Adobe, Inc.

Brandon Pack

Balaji Krishnamurthy

William Brandon George

Nikaash Puri

Mihir Naware

Lauren Dest

Joshua Butikofer

Kumar Mrityunjay Singh

Kunal Kumar Jain

Hyman Chung

Yaman Kumar

Matthew William Rozen

Pranjal Prasoon

Somesh Singh

James Patrick O'Reilly

Abhinav Chauhan

Mihai Corlan

Timothy Chia-chi Liu

Alexander Klimetschek

Suman Basetty

Clinton Hansen Goudie-Nice

Thomas Greger

Anil Sachdev

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

Techniques for providing content to a computing device of a user are described. For example, a platform hosts a plurality of tools includes an analytics tool and a content tool. Input of a customer of the platform is received and specifies user segments and content to be provided accordingly. The tools are updated based on the customer input. User interactions of the user are also received and analyzed by, for example, the analytics tool. The analytics tool outputs an indication that the user is associated with a user segment based on the analysis. The output identifies the user segment and the user based on a format usable by the other tools. In turn, the content tool automatically receives and uses the output as an input to determine the content and the target of the content. Accordingly, the content tool provides the content to the computing device of the user.

Using user segments for targeted content

A load manager comprises a configuration manager and a load monitor. The load manager is configured to monitor and manage aspects of a distributed computer system comprising a plurality of servers. Each server is configured to perform tasks according to a respective time-based scheduler configuration. In some embodiments, the load monitor monitors one or more load metrics of each of the one or more servers. In response to one or more load metrics exceeding a threshold, the configuration manager determines the current time-based task scheduler configuration of the server exceeding the threshold. The load manager is further configured to modify the time-based task scheduler configuration to adjust a further task load on the server based on the at least one or more load metrics.

Systems and methods for load balancing of time-based tasks in a distributed computing system

A load manager comprises a configuration manager and a load monitor. The load manager is configured to monitor and manage aspects of a distributed computer system comprising a plurality of servers. Each server is configured to perform tasks according to a respective time-based scheduler configuration. In some embodiments, the load monitor monitors one or more load metrics of each of the one or more servers. In response to one or more load metrics exceeding a threshold, the configuration manager determines the current time-based task scheduler configuration of the server exceeding the threshold. The load manager is further configured to modify the time-based task scheduler configuration to adjust a further task load on the server based on the at least on or more load metrics.

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Sandy, UT, United States of America

Brandon Pack