International Business Machines Corporation

Justin Thomas Lessler

Jeffrey Thomas Kreulen

William Scott Spangler

Vikas Krishna

Joann Ruvolo

Carl Kraenzel

Stefan Bengt Edlund

William Cody

Paul Brian Moody

Thomas P Moran

W Scott Spangler

Larry Lee Proctor

Amy W Chow

Sesha Sung Shan Baratham

Michael Ponce Sanchez

Described is a method for synchronizing a client with a server. A document score is calculated for each document in a server database. The document score indicates the importance of the respective document to the client. Documents are transferred from the server to the client if their document scores exceed a threshold value. The calculation of the document score can be determined from time the document was created, the number of times the document has been read, the time the document was last accessed, the author of the document and one or more terms in the document. Relationships between the document and other documents on the server database can also affect the document score.

Method for synchronizing documents for disconnected operation

Described are a dynamic interest profile (DIP) system and method for dynamically tracking interests of a user based on personal information. The DIP system obtains electronic documents of the user from a document stream and processes the documents to obtain certain information therefrom. Based on the information obtained from the documents, the DIP system identifies terms, people, documents, and collections that are of importance to the user. These items of importance become part of a dynamic interest profile of the user. The dynamic interest profiles persist in a database. The DIP system also provides an application program interface (API) for accessing DIPs in the database. Application programs can employ this API to customize program behavior to the particular interests of the user executing those programs.

System and method for dynamically tracking user interests based on personal information

A method of generating a context-inferenced search query and of sorting a result of the query is described. The method includes analyzing an event associated with the user to determine a contextual setting, dynamically generating a search query based on the contextual setting, and searching at least one information source using the search query to generate a search result. Additionally, the method includes calculating an importance value for each item of the search result, sorting the items of the search result according the importance value, and displaying the sorted search result to the user.

Method of generating a context-inferenced search query and of sorting a result of the query

A method and structure for analyzing a database having non-text data in data fields and text in text fields. The invention first selects a subset of the database based upon criteria. The subset includes data field(s) and associated text field(s). The invention searches for data matching the criteria within structured data fields of the database. If the invention searches multiple databases, the invention creates shared dimensions for databases that do not share common attributes. The invention automatically selects a relatively short text phrase from the text fields that helps to explain the underlying meaning (i.e. unique text content) of a data subset selected using the non-text data fields.

Text explanation for on-line analytic processing events

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.

Method for automatically finding frequently asked questions in a helpdesk data set

A computing system and method explores a knowledge repository by accepting a natural language query from a user, determining a distance between the query and every category in every partitioning of the knowledge repository, and displaying a radial graph ( ) of the nearest categories. Further, in response to a user selecting a category, visually displaying matching elements in the category along with its nearest neighbor categories in a scatter plot ( ).

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San Jose, CA, United States of America

Justin Thomas Lessler