International Business Machines Corporation

Genesys Telecommunications Laboratories, Inc.

Martin C Buskirk, Jr

Frederick J Damerau

David H Anderson

David E Johnson

David H Johnson

Shelley St Phillips Germain

Marguerite Raaen

Danilo P Servilla

Giuseppe DiAdamo

Scott Charles Lamb

Richard J Madrid

A method of automatically labeling of unlabeled text data can be practiced independent of human intervention, but that does not preclude manual intervention. The method can be used to extract relevant features of unlabeled text data for a keyword search. The method of automated labeling of unlabeled text data uses a document collection as a reference answer set. Members of the answer set are converted to vectors representing centroids of unknown groups of unlabeled text data. Unlabeled text data are clustered relative to the centroids by a nearest neighbor algorithm and the ID of the relevant answer is assigned to all documents in the cluster. At this point in the process, a supervised machine learning algorithm is trained on labeled data, and a classifier for assigning labels to new text data is output. Alternatively, a feature extraction algorithm may be run on classes generated by the step of clustering, and search features output which index the unlabeled text data.

Automatic labeling of unlabeled text data

Methods, systems and computer program products are provided which automatically distribute tasks associated with a telephone system and tasks associated with a computer system between a plurality of agents tracking ACD telephone status of the plurality of agents and tracking computer status of the plurality of agents. When a request for a computer task is received, an agent of the plurality of agents is assigned to the computer task corresponding to the received request based on the ACD telephone status and the computer status of the agent of the plurality of agents.

Methods, systems and computer program products for automatic task distribution

A machine learning based electronic mail system. A classifier and action selection module analyzes the incoming message and classifies the messages with associated confidence levels, which may include analyzing the electronic message by tokenization of the text, morphological analysis of the text, and other well known processes. The classifier and action selection module then determines the appropriate action or actions to effect on the message.

Machine learning based electronic messaging system

The present invention enables a user, logged on to a data network over a traditional telephone line, to place additional outgoing calls over that same line without interruption of the data session. The user is also able to provide communication between the outgoing call and the data session such that, voice over the data session can be conferenced into voice over the newly outgoing telephone session.

Method and apparatus for placing telephone calls while connected to the internet

The present invention enables a user, logged on to a data network over a traditional telephone line, to be notified of incoming calls over that same line without interruption of the data session. In addition, the user can accept the incoming call without terminating the data session. The user is also able to provide communication between the incoming call and the data session such that, voice over the data session can be conferenced into voice over the incoming telephone line.

Growing community of inventors

Raleigh, NC, United States of America

Martin C Buskirk, Jr