On24, Inc.

Brian Streit

Jayesh Sahasi

Jairo Diaz

Stephen Andrew Sims

Jaimini Joshi

Joshua Mark Hornik

Mario Andres Martinez Botero

Jairo Andres Diaz Rodriguez

Methods, systems, and apparatuses for identifying key moments of a media asset are described herein. Key moments may be identified, for example, based on analyzing data associated with the media asset and user engagement data associated with the media asset. The data associated with the media asset may be provided to a machine learning model configured to determine one or more first segments of the media asset that satisfy a first threshold. The user engagement data may be analyzed to determine one or more second segments of the media asset that satisfy a second threshold. A compilation of segments of the media asset, representing the key moments thereof, may be generated based on the one or more first segments and the one or more second segments.

Methods, systems, and apparatuses for generating personalized content

Methods, systems, and apparatuses for user engagement analysis are described herein. An analytics subsystem may use a plurality of activity data to generate a plurality of user profiles, corresponding user interest clouds for each user device of a plurality of user devices, and a first interest cloud associated with a particular client identifier. The analytics subsystem may generate a second interest cloud associated based on a subset of a plurality of media assets associated with a threshold quantity of engagements. The analytics subsystem may determine a plurality of clusters of the plurality of media assets and may generate a content interest cloud for each of the plurality of clusters.

Methods, systems, and apparatuses for user engagement analysis

Methods, systems, and apparatuses for content recommendations based on user activity data are described herein. An analytics subsystem may receive first activity data. The first activity may be indicative of a plurality of first engagements with a first plurality of media assets. At least one machine learning model may be configured to receive an input of activity data, such as the first activity data, and to determine at least one content recommendation on that basis. The at least one content recommendation may comprise a recommendation for at least one media asset. The at least one media asset may be associated with at least one media asset classification. The analytics subsystem may send the at least one content recommendation.

Methods, systems, and apparatuses for content recommendations based on user activity

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San Francisco, CA, United States of America

Brian Streit