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.

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.