Abstract: A system and method of automatically classifying a communication involving at least one human, e.g., a human-to-human telephone conversation, into predefined categories of interest, e.g., “angry customer, etc. The system automatically, or semi-automatically with user interaction, expanding user input into semantically equivalent events. The system recognizes these events, each of which is given a confidence level, and classifies the telephone conversation based on an overall confidence level. In some embodiments, a different base unit is utilized. Instead of recognizing individual words, the system recognizes composite words, each of which is pre-programmed as an atomic unit, in a given context. The recognition includes semantically relevant composite words and contexts automatically generated by the system.

Abstract: A method for providing automatic, personalized information services to a computer user includes the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability.

Abstract: A method for providing automatic, personalized information services to a computer user includes the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability.