Abstract: Computer-implemented systems and methods are provided for improved generation and control of conversations. A computing device is utilized to control or simulate conversation using estimated contextual cues extracted from profile information or prior responses. The computing device is configured to automatically tailor a flow of a conversation to an effort to improve relevancy and engagement without the need of a human operator to manually tailor the conversation, which, for example, could be impractically expensive. A structured workflow is maintained in the form of a series of conversation decisions, and a machine learning engine is utilized to maintain a continuously trained data structure that generates predictions that bias conversation decisions (e.g., by weighting tree options) for tailoring the conversation flow.

Abstract: Computer-implemented systems and methods are provided for improved generation and control of conversations. A computing device is utilized to control or simulate conversation using estimated contextual cues extracted from profile information or prior responses. The computing device is configured to automatically tailor a flow of a conversation to an effort to improve relevancy and engagement without the need of a human operator to manually tailor the conversation, which, for example, could be impractically expensive. A structured workflow is maintained in the form of a series of conversation decisions, and a machine learning engine is utilized to maintain a continuously trained data structure that generates predictions that bias conversation decisions (e.g., by weighting tree options) for tailoring the conversation flow.