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公开(公告)号：US11032578B2
公开(公告)日：2021-06-08
申请号：US16020018
申请日：2018-06-27
申请人： Adobe Inc.
IPC分类号： H04N7/12 , H04N19/94 , H04N19/46 , H04N19/91 , H04N21/274
摘要： Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.
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22.发明申请公开(公告)号：US20200021873A1
公开(公告)日：2020-01-16
申请号：US16032240
申请日：2018-07-11
申请人： Adobe Inc.
IPC分类号： H04N21/262 , G06N5/04 , H04N21/8549 , H04N21/466
摘要： 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.
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公开(公告)号：US20240296519A1
公开(公告)日：2024-09-05
申请号：US18178018
申请日：2023-03-03
申请人： ADOBE INC.
发明人： Pooja Guhan , Saayan Mitra , Somdeb Sarkhel , Ritwik Sinha , Stefano Petrangeli , Viswanathan Swaminathan
摘要： Systems and methods for media generation are provided. According to one aspect, a method for media generation includes obtaining a media object and context data describing a context of the media object, wherein the media object comprises one or more modification parameters; generating a modified media object by adjusting the one or more modification parameters using a reinforcement learning model based on the context data; and providing the modified media object within the context.
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公开(公告)号：US12050647B2
公开(公告)日：2024-07-30
申请号：US17877469
申请日：2022-07-29
申请人： Adobe Inc.
发明人： Somdeb Sarkhel , Xiang Chen , Viswanathan Swaminathan , Swapneel Mehta , Saayan Mitra , Ryan Rossi , Han Guo , Ali Aminian , Kshitiz Garg
IPC分类号： G06F16/90 , G06F16/901 , G06N3/045 , G06Q50/00
CPC分类号： G06F16/9024 , G06N3/045 , G06Q50/01
摘要： Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.
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公开(公告)号：US11893007B2
公开(公告)日：2024-02-06
申请号：US17369527
申请日：2021-07-07
申请人： ADOBE INC.
IPC分类号： G06F16/00 , G06F16/23 , G06F16/583 , H04N21/6336 , H03M7/30 , H04N21/21
CPC分类号： G06F16/2365 , G06F16/5862 , H03M7/30 , H03M7/6088 , H04N21/21 , H04N21/6336
摘要： Embodiments of the present disclosure provide systems, methods, and computer storage media for optimizing computing resources generally associated with cloud-based media services. Instead of decoding digital assets on-premises to stream to a remote client device, an encoded asset can be streamed to the remote client device. A codebook employable for decoding the encoded asset can be embedded into the stream transmitted to the remote client device, so that the remote client device can extract the embedded codebook, and employ the extracted codebook to decode the encoded asset locally. In this way, not only are processing resources associated with on-premises decoding eliminated, but on-premises storage of codebooks can be significantly reduced, while expensive bandwidth is freed up by virtue of transmitting a smaller quantity of data from the cloud to the remote client device.
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公开(公告)号：US20230281680A1
公开(公告)日：2023-09-07
申请号：US17652939
申请日：2022-03-01
申请人： ADOBE INC.
发明人： Michail Mamakos , Sridhar Mahadevan , Viswanathan Swaminathan , Mariette Philippe Souppe , Ritwik Sinha , Saayan Mitra , Zhao Song
CPC分类号： G06Q30/0283 , G06Q10/06313 , G06F9/5033
摘要： 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.
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27.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
申请人： Adobe Inc.
发明人： Somdeb Sarkhel , Saayan Mitra , Jiatong Xie , Alok Kothari
摘要： 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.
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28.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
申请人： Adobe Inc.
发明人： Somdeb Sarkhel , Saayan Mitra , Jiatong Xie , Alok Kothari
摘要： 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.
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公开(公告)号：US11120363B2
公开(公告)日：2021-09-14
申请号：US15788455
申请日：2017-10-19
申请人： ADOBE INC.
摘要： 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.
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30.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
申请人： Adobe Inc.
发明人： Somdeb Sarkhel , Saayan Mitra , Jiatong Xie , Alok Kothari
摘要： 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.
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