-
公开(公告)号：US20230281680A1
公开(公告)日：2023-09-07
申请号：US17652939
申请日：2022-03-01
Applicant: ADOBE INC.
Inventor： Michail Mamakos , Sridhar Mahadevan , Viswanathan Swaminathan , Mariette Philippe Souppe , Ritwik Sinha , Saayan Mitra , Zhao Song
CPC classification number: G06Q30/0283 , G06Q10/06313 , G06F9/5033
Abstract: Systems and methods for resource allocation are described. The systems and methods include receiving utilization data for computing resources shared by a plurality of users, updating a pricing agent using a reinforcement learning model based on the utilization data, identifying resource pricing information using the pricing agent, and allocating the computing resources to the plurality of users based on the resource pricing information.
-
42.发明申请公开(公告)号：US20210374809A1
公开(公告)日：2021-12-02
申请号：US17403702
申请日：2021-08-16
Applicant: Adobe Inc.
Inventor： Somdeb Sarkhel , Saayan Mitra , Jiatong Xie , Alok Kothari
Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.
-
43.发明授权公开(公告)号：US11127050B2
公开(公告)日：2021-09-21
申请号：US16687082
申请日：2019-11-18
Applicant: Adobe Inc.
Inventor： Somdeb Sarkhel , Saayan Mitra , Jiatong Xie , Alok Kothari
Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.
-
Patent Agency Ranking
公开(公告)号：US11120363B2
公开(公告)日：2021-09-14
申请号：US15788455
申请日：2017-10-19
Applicant: ADOBE INC.
Inventor： Viswanathan Swaminathan , Saayan Mitra
Abstract: Embodiments of the present disclosure provide systems, methods, and computer storage media for mitigating latencies associated with the encoding of digital assets. Instead of waiting for codebook generation to complete in order to encode a digital asset for storage, embodiments described herein describe a shifting codebook generation and employment technique that significantly mitigates any latencies typically associated with encoding schemes. As a digital asset is received, a single codebook is trained based on each portion of the digital asset, or in some instances along with each portion of other digital assets being received. The single codebook is employed to encode subsequent portion(s) of the digital asset as it is received. The process continues until an end of the digital asset is reached or another command to terminate the encoding process is received. To encode an initial portion of the digital asset, a bootstrap codebook can be employed.
-
45.发明申请公开(公告)号：US20210150585A1
公开(公告)日：2021-05-20
申请号：US16687082
申请日：2019-11-18
Applicant: Adobe Inc.
Inventor： Somdeb Sarkhel , Saayan Mitra , Jiatong Xie , Alok Kothari
Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.
-
公开(公告)号：US10887640B2
公开(公告)日：2021-01-05
申请号：US16032240
申请日：2018-07-11
Applicant: Adobe Inc.
Inventor： Viswanathan Swaminathan , Somdeb Sarkhel , Saayan Mitra
IPC: H04N21/466 , H04N21/262 , H04N21/8549 , G06N5/04
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing an artificial intelligence framework for generating enhanced digital content and improving digital content campaign design. In particular, the disclosed systems can utilize a metadata neural network, a summarizer neural network, and/or a performance neural network to generate metadata for digital content, predict future performance metrics, generate enhanced digital content, and provide recommended content changes to improve performance upon dissemination to one or more client devices.
-
公开(公告)号：US10860858B2
公开(公告)日：2020-12-08
申请号：US16009559
申请日：2018-06-15
Applicant: Adobe Inc.
Inventor： Viswanathan Swaminathan , Saayan Mitra , Somdeb Sarkhel , Qi Lou
Abstract: The present disclosure relates to systems, methods, and computer readable media that utilize a trained multi-modal combination model for content and text-based evaluation and distribution of digital video content to client devices. For example, systems described herein include training and/or utilizing a combination of trained visual and text-based prediction models to determine predicted performance metrics for a digital video. The systems described herein can further utilize a multi-modal combination model to determine a combined performance metric that considers both visual and textual performance metrics of the digital video. The systems described herein can further select one or more digital videos for distribution to one or more client devices based on combined performance metrics associated with the digital videos.
-
公开(公告)号：US20190384981A1
公开(公告)日：2019-12-19
申请号：US16009559
申请日：2018-06-15
Applicant: Adobe Inc.
Inventor： Viswanathan Swaminathan , Saayan Mitra , Somdeb Sarkhel , Qi Lou
Abstract: The present disclosure relates to systems, methods, and computer readable media that utilize a trained multi-modal combination model for content and text-based evaluation and distribution of digital video content to client devices. For example, systems described herein include training and/or utilizing a combination of trained visual and text-based prediction models to determine predicted performance metrics for a digital video. The systems described herein can further utilize a multi-modal combination model to determine a combined performance metric that considers both visual and textual performance metrics of the digital video. The systems described herein can further select one or more digital videos for distribution to one or more client devices based on combined performance metrics associated with the digital videos.
-
-
-
-
-
-
-
Search Results
48
records
(took 0.029 seconds)
