low-level form elements.

公开(公告)号：US11107115B2

公开(公告)日：2021-08-31

申请号：US16057743

申请日：2018-08-07

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

IPC: G06Q30/00 ,  G06Q30/02 ,  G06N20/00 ,  G05B19/418

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

公开(公告)号：US11073965B2

公开(公告)日：2021-07-27

申请号：US16193475

申请日：2018-11-16

Applicant: Adobe Inc.

Inventor： Harpreet Singh ,  Balaji Krishnamurthy ,  Akash Rupela

IPC: G06F3/0482 ,  G06F40/197 ,  H04L29/08

Abstract: In some embodiments, a configuration management application accesses configuration data for a multi-target website. The configuration management application provides the user interface including a timeline area and a page display area. The timeline area is configured to display timeline entries corresponding to configurations of the multi-target website. Based on a selection of a timeline entry, the page display area is configured to display a webpage configuration corresponding to the selected timeline entry. In addition, the page display area is configured to display graphical annotations indicating interaction metrics for the configured page regions. In some cases, the timeline entries, configurations, and interaction metrics are determined based on a selection of a target segment for the multi-target website.

公开(公告)号：US11030782B2

公开(公告)日：2021-06-08

申请号：US16679165

申请日：2019-11-09

Applicant: Adobe Inc.

Inventor： Kumar Ayush ,  Surgan Jandial ,  Abhijeet Kumar ,  Mayur Hemani ,  Balaji Krishnamurthy ,  Ayush Chopra

IPC: G06T11/40 ,  G06T3/00 ,  G06N3/08 ,  G06N3/04

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a virtual try-on digital image utilizing a unified neural network framework. For example, the disclosed systems can utilize a coarse-to-fine warping process to generate a warped version of a product digital image to fit a model digital image. In addition, the disclosed systems can utilize a texture transfer process to generate a corrected segmentation mask indicating portions of a model digital image to replace with a warped product digital image. The disclosed systems can further generate a virtual try-on digital image based on a warped product digital image, a model digital image, and a corrected segmentation mask. In some embodiments, the disclosed systems can train one or more neural networks to generate accurate outputs for various stages of generating a virtual try-on digital image.

公开(公告)号：US11003862B2

公开(公告)日：2021-05-11

申请号：US16359402

申请日：2019-03-20

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Balaji Krishnamurthy

IPC: G06F40/30 ,  G06F16/93 ,  G10L15/02 ,  G06N3/04 ,  G06N3/08

Abstract: Classifying structural features of a digital document by feature type using machine learning is leveraged in a digital medium environment. A document analysis system is leveraged to extract structural features from digital documents, and to classifying the structural features by respective feature types. To do this, the document analysis system employs a character analysis model and a classification model. The character analysis model takes text content from a digital document and generates text vectors that represent the text content. A vector sequence is generated based on the text vectors and position information for structural features of the digital document, and the classification model processes the vector sequence to classify the structural features into different feature types. The document analysis system can generate a modifiable version of the digital document that enables its structural features to be modified based on their respective feature types.

公开(公告)号：US20210124993A1

公开(公告)日：2021-04-29

申请号：US16661617

申请日：2019-10-23

Applicant: Adobe Inc.

Inventor： Mayank Singh ,  Puneet Mangla ,  Nupur Kumari ,  Balaji Krishnamurthy ,  Abhishek Sinha

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a classification neural network to classify digital images in few-shot tasks based on self-supervision and manifold mixup. For example, the disclosed systems can train a feature extractor as part of a base neural network utilizing self-supervision and manifold mixup. Indeed, the disclosed systems can apply manifold mixup regularization over a feature manifold learned via self-supervised training such as rotation training or exemplar training. Based on training the feature extractor, the disclosed systems can also train a classifier to classify digital images into novel classes not present within the base classes used to train the feature extractor.

公开(公告)号：US10609434B2

公开(公告)日：2020-03-31

申请号：US16057729

申请日：2018-08-07

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  H04N21/2668 ,  H04N21/258 ,  H04N21/475 ,  G06N20/00 ,  H04N21/81 ,  G06Q30/02

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

公开(公告)号：US20200092593A1

公开(公告)日：2020-03-19

申请号：US16694612

申请日：2019-11-25

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  H04N21/258 ,  G06Q30/02 ,  H04N21/475 ,  H04N21/81 ,  G06N20/00 ,  H04N21/2668

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

公开(公告)号：US20190286978A1

公开(公告)日：2019-09-19

申请号：US15921369

申请日：2018-03-14

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Balaji Krishnamurthy ,  Shagun Sodhani

IPC: G06N3/08 ,  G06F17/30 ,  G06N7/00

Abstract: Systems and techniques map an input field from a data schema to a hierarchical standard data model (XDM). The XDM includes a tree of single XDM fields and each of the single XDM fields includes a composition of single level XDM fields. An input field from a data schema is processed by an unsupervised learning algorithm to obtain a sequence of vectors representing the input field and a sequence of vectors representing single level hierarchical standard data model (XDM) fields. These vectors are processed by a neural network to obtain a similarity score between the input field and each of the single level XDM fields. A probability of a match is determined using the similarity score between the input field and each of the single level XDM fields. The input field is mapped to the XDM field having the probability of the match with a highest score.

公开(公告)号：US20190188463A1

公开(公告)日：2019-06-20

申请号：US15843953

申请日：2017-12-15

Applicant: Adobe Inc.

Inventor： Shagun Sodhani ,  Kartikay Garg ,  Balaji Krishnamurthy

IPC: G06K9/00 ,  G06K9/62

CPC classification number: G06K9/00456 ,  G06K9/00463 ,  G06K9/6262 ,  G06K9/627 ,  G06K2209/01 ,  G06N3/0445 ,  G06N3/0481 ,  G06N3/084

Abstract: Techniques for determining reading order in a document. A current labeled text run (R1), RIGHT text run (R1) and DOWN text run (R3) are generated. The R1 labeled text run is processed by a first LSTM, the R2 labeled text run is processed by a second LSTM, and the R3 labeled text run is processed by a third LSTM, wherein each of the LSTMs generates a respective internal representation (R1′, R2′ and R3′). Deep learning tools other than LSTMs can be used, as will be appreciated. The respective internal representations R1′, R2′ and R3′ are concatenated or otherwise combined into a vector or tensor representation and provided to a classifier network that generates a predicted label for a next text run as RIGHT, DOWN or EOS in the reading order of the document.