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 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. Multiple experts may provide responses to a particular customer service inquiry, optionally within a single chat window.

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 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 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.