Citrix Systems, Inc.

Other

Feng Tao

Jian Ding

Daowen Wei

Hengbo Wang

Manbinder Pal Singh

A method for video frame analysis includes determining a first dissimilarity metric and a second dissimilarity metric. The first dissimilarity metric may correspond to a first difference between a first foreground of a first key frame in a video and a second foreground of a second key frame following the first key frame in the video. The second dissimilarity metric may correspond to a second difference between the second foreground of the second key frame and a third foreground of a third key frame following the second key frame in the video. A playback of the video may be generated based on the first dissimilarity metric and the second dissimilarity metric. Related systems and computer program products are also provided.

Video frame analysis for targeted video browsing

Described embodiments provide systems and methods for defining a clip within a recording. A device may generate a recording of a session of a user via which a plurality of applications is accessible. The device may detect a transition of an application of the plurality of applications in a foreground of the session. The device may identify, from the recording, a frame corresponding to the transition in the foreground. The device may store an identification of the frame corresponding to the transition to define a clip of the user within the recording using the application in the session.

Extracting clips of application use from recordings of sessions

Methods and systems for using machine learning to provide application recommendations are described herein. A computing device may capture a first edge frame of a first application displayed at the computing device. The computing device may apply machine learning to the first edge frame of the first application to identify a context tag. The computing device may identify applications subsequently accessed in a sequential manner after accessing the first application in a context corresponding to the identified context tag, where each of the applications corresponds to a context level score. The computing device may identify a second application, which may have a higher context level score than remaining applications. Along with the first application, the computing device may display a selectable interface element corresponding to the second application, and selection of the selectable interface element may cause display of an application list that includes the second application.

Contextual application switch based on user behaviors

Described embodiments provide systems and methods for balancing user sessions using load pattern forecasting are provided here. A device can determine a pattern of load distribution for a session prior to establishment of the session on a server of a computing environment. The pattern can be indicative of load on a server to run the session for a range of time. The device can determine an amount of usage of the plurality of servers on a per server basis. The usage can be indicative of sessions to be run on individual servers for the range of time. The device can select one of the plurality of servers on which to run the session based on a comparison of the pattern of load distribution for the session and the determined amounts of usage of the plurality servers. The device can assign the session to the selected server of the computing environment to balance resource usage across the computing environment.

Load balancing of computing sessions with load patterns

A system and method that automatically terminates an application. A method includes monitoring activity data points for an application launched by a client device within a workspace environment. The activity data points may include user interactions with a physical interface component. State data for each file associated with the application is monitored and, if a determination is made that the application is inactive based on the activity data points, the method determines if a file associated with the application includes unsaved content based on state data. If it is determined that no files for the application include unsaved content, the method forecasts whether the application will be inactive for a future period based on the activity data. The application is terminated if it is determined that no files for the application include unsaved content and the application is forecast to be inactive.

Auto termination of applications based on application and user activity

One disclosed method involves determining first data indicative of a context of one or more client devices in response to access of a first file by the one or more client devices, and determining, subsequent to the access of the first file by the one or more client devices, second data indicative of a current context of a first client device. Based at least in part on the first data and the second data being of a first context type, at least one command is generated that causes the first client device to present an indication that the first file is available for access.

Intelligent file access system

A method is for operating a GUI on a display, the GUI having existing windows. The method may include receiving a window creation command and a current screen of the GUI with the existing windows, and the window creation command may have an original window size value and an original window position value. The method may include dividing the current screen of the GUI into different content regions, each different content region having a region size value, and a region position value. The method may include classifying each of the different content regions as a content type from a different content types, generating a new window position value, and generating a new window creation command including the original window size value and the new window position value, and forwarding the new window creation command to the GUI.

Computing device with new window creation command and related systems and methods

A system and method that automatically terminates an application. A method includes monitoring activity data points for an application launched by a client device within a workspace environment. The activity data points may include user interactions with a physical interface component and background interactions occurring with the application. State data for each file associated with the application is monitored and, if a determination is made that the application is inactive based on the activity data points, the method determines if a file associated with the application includes unsaved content based on state data. If it is determined that no files for the application include unsaved content, the method forecasts whether the application will be inactive for a future period based on the activity data. The application is terminated if it is determined that no files for the application include unsaved content and the application is forecast to be inactive.

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Jiangsu, China

Feng Tao