International Business Machines Corporation

Michael Ponce Sanchez

William Scott Spangler

Jeffrey Thomas Kreulen

Justin Thomas Lessler

A system and method automatically identify candidate helpdesk problem categories that are most amenable to automated solutions. The system generates a dictionary wherein each word in the text data set is identified, and the number of documents containing these words is counted, and a corresponding count is generated. The documents are partitioned into clusters. For each generated cluster, the system sorts the dictionary terms in order of decreasing occurrence frequency. It then determines a search space by selecting the top dictionary terms as specified by a user defined depth of search. Next, the system chooses a set of terms from the search space as specified by a user-defined value indicating the desired level of detail. For each possible combination of frequent terms in the search space, the system finds the set of examples containing all the terms, and then determines if the frequency is sufficiently high and the overlap sufficiently low for this candidate set of examples to be a frequently asked question.

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Tustin, CA, United States of America

Michael Ponce Sanchez