Abstract: A client-side virtual private network (VPN) chaining architecture can provision multiple sessions for multiple VPN clients that are configured to communicate packet traffic in parallel between an end-user device and one or more destinations. The client-side chaining architecture can capture packet traffic per specific users/apps and process (e.g., drop) or reroute the captured packet traffic for different VPN clients. For example, packet traffic can be rerouted from a main VPN client to a secondary VPN client. As such, there can be multiple VPN clients that are simultaneously chained in various ways to the same end-user device.

Abstract: A client-side virtual private network (VPN) chaining architecture can provision multiple sessions for multiple VPN clients that are configured to communicate packet traffic in parallel between an end-user device and one or more destinations. The client-side chaining architecture can capture packet traffic per specific users/apps and process (e.g., drop) or reroute the captured packet traffic for different VPN clients. For example, packet traffic can be rerouted from a main VPN client to a secondary VPN client. As such, there can be multiple VPN clients that are simultaneously chained in various ways to the same end-user device.

Abstract: A system that transforms electronic messages into annotated messages that include contextual information to aid a recipient in utilizing the electronic message, understanding its meaning, and responding to the message. Annotations are additions or modifications to the original message with contextual information that is related to the features and contents of the original message. Message features are extracted and used to search one or more sources of contextual information. Relevant items are retrieved and added to the message, for example as attachments, hyperlinks, or inline notes. Machine learning techniques may be used to generate or refine modules for feature extraction and information selection. Feedback components may be used to track the usage and value of annotations, in order to iteratively improve the annotation system.

Abstract: A system that transforms electronic messages into annotated messages that include contextual information to aid a recipient in utilizing the electronic message, understanding its meaning, and responding to the message. Annotations are additions or modifications to the original message with contextual information that is related to the features and contents of the original message. Message features are extracted and used to search one or more sources of contextual information. Relevant items are retrieved and added to the message, for example as attachments, hyperlinks, or inline notes. Machine learning techniques may be used to generate or refine modules for feature extraction and information selection. Feedback components may be used to track the usage and value of annotations, in order to iteratively improve the annotation system.

Abstract: A system that transforms electronic messages into annotated messages that include contextual information to aid a recipient in utilizing the electronic message, understanding its meaning, and responding to the message. Annotations are additions or modifications to the original message with contextual information that is related to the features and contents of the original message. Message features are extracted and used to search one or more sources of contextual information. Relevant items are retrieved and added to the message, for example as attachments, hyperlinks, or inline notes. Machine learning techniques may be used to generate or refine modules for feature extraction and information selection. Feedback components may be used to track the usage and value of annotations, in order to iteratively improve the annotation system.

Abstract: A system that transforms electronic messages into annotated messages that include contextual information to aid a recipient in utilizing the electronic message, understanding its meaning, and responding to the message. Annotations are additions or modifications to the original message with contextual information that is related to the features and contents of the original message. Message features are extracted and used to search one or more sources of contextual information. Relevant items are retrieved and added to the message, for example as attachments, hyperlinks, or inline notes. Machine learning techniques may be used to generate or refine modules for feature extraction and information selection. Feedback components may be used to track the usage and value of annotations, in order to iteratively improve the annotation system.