Abstract: A system for performing hypothesis generation is provided. An extraction processor extracts an entity from a data set. An association processor associates the extracted entity with a set of reference entities to obtain a potential association wherein the potential association between the extracted entity and the set of reference entities is described using a vector-based belief-value-set. A threshold processor determines whether a set of belief values of the vector-based belief-value-set exceed a predetermined threshold. If the belief values exceed a predetermined threshold the threshold processor adopts the association.

Abstract: A methodology, a system, and an apparatus is defined for performing evidence-based decision-making about matching a given entity against one or more of a set of known or reference entities. A satisfactory decision is achieved as a function of both potentiality and plausibility, where plausibility refers to the full set of values garnered by the evidence accumulation process in the process of generating belief/disbelief/uncertainty/conflict masses. Potentiality is a mechanism to set the various match threshold values, where the thresholds define acceptable confidence levels for decision-making. Evidence is computed on the basis of partial matching of feature vector elements, where separate and distinct feature vectors are associated with both the given entity and each of the reference entities. Following evidence-combination methods, evidence is accrued for both the positive and negative decisions regarding a potential match.

Abstract: A predictive architecture system and method that supports predicting likely future evolutions by aggregating and analyzing large amounts of data. The invented system includes a multi-level architecture, along with one or more feedback functions that drive search and discovery for information in support of a “hypothesis” or discovery of significant information in advance of clear hypothesis formation and passing on higher-level predictive support or lack of support for existing and/or new hypotheses.

Abstract: A knowledge discovery apparatus and method that extracts both specifically desired as well as pertinent and relevant information to query from a corpus of multiple elements that can be structured, unstructured, and/or semi-structured, along with imagery, video, speech, and other forms of data representation, to generate a set of outputs with a confidence metric applied to the match of the output against the query. The invented apparatus includes a multi-level architecture, along with one or more feedback loop(s) from any level n to any lower level n?1 so that a user can control the output of this knowledge discovery method via providing inputs to the utility function.

Abstract: A knowledge discovery apparatus and method that extracts both specifically desired as well as pertinent and relevant information to query from a corpus of multiple elements that can be structured, unstructured, and/or semi-structured, along with imagery, video, speech, and other forms of data representation, to generate a set of outputs with a confidence metric-applied to the match of the output against the query. The invented apparatus includes a multi-level architecture, along with one or more feedback loop(s) from any Level N to any lower Level so that a user can control the output of this knowledge discovery method via providing inputs to the utility function.