Adobe, Inc.

Matthew William Rozen

Balaji Krishnamurthy

Nikaash Puri

Mihir Naware

Hyman Chung

Somesh Singh

Pranjal Prasoon

William Brandon George

Peter Raymond Fransen

Lauren Dest

Joshua Butikofer

Kumar Mrityunjay Singh

Yaman Kumar

Kunal Kumar Jain

Brian David Williams

Brandon Pack

Cory Lynn Edwards

Alexander Klimetschek

Timothy Chia-chi Liu

Mihai Corlan

Abhinav Chauhan

James Patrick O'Reilly

Clinton Hansen Goudie-Nice

Suman Basetty

Nikitha Srikanth

Nisarg Shailesh Joshi

Seoyoung Park

Nithyakala Sainath

In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

Digital content analysis

Navigation and reward techniques involving physical goods and services are described. In one example, digital content is configured to aid navigation of a user between different physical goods or services. This navigation includes user specified good or services as well as recommended goods or services that are not specified by the user. In another example, digital content is provided as part of a reward system. In return for permitting access to user data, the user is provided with rewards that are based on this monitored interaction. In this way, an owner of the store may gain detailed knowledge which may be used to increase likelihood of offering goods or services of interest to the user. In return, the user is provided with rewards to permit access to this detailed knowledge.

Navigation and rewards involving physical goods and services

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Content analytics as part of content creation

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San Francisco, CA, United States of America

Matthew William Rozen