Brian L. Hazlehurst - Portland OR Scott M. Burke - Corvallis OR Kristopher E. Nybakken - Portland OR
Assignee:
Sapient Health Network - Portland OR
International Classification:
G06F 1700
US Classification:
707 3
Abstract:
An Intelligent Query Engine (IQE) system automatically develops multiple information spaces in which different types of real-world objects (e. g. , documents, users, products) can be represented. Machine learning techniques are used to facilitate automated emergence of information spaces in which objects are represented as vectors of real numbers. The system then delivers information to users based upon similarity measures applied to the representation of the objects in these information spaces. The system simultaneously classifies documents, users, products, and other objects. Documents are managed by collators that act as classifiers of overlapping portions of the database of documents. Collators evolve to meet the demands for information delivery expressed by user feedback. Liaisons act on the behalf of users to elicit information from the population of collators.
Intelligent Query System For Automatically Indexing In A Database And Automatically Categorizing Users
Brian L. Hazlehurst - Portland OR Scott M. Burke - Corvallis OR Kristopher E. Nybakken - Portland OR
Assignee:
Webmd Corporation - Atlanta GA
International Classification:
G06F 1700
US Classification:
707102
Abstract:
An intelligent Query Engine (IQE) system automatically develops multiple information spaces in which different types of real-world objects (e. g. , documents, users, products) can be represented. Machine learning techniques are used to facilitate automated emergence of information spaces in which objects are represented as vectors of real numbers. The system then delivers information to users based upon similarity measures applied to the representation of the objects in these information spaces. The system simultaneously classifies documents, users, products, and other objects. Documents are managed by collators that act as classifiers of overlapping portions of the database of documents. Collators evolve to meet the demands for information delivery expressed by user feedback. Liaisons act on the behalf of users to elicit information from the population of collators.