Abstract: The present invention relates to a method and apparatus for assisting with automated task management. In one embodiment, an apparatus for assisting a user in the execution of a task, where the task includes one or more workflows required to accomplish a goal defined by the user, includes a task learner for creating new workflows from user demonstrations, a workflow tracker for identifying and tracking the progress of a current workflow executing on a machine used by the user, a task assistance processor coupled to the workflow tracker, for generating a suggestion based on the progress of the current workflow, and a task executor coupled to the task assistance processor, for manipulating an application on the machine used by the user to carry out the suggestion.

Abstract: The present invention relates to a method and apparatus for assisting with automated task management. In one embodiment, an apparatus for assisting a user in the execution of a task, where the task includes one or more workflows required to accomplish a goal defined by the user, includes a task learner for creating new workflows from user demonstrations, a workflow tracker for identifying and tracking the progress of a current workflow executing on a machine used by the user, a task assistance processor coupled to the workflow tracker, for generating a suggestion based on the progress of the current workflow, and a task executor coupled to the task assistance processor, for manipulating an application on the machine used by the user to carry out the suggestion.

Abstract: In one embodiment, the present invention is a method and apparatus for classifying nucleic acid responses to infectious agents. In one embodiment, a method for selecting genes to be analyzed to determine exposure to a condition (from among a plurality of potential conditions) includes determining, for each gene in a set of test data that includes genes and corresponding expression patterns for exposure to given conditions, a distance between each pair of conditions. A subset of genes from within the set of test data is then identified for which the distance between each pair of conditions is maximized. In this way, the number of genes whose expression patterns must be analyzed in order to reliably diagnose a condition is minimized.