Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service inquiries. Customer service inquiries and answers from a first enterprise and/or industry are optionally used to train an inquiry classifier for a second enterprise and/or industry. The classifier being configured to predict if a new customer service inquiry will require access to confidential information for a human to generate an answer that resolves the inquiry.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include an expert system configured to identify clusters of inquiries that can be responded to using predetermined response content. A progressive approach to machine learning is disclosed, which combines unsupervised machine learning and supervised machine learning system. Human input used to improve the quality of data generated by the unsupervised machine learning system before that data is used to train the supervised machine learning system.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing the inquiries to an independent group of experts. Machine learning based systems are used in combination with contact center staff and/or the independent experts to resolve the customer service inquiries. The AI based systems can be configured to determine which of the customer service inquiries should be routed to the independent experts, and/or to generate content for inclusion in responses to the customer service inquiries. The machines systems are optionally trained based on scored responses to prior customer service inquiries. These scored responses can include content provided by a machine learning system and/or a human expert.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries town independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include an AI based system configured to determine which of the inquiries should be routed to the independent group of experts. The expert system is optionally trained based on scored responses to prior customer service inquiries.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service requests.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include an expert system configured to identify clusters of inquiries that can be responded to using predetermined response content. The expert system is optionally trained based on scored responses to prior customer service inquiries.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries town independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include an Al based system configured to determine which of the inquiries should be routed to the independent group of experts. The expert system is optionally trained based on scored responses to prior customer service inquiries.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service requests.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include an expert system configured to identify clusters of inquiries that can be responded to using predetermined response content. The expert system is optionally trained based on scored responses to prior customer service inquiries.

Abstract: Techniques for managing instant message (IM) communications are provided. In various embodiments, IM communications in a plurality of network implementations are managed using one or more policies. A policy in the one or more policies includes an action applicable for an IM communication. Once an IM communications is received from an IM client, a policy that is applicable for that IM communication is determined. After determining an applicable policy, an action associated with the policy for the instant message communication is performed. Examples of actions that may be taken include recording the IM communication, modifying the IM communication, blocking the IM communication, forwarding the IM communication, and the like.

Abstract: Network flows are identified by analyzing network traffic and network host information. The network host information may be collected by network host monitors associated with network hosts. Network traffic and network host information are evaluated against network flow profiles to identify network flows. If a network flows are identified with high certainty and are associated with previously identified network applications, then network flow policies can be applied to the network flows to block, throttle, accelerate, enhance, or transform the network flows. If a network flow is identified with lesser certainty or is not associated with a previously identified network application, then a new network flow profile can be created from further analysis of network traffic information, network host information, and possibly additional network host information collected to enhance the analysis.

Abstract: Techniques for enabling secure communications between IM modules are provided. An IM is received from a first IM client for a second IM client at a first IM module. It is determined if the second IM client can receive IMs through a second IM module that is capable of receiving secure communications from the first IM module. If the second IM module is capable of receiving secure communications from the first IM module, an encrypted IM is sent from the first IM module to the second IM client. The encrypted IM is received at the second IM module, which decrypts the IM and sends the decrypted IM to the second IM client.

Abstract: Techniques for managing instant message (IM) communications are provided. In various embodiments, IM communications in a plurality of network implementations are managed using one or more policies. A policy in the one or more policies includes an action applicable for an IM communication. Once an IM communications is received from an IM client, a policy that is applicable for that IM communication is determined. After determining an applicable policy, an action associated with the policy for the instant message communication is performed. Examples of actions that may be taken include recording the IM communication, modifying the IM communication, blocking the IM communication, forwarding the IM communication, and the like.

Abstract: Techniques for managing instant message (IM) communications are provided. Instant message communications of a plurality of network implementations is managed using an instant message module that uses one or more policies. A policy in the one or more policies includes an action applicable for an IM communication. The techniques comprise receiving an instant message communication at the instant message module. The IM communication may be in a network implementation of one of the plurality of network implementations. Then, a policy is determined from the one or more policies that is applicable for the instant message communication. An action associated with the policy for the instant message communication is then performed.

Abstract: Network flows are identified by analyzing network traffic and network host information. The network host information may be collected by network host monitors associated with network hosts. Network traffic and network host information are evaluated against network flow profiles to identify network flows. If a network flows are identified with high certainty and are associated with previously identified network applications, then network flow policies can be applied to the network flows to block, throttle, accelerate, enhance, or transform the network flows. If a network flow is identified with lesser certainty or is not associated with a previously identified network application, then a new network flow profile can be created from further analysis of network traffic information, network host information, and possibly additional network host information collected to enhance the analysis.

Abstract: Network flows are identified by analyzing network traffic and network host information. The network host information may be collected by network host monitors associated with network hosts. Network traffic and network host information are evaluated against network flow profiles to identify network flows. If a network flows are identified with high certainty and are associated with previously identified network applications, then network flow policies can be applied to the network flows to block, throttle, accelerate, enhance, or transform the network flows. If a network flow is identified with lesser certainty or is not associated with a previously identified network application, then a new network flow profile can be created from further analysis of network traffic information, network host information, and possibly additional network host information collected to enhance the analysis.

Abstract: Network flows are identified by analyzing network traffic and network host information. The network host information may be collected by network host monitors associated with network hosts. Network traffic and network host information are evaluated against network flow profiles to identify network flows. If a network flows are identified with high certainty and are associated with previously identified network applications, then network flow policies can be applied to the network flows to block, throttle, accelerate, enhance, or transform the network flows. If a network flow is identified with lesser certainty or is not associated with a previously identified network application, then a new network flow profile can be created from further analysis of network traffic information, network host information, and possibly additional network host information collected to enhance the analysis.