Abstract: Methods, systems, and apparatuses are described herein for using machine learning to generate and use synthetic profiles. A computing device may train a machine learning model to generate synthetic user profiles. The computing device may then use the trained machine learning model to generate a plurality of synthetic user profiles based on real user profile information, provide those synthetic user profiles to a quote provider via an API, then average the quotes received from that provider to determine an expected quote for the real user. The computing device may also collect a plurality of quotes from a quote provider based on synthetic user profiles, then train a second machine learning model to estimate quotes by that provider. That trained second machine learning model may be used to estimate quotes for users, and may be re-trained based on real quotes provided by the quote provider at a later time.

Abstract: Methods and systems are described herein for novel uses and/or improvements to artificial intelligence applications. As one example, methods and systems are described herein for enabling a resource management communications platform (e.g., an artificial-intelligence-based chatbot application) to intervene at opportune moments to reduce the likelihood that a user performs an undesirable action, while also minimizing the inconvenience to the user. For example, the system may identify implicit user information based on previous notifications and the characteristics of those notifications. Characteristics of previous notifications may include response times from the user.