BEHAVIORAL LEARNING FOR A VISUAL REPRESENTATION IN A COMMUNICATION ENVIRONMENT

Utterances comprising text and behavioral movement commands entered by a user are processed to identify patterns of behavioral movements executed by the user's visual representation. Once identified, the patterns are used to generate behavioral movements responsive to new utterances received from the user, without requiring the user to explicitly alter the behavioral characteristics selected by the user. An application module parses an utterance generated by a user to determine the presence of gesture commands. If a gesture command is found in an utterance, the utterance is stored for behavioral learning processing. A stored utterance is analyzed with existing stored utterances to determine if the stored utterances provide the basis for creating a new behavioral rule. Newly stored utterances are first analyzed to generate different contexts associated with the behavioral movement. To determine if any of the contexts should be used as a basis for a new behavioral rule in an embodiment in which contexts are stored, the contexts of the existing utterances in the log are compared with the new contexts. If a context appears in the log at a frequency above a threshold, then the context is used as the basis for a new behavioral rule. The new behavioral rule is then used to modify existing rules, or create more generally applicable rules. New general rules are not created unless the number of existing rules that could create a behavioral rule exceeds a threshold to control how persistent a user's behavior must be to create a new rule.