Getgo, Inc.

Citrix Systems, Inc.

Logmein, Inc.

Elizabeth Thapliyal

Ashish V Thapliyal

Ryan Warren Kasper

Anil Roychoudhry

Laura Ann Martin

Michael Wookey

Ajay Kumar

Yogesh Moorjani

Ye Zhou

AnneMarie Lock

Abhinav Kuruvadi Ramesh Babu

Anne Marie Lock

James Kalbach

Stefan Alexander Von Imhof

Margaret Dianne Cramer

Techniques for managing a user's tasks based on the user's focus level. High focus time slots are identified during which the user has a high level of focus, as well as low focus time slots during which the user has a low level of focus. Tasks for completion by the user are scheduled such that the high focus time slots are allocated to tasks that require a high level of user focus, and the low focus time slots are allocated to tasks that require a low level of user focus. High focus times may be identified based on user indications and/or on biometric data describing the user's physical state. Interruption requests may also be managed based on the user's focus level. Necessary resources may be pre-loaded prior to the high focus time periods, and breaks may be automatically scheduled to maintain the user's high level of focus.

Focus aware productivity tool

Item classification rules are created based on examples selected by a user, such as by selecting a subset of emails, and the rule is used across a larger set of items to obtain automatic classification of similar items according to the rule. Based on an analysis, a candidate classification rule is generated identifying text-based features shared among the items of the subset. The user can review the candidate rule as well as a resultant subset of items generated by the rule, and either accept the candidate rule or make an adjustment to the examples and then perform one or more iterations of the analysis to refine the rule. Adjustments can be made by removing items incorrectly included in a resultant subset and/or adding items incorrectly excluded from a resultant subset, and using the adjusted subset in a next iteration.

Example-based item classification

A technique for facilitating online collaboration among users of client machines allows multiple users to work together and concurrently on a set of documents. In response to a request from a user, a server opens a document in a software application running on the server and virtualizes the software application to the client machines. Multiple users can then operate the software application from the respective client machines to edit the document via remote control. The server multiplexes pointer input from the client machines to provide pointer input to the software application from one user at a time. Multiplexing of pointer input takes place seamlessly and automatically in response to user activity with respect to the virtualized software application.

Collaborative online document editing

A technique for recording dictation, meetings, lectures, and other events includes automatically segmenting an audio recording into portions by detecting speech transitions within the recording and selectively identifying certain portions of the recording as noteworthy. Noteworthy audio portions are displayed to a user for selective playback. The user can navigate to different noteworthy audio portions while ignoring other portions. Each noteworthy audio portion starts and ends with a speech transition. Thus, the improved technique typically captures noteworthy topics from beginning to end, thereby reducing or avoiding the need for users to have to search for the beginnings and ends of relevant topics manually.

Capturing noteworthy portions of audio recordings

Growing community of inventors

Santa Barbara, CA, United States of America

Elizabeth Thapliyal