Microsoft Technology Licensing, LLC

David V Ingerman

Steven J Altschuler

Lani Fang Wu

Edward K Y Jung

Greg Ridgeway

Lei Zhao

Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by &ldquo;clustering&rdquo; similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.

Methods and apparatus for analyzing computer-based tasks to build task models

Methods and apparatus for using task models to help computer users complete tasks

A pattern lattice data space as a framework for analyzing data, in which both schema-based and statistical analysis are accommodated, is defined. Ways to manage the size of the lattice structures in the pattern lattice data space are described. Utilities to classify or cluster, search (find similar data), or relate data using lattice fragments in the pattern lattice data space are also described. Superpattern cone or lattice generation function, which may be used by the classification and clustering functions, is also described. In addition, a subpattern cone or lattice generation process, which may be used by the search (find similar data) and data relating functions, is also described. Finally, a function to label, in readily understandable &ldquo;pidgin&rdquo;, categories which classify information, is also described.

Methods and apparatus for finding semantic information, such as usage logs, similar to a query using a pattern lattice data space

Methods and apparatus using task models for targeting marketing information to computer users based on a task being performed

Entering and evaluating group(s) of similar dependence hypotheses, such as time dependence hypotheses, and forecasting values of a variable based on the group(s) of similar dependence hypotheses. The group(s) of similar dependence hypotheses may be used to forecast requests for an Internet resource or Internet resources having one or more particular attributes. The entered group(s) of similar dependence hypotheses may be evaluated based on known data, such as past requests for Internet resources or Internet resources of a particular type.

Methods and apparatus for entering and evaluating time dependence hypotheses and for forecasting based on the time dependence hypotheses entered

Resource usage data is used to infer degrees of influence between users. Once such inferences are made, a directed graph representation of the users and the inferred 'influence' between the users can be generated. 'Influential rumormongers' can then be determined from the directed graph, for example, by using a greedy graph covering algorithm. In this way, marketing information can be targeted to 'influential rumormongers' to optimize its dissemination and impact. If actual (explicit) data regarding the influence between users is known, such data may be used to refine or replace at least some edge values.

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Princeton, NJ, United States of America

David V Ingerman