Abstract: A realtime contextual event notification system that ingests events as streams from any authorized entity applies rules to the event streams, determines a context of an end-user who is a recipient of a targeted notification, and provides notifications to the end-user in accordance with the context. The event streams may come from multiple sources and rules may be applied to provide realtime contextual information associated with the end-user.

Abstract: The present invention is a method and system for automatedly producing at least one desktop analytics trigger. Upon receiving at least one type of data input, the system analyzes the data input and produces at least one desktop analytics trigger based on the results of the analysis of the data input. The data input can include data on the programs, applications, or information a user utilizes during a task, to allow use of desktop process analytics. This process may be used to either generate a new desktop analytics trigger or update an existing desktop analytics trigger.

Abstract: The present invention permits automated on-screen identification and obfuscation of sensitive data. The protective system receives a user's attempt to access sensitive data in a session, determines the user's level of access to sensitive data, and then obscures any sensitive data for which the user must supply credentials by placing at least one data field block over the sensitive data before the sensitive data is displayed on a desktop. As such the sensitive data cannot be seen by the user. The system can receive at least one credential from the user and compare it to at least one protective analytics rule in order to determine if it should remove the block and allow the user to view the sensitive data.

Abstract: The present invention is a method and system for automatedly producing at least one desktop analytics trigger. Upon receiving at least one type of data input, the system analyzes the data input and produces at least one desktop analytics trigger based on the results of the analysis of the data input. The data input can include data on the programs, applications, or information a user utilizes during a task, to allow use of desktop process analytics. This process may be used to either generate a new desktop analytics trigger or update an existing desktop analytics trigger.

Abstract: The present invention is a method and system for automatedly producing at least one desktop analytics trigger. Upon receiving at least one type of data input, the system analyzes the data input and produces at least one desktop analytics trigger based on the results of the analysis of the data input. The data input can include data on the programs, applications, or information a user utilizes during a task, to allow use of desktop process analytics. This process may be used to either generate a new desktop analytics trigger or update an existing desktop analytics trigger.

Abstract: The present invention is a method and system for automatedly producing at least one desktop analytics trigger. Upon receiving at least one type of data input, the system analyzes the data input and produces at least one desktop analytics trigger based on the results of the analysis of the data input. The data input can include data on the programs, applications, or information a user utilizes during a task, to allow use of desktop process analytics. This process may be used to either generate a new desktop analytics trigger or update an existing desktop analytics trigger.

Abstract: The present invention permits automated on-screen identification and obfuscation of sensitive data. The protective system receives a user's attempt to access sensitive data in a session, determines the user's level of access to sensitive data, and then obscures any sensitive data for which the user must supply credentials by placing at least one data field block over the sensitive data before the sensitive data is displayed on a desktop. As such the sensitive data cannot be seen by the user. The system can receive at least one credential from the user and compare it to at least one protective analytics rule in order to determine if it should remove the block and allow the user to view the sensitive data.