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21.
公开(公告)号:US20190213476A1
公开(公告)日:2019-07-11
申请号:US15867169
申请日:2018-01-10
Applicant: Adobe Inc.
Inventor: Harvineet Singh , Sahil Garg , Neha Banerjee , Moumita Sinha , Atanu Sinha
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for determining and applying digital content transmission times using machine-learning. For example, in one or more embodiments, the disclosed system trains a recurrent neural network based on past electronic messages for a user that have been partitioned into a plurality of time bins. Additionally, in one or more embodiments, the system utilizes the trained recurrent neural network to generate predictions of engagement metrics (e.g., a hazard metric based on survival analysis or interaction probability metric) for sending a new electronic message within the plurality of time bins. The system then executes the digital content campaign by selecting a time bin based on the predicted engagement metrics and then sending the new electronic message at a send time corresponding to the selected time bin.
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公开(公告)号:US10311913B1
公开(公告)日:2019-06-04
申请号:US15902046
申请日:2018-02-22
Applicant: Adobe Inc.
Inventor: Sumit Shekhar , Harvineet Singh , Dhruv Singal , Atanu R. Sinha
IPC: G11B27/031 , G06K9/00 , G06K9/62
Abstract: Certain embodiments involve generating summarized versions of video content based on memorability of the video content. For example, a video summarization system accesses segments of an input video. The video summarization system identifies memorability scores for the respective segments. The video summarization system selects a subset of segments from the segments based on each computed memorability score in the subset having a threshold memorability score. The video summarization system generates visual summary content from the subset of the segments.
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