Abstract: A computer-implemented method, computer program product and computing system for defining a first feature group having a first plurality of options. At least one additional feature group having at least one additional plurality of options is defined. A first level-one sample assembly is defined that includes an option chosen from the first plurality of options and an option chosen from the at least one additional plurality of options. A level-one probabilistic model is defined based, at least in part, upon the first level-one sample assembly.

Abstract: A computer-implemented method, computer program product and computing system for receiving non-structured content concerning a plurality of items. The non-structured content is processed to identify one or more proposed features for the plurality of items. The one or more proposed features are provided to a user for review. Feature feedback concerning the one or more proposed features is received. The one or more proposed features are modified based, at least in part, upon the feature feedback received from the user, thus generating one or more approved features.

Abstract: A computer-implemented method, computer program product and computing system for receiving user content for analysis. Key content included within the user content is identified. Surplus content included within the user content is identified.

Abstract: A method, computer program product, and computer system for acquiring data representing a plurality of collaboration items, each collaboration item being associated with one of a communication and a collaboration among a subset of one or more users. Using a machine learning procedure, one of at least one latent variable and at least one action variable in a model of the data representing the plurality of collaboration items may be determined. At least one of a representation of the collaboration items may be presented to one or more users based upon, at least in part, the at least one latent variable, and potential collaboration actions may be presented to the one or more users based upon, at least in part, the at least one action variable.

Abstract: A computer-implemented method, computer program product and computing system for receiving a first piece of content that has a first structure and includes a first plurality of items. A second piece of content is received that has a second structure and includes a second plurality of items. Commonality between the first piece of content and the second piece of content is identified. The first piece of content and the second piece of content are combined to form combined content that is based, at least in part, upon the identified commonality.

Abstract: A computer-implemented method, computer program product and computing system for defining a model that includes a plurality of variables. At least one variable of the plurality of variables is conditioned based, at least in part, upon a conditioning command received from a user, thus defining a conditioned variable. The model is inferenced based, at least in part, upon the conditioned variable.

Abstract: A method, computer program product, and computer system for identifying, by a computing device, a plurality of content from at least one source. A first portion of the plurality of content may be categorized in a first feed category based on a first probabilistic model. A second portion of the plurality of content may be categorized in a second feed category based on the first probabilistic model. User feedback may be received to change the categorization of a first content of the first portion of the plurality of content in the first feed category to the second feed category. A second probabilistic model may be generated based upon, at least in part, the user feedback. The categorization of a second content of the first portion of the plurality of content in the first feed category may be reorganized to the second feed category based upon, at least in part, the second probabilistic model.