Abstract: Embodiments of the invention are directed to a system and method for providing a high-volume transaction queueing, reserve ecommerce solution that automatically engages and queues transactions when a primary back-end transaction processing system becomes unresponsive or unstable. Through machine learning algorithms, embodiments of the invention control transaction submission rates by queuing them and throttling the rate at which they are processed based on self-awareness and constant monitoring, feedback and health checks of the primary system. When metrics indicate that the third-party system can begin accepting transactions again, the system automatically feeds the queued transactions along with real-time orders at a rate that the third-party system can successfully manage.

Abstract: A system and method obtaining structured information from a conversation including receiving a first input from a user, determining a first set of slots filled based on the first input using natural language processing and a non-linear slot filling algorithm, determining first conversation based on the first set of slots filled, determining a first empty slot associated with the first conversation, prompting the user for a second input, the second input associated with the first empty slot, filling the first empty slot using natural language processing and the non-linear slot filling algorithm, determining that the slots associated with the first conversation are filled; and, responsive to determining that the slots associated with the first conversation are filled, initiating an action associated with the conversation.

Abstract: Embodiments of the invention are directed to a system and method for providing a high-volume transaction queueing, reserve ecommerce solution that automatically engages and queues transactions when a primary back-end transaction processing system becomes unresponsive or unstable. Through machine learning algorithms, embodiments of the invention control transaction submission rates by queuing them and throttling the rate at which they are processed based on self-awareness and constant monitoring, feedback and health checks of the primary system. When metrics indicate that the third-party system can begin accepting transactions again, the system automatically feeds the queued transactions along with real-time orders at a rate that the third-party system can successfully manage.