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公开(公告)号：US11816696B2
公开(公告)日：2023-11-14
申请号：US17355907
申请日：2021-06-23
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 , G06Q30/0242 , G06Q30/0251 , G06N20/00 , G06N5/00 , G05B19/418
CPC classification number: G06Q30/0244 , G06N5/00 , G06N20/00 , G06Q30/0242 , G06Q30/0254 , G06Q30/0255 , G06Q30/0264
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.
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公开(公告)号：US20220230369A1
公开(公告)日：2022-07-21
申请号：US17657255
申请日：2022-03-30
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.
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公开(公告)号：US11109084B2
公开(公告)日：2021-08-31
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 , H04N21/2668 , 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.
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4.发明授权公开(公告)号：US10726325B2
公开(公告)日：2020-07-28
申请号：US15486862
申请日：2017-04-13
Applicant: Adobe Inc.
Inventor： Balaji Krishnamurthy , Piyush Gupta , Nupur Kumari , Akash Rupela
Abstract: Disclosed systems and methods generate user-session representation vectors from data generated by user interactions with online services. A transformation application executing on a computing device receives interaction data, which is generated by user devices interacting with an online service. The transformation application separates the interaction data into session datasets. The transformation involves normalizing the session datasets by modifying the rows within each session dataset by removing event identifiers and time stamps. The application transforms each normalized session dataset into a respective user-session representation vector. The application outputs the user-session representation vectors.
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公开(公告)号：US20240185588A1
公开(公告)日：2024-06-06
申请号：US18062314
申请日：2022-12-06
Applicant: ADOBE INC.
Inventor： Nupur Kumari , Richard Zhang , Junyan Zhu , Elya Shechtman
IPC: G06V10/778 , G06V10/75 , G06V10/774
CPC classification number: G06V10/778 , G06V10/751 , G06V10/774
Abstract: Systems and methods for fine-tuning diffusion models are described. Embodiments of the present disclosure obtain an input text indicating an element to be included in an image; generate a synthetic image depicting the element based on the input text using a diffusion model trained by comparing synthetic images depicting the element to training images depicting elements similar to the element and updating selected parameters corresponding to an attention layer of the diffusion model based on the comparison.
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Patent Agency Ranking
公开(公告)号：US11875512B2
公开(公告)日：2024-01-16
申请号：US18148256
申请日：2022-12-29
Applicant: Adobe Inc.
Inventor： Mayank Singh , Balaji Krishnamurthy , Nupur Kumari , Puneet Mangla
IPC: G06T7/00 , G06T7/11 , G06N3/08 , G06N3/04 , G06F18/214 , G06F18/21 , G06V10/774 , G06V10/82
CPC classification number: G06T7/11 , G06F18/214 , G06F18/217 , G06N3/04 , G06N3/08 , G06V10/774 , G06V10/82 , G06T2207/20081 , G06T2207/20084
Abstract: Embodiments are disclosed for training a neural network classifier to learn to more closely align an input image with its attribution map. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a training image comprising a representation of one or more objects, the training image associated with at least one label for the representation of the one or more objects, generating a perturbed training image based on the training image using a neural network, and training the neural network using the perturbed training image by minimizing a combination of classification loss and attribution loss to learn to align an image with its corresponding attribution map.
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公开(公告)号：US11829880B2
公开(公告)日：2023-11-28
申请号：US18049209
申请日：2022-10-24
Applicant: Adobe Inc.
Inventor： Mayank Singh , Nupur Kumari , Dhruv Khattar , Balaji Krishnamurthy , Abhishek Sinha
CPC classification number: G06N3/08 , G06N20/00 , H04L63/1441
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating trained neural network with increased robustness against adversarial attacks by utilizing a dynamic dropout routine and/or a cyclic learning rate routine. For example, the disclosed systems can determine a dynamic dropout probability distribution associated with neurons of a neural network. The disclosed systems can further drop neurons from a neural network based on the dynamic dropout probability distribution to help neurons learn distinguishable features. In addition, the disclosed systems can utilize a cyclic learning rate routine to force copy weights of a copy neural network away from weights of an original neural network without decreasing prediction accuracy to ensure that the decision boundaries learned are different.
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公开(公告)号：US11631205B2
公开(公告)日：2023-04-18
申请号：US17657255
申请日：2022-03-30
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.
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公开(公告)号：US20210327108A1
公开(公告)日：2021-10-21
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.
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公开(公告)号：US11631156B2
公开(公告)日：2023-04-18
申请号：US17088120
申请日：2020-11-03
Applicant: ADOBE INC.
Inventor： Mayank Singh , Parth Patel , Nupur Kumari , Balaji Krishnamurthy
Abstract: This disclosure includes technologies for image processing, particularly for image generation and editing in a configurable semantic direction. A generative adversarial network is trained with an auxiliary network with an auxiliary task that is designed to disentangle the latent space of the generative adversarial network. Resultantly, a new type of GAN is created to improve image generation or editing in both conditional and unconditional settings.
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