公开(公告)号：US20220027722A1

公开(公告)日：2022-01-27

申请号：US16939661

申请日：2020-07-27

Applicant: Adobe Inc.

Inventor： Gang Wu ,  Viswanathan Swaminathan ,  Ryan Rossi ,  Hongchang Gao

IPC: G06N3/08 ,  G06N3/04

Abstract: A deep relational factorization machine (“DRFM”) system is configured to provide a high-order prediction based on high-order feature interaction data for a dataset of sample nodes. The DRFM system can be configured with improved factorization machine (“FM”) techniques for determining high-order feature interaction data describing interactions among three or more features. The DRFM system can be configured with improved graph convolutional neural network (“GCN”) techniques for determining sample interaction data describing sample interactions among sample nodes, including sample interaction data that is based on the high-order feature interaction data. The DRFM system generates a high-order prediction based on the high-order feature interaction embedding vector and the sample interaction embedding vector. The high-order prediction can be provided to a prediction computing system configured to perform operations based on the high-order prediction.

公开(公告)号：US12238451B2

公开(公告)日：2025-02-25

申请号：US18055301

申请日：2022-11-14

Applicant: Adobe Inc.

Inventor： Uttaran Bhattacharya ,  Gang Wu ,  Viswanathan Swaminathan ,  Stefano Petrangeli

IPC: H04N7/00 ,  G06T11/60

Abstract: Embodiments are disclosed for predicting, using neural networks, editing operations for application to a video sequence based on processing conversational messages by a video editing system. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including a video sequence and text sentences, the text sentences describing a modification to the video sequence, mapping, by a first neural network content of the text sentences describing the modification to the video sequence to a candidate editing operation, processing, by a second neural network, the video sequence to predict parameter values for the candidate editing operation, and generating a modified video sequence by applying the candidate editing operation with the predicted parameter values to the video sequence.

公开(公告)号：US11487579B2

公开(公告)日：2022-11-01

申请号：US16867104

申请日：2020-05-05

Applicant: ADOBE INC.

Inventor： Kanak Vivek Mahadik ,  Ryan A. Rossi ,  Sana Malik Lee ,  Georgios Theocharous ,  Handong Zhao ,  Gang Wu ,  Youngsuk Park

IPC: G06F9/50 ,  G06F11/30 ,  G06F17/10 ,  G06F11/34

Abstract: A system and method for automatically adjusting computing resources provisioned for a computer service or application by applying historical resource usage data to a predictive model to generate predictive resource usage. The predictive resource usage is then simulated for various service configurations, determining scaling requirements and resource wastage for each configuration. A cost value is generated based on the scaling requirement and resource wastage, with the cost value for each service configuration used to automatically select a configuration to apply to the service. Alternatively, the method for automatically adjusting computer resources provisioned for a service may include receiving resource usage data of the service, applying it to a linear quadratic regulator (LQR) to find an optimal stationary policy (treating the resource usage data as states and resource-provisioning variables as actions), and providing instructions for configuring the service based on the optimal stationary policy.

公开(公告)号：US11348130B2

公开(公告)日：2022-05-31

申请号：US16995530

申请日：2020-08-17

Applicant: Adobe Inc.

Inventor： Chih Hsin Hsueh ,  Viswanathan Swaminathan ,  Venkata Karthik Penikalapati ,  Seth Olson ,  Michael Schiff ,  Gang Wu ,  Daniel Pang ,  Alok Kothari

IPC: G06Q30/02 ,  G06N20/00

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods to generate sketches for clearing-bid values and bid-success rates based on multi-dimensional targeting criteria for a digital-content campaign and dynamically determine predicted values for the digital-content campaign based on the sketches. To illustrate, the disclosed systems can use a running-average-tuple-sketch to generate tuple sketches of historical clearing-bid values and tuple sketches of historical bid-success-rates from historical auction data. Based on the tuple sketches, the disclosed systems can determine one or more of a predicted cost per quantity of impressions, a predicted number of impressions, or a predicted expenditure for the digital-content campaign—according to user-input targeting criteria and expenditure constraints.

公开(公告)号：US12288237B2

公开(公告)日：2025-04-29

申请号：US17743360

申请日：2022-05-12

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Aravind Reddy Talla ,  Zhao Song ,  Anup Rao ,  Tung Mai ,  Nedim Lipka ,  Gang Wu ,  Eunyee Koh

IPC: G06Q30/0601

Abstract: Embodiments provide systems, methods, and computer storage media for a Nonsymmetric Determinantal Point Process (NDPPs) for compatible set recommendations in a setting where data representing entities (e.g., items) arrives in a stream. A stream representing compatible sets of entities is received and used to update a latent representation of the entities and a compatibility distribution indicating likelihood of compatibility of subsets of the entities. The probability distribution is accessed in a single sequential pass to predict a compatible complete set of entities that completes an incomplete set of entities. The predicted complete compatible set is provided a recommendation for entities that complete the incomplete set of entities.

公开(公告)号：US20220343155A1

公开(公告)日：2022-10-27

申请号：US17337998

申请日：2021-06-03

Applicant: Adobe Inc.

Inventor： Saayan Mitra ,  Gang Wu ,  Georgios Theocharous ,  Richard Whitehead ,  Viswanathan Swaminathan ,  Zahraa Parekh ,  Ben Tepfer

IPC: G06N3/08 ,  G06K9/62 ,  G06F9/48

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that intelligently generate and modify schedules of task sequences utilizing a graph neural network and/or reinforcement learning model. For example, the disclosed system utilizes a graph neural network to generate performance efficiency scores indicating predicted performances of the sets of tasks. Additionally, the disclosed systems utilizes the performance efficiency scores to rank sets of tasks and then determine a schedule including an ordered sequence of tasks. Furthermore, disclosed system generates modified schedules in response to detecting a modification to the schedule. For example, the disclosed system utilizes a reinforcement learning model to provide recommendations of new tasks or task sequences deviating from the schedule in the event of an interruption. The disclosed system also utilizes the reinforcement learning model to learn from user choices to inform future scheduling of tasks.

公开(公告)号：US20210357255A1

公开(公告)日：2021-11-18

申请号：US16867104

申请日：2020-05-05

Applicant: ADOBE INC.

Inventor： Kanak Vivek Mahadik ,  Ryan A. Rossi ,  Sana Malik Lee ,  Georgios Theocharous ,  Handong Zhao ,  Gang Wu ,  Youngsuk Park

IPC: G06F9/50 ,  G06F11/34 ,  G06F11/30 ,  G06F17/10

Abstract: A system and method for automatically adjusting computing resources provisioned for a computer service or application by applying historical resource usage data to a predictive model to generate predictive resource usage. The predictive resource usage is then simulated for various service configurations, determining scaling requirements and resource wastage for each configuration. A cost value is generated based on the scaling requirement and resource wastage, with the cost value for each service configuration used to automatically select a configuration to apply to the service. Alternatively, the method for automatically adjusting computer resources provisioned for a service may include receiving resource usage data of the service, applying it to a linear quadratic regulator (LQR) to find an optimal stationary policy (treating the resource usage data as states and resource-provisioning variables as actions), and providing instructions for configuring the service based on the optimal stationary policy.

公开(公告)号：US20210272341A1

公开(公告)日：2021-09-02

申请号：US16804822

申请日：2020-02-28

Applicant: Adobe Inc.

Inventor： Viswanathan Swaminathan ,  Gang Wu ,  Akshay Malhotra

IPC: G06T11/60 ,  G06K9/62 ,  G06T7/11 ,  G06T7/70 ,  G06K9/00 ,  G06N3/08

Abstract: Generating images and videos depicting a human subject wearing textually defined attire is described. An image generation system receives a two-dimensional reference image depicting a person and a textual description describing target clothing in which the person is to be depicted as wearing. To maintain a personal identity of the person, the image generation system implements a generative model, trained using both discriminator loss and perceptual quality loss, which is configured to generate images from text. In some implementations, the image generation system is configured to train the generative model to output visually realistic images depicting the human subject in the target clothing. The image generation system is further configured to apply the trained generative model to process individual frames of a reference video depicting a person and output frames depicting the person wearing textually described target clothing.