Abstract: A computer-implemented method, computer program product and computing system for maintaining an ML object collection that defines a plurality of ML objects; and associating usage criteria with each of the plurality of ML objects.

Abstract: A computer-implemented method, computer program product and computing system for allowing a plurality of entities to access an ML object collection that defines a plurality of ML objects; and making an inquiry to a first entity, chosen from the plurality of entities, about a specific ML object defined within the ML object collection.

Abstract: A computer-implemented method, computer program product and computing system for accessing an ML object collection that defines a plurality of ML objects; and identifying a first ML object chosen from the plurality of ML objects defined within the ML object collection.

Abstract: A computer-implemented method, computer program product and computing system for obtaining the word-based synonym ML object from an ML object collection that defines a plurality of ML objects; adding the word-based synonym ML object to a probabilistic model; generating a list of synonym words via the word-based synonym ML object; and enabling the list of synonym words to be edited by a user.

Abstract: A computer-implemented method, computer program product and computing system for identifying a specific uncertainty in a probabilistic model; providing a user with one or more initial questions concerning the specific uncertainty; and receiving a response from the user concerning the one or more initial questions.

Abstract: A computer-implemented method, computer program product and computing system for identifying the need for a probabilistic model to process existing content; importing a navigatable structure; utilizing the navigatable structure as a basis for an initial probabilistic model; and determining whether the initial probabilistic model is a good explanation of the existing content.

Abstract: A computer-implemented method, computer program product and computing system for obtaining the phrase-based synonym ML object from an ML object collection that defines a plurality of ML objects; adding the phrase-based synonym ML object to a probabilistic model; generating a list of synonym phrases via the phrase-based synonym ML object; and enabling the list of synonym phrases to be edited by a user.

Abstract: A computer-implemented method, computer program product and computing system for maintaining an ML object collection that defines at least one ML object, wherein the at least one ML object defines a plurality of linked versions of the ML object.

Abstract: A computer-implemented method, computer program product and computing system for accessing an ML object collection that defines a plurality of ML objects; and assigning a confidence level to a specific ML object, chosen from the plurality of ML objects, concerning the applicability of the specific ML object for use with a probabilistic model.

Abstract: A computer-implemented method, computer program product and computing system for maintaining an ML object collection that defines a plurality of ML objects; and associating access criteria with each of the plurality of ML objects.

Abstract: A computer-implemented method, computer program product and computing system for identifying a need for an ML object within a probabilistic model; accessing an ML object repository that defines a plurality of ML objects; obtaining a first ML object selected from the plurality of ML objects defined within the ML object repository; and adding the first ML object to the probabilistic model.

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 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 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 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 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 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.