-
公开(公告)号：US11301506B2
公开(公告)日：2022-04-12
申请号：US15637829
申请日：2017-06-29
Applicant: Adobe Inc.
Inventor： Mayur Hemani , Balaji Krishnamurthy
IPC: G06F16/00 , G06F16/48 , G06F3/0482 , G06F16/36 , G06F16/332 , G06F16/33 , G06F16/55 , G06F16/56
Abstract: Automated digital asset tagging techniques and systems are described that support use of multiple vocabulary sets. In one example, a plurality of digital assets are obtained having first-vocabulary tags taken from a first-vocabulary set. Second-vocabulary tags taken from a second-vocabulary set are assigned to the plurality of digital assets through machine learning. A determination is made that at least one first-vocabulary tag includes a plurality of visual classes based on the assignment of at least one second-vocabulary tag. Digital assets are collected from the plurality of digital assets that correspond to one visual class of the plurality of visual classes. The model is generated using machine learning based on the collected digital assets.
-
公开(公告)号：US11295491B2
公开(公告)日：2022-04-05
申请号：US16850677
申请日：2020-04-16
Applicant: Adobe Inc.
Inventor： Nupur Kumari , Piyush Gupta , Akash Rupela , Siddarth R , Balaji Krishnamurthy
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.
-
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
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.
-
115.发明授权公开(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.
-
Patent Agency Ranking
116.发明授权公开(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
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.
-
117.发明授权公开(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
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
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.
-
-
-
-
-
-
-
-
-
Search Results
125
records
(took 0.048 seconds)
