Abstract: Various embodiments of a system and method for order processing state management are described. Embodiments may include an order handling system that includes multiple processing components for performing a respective portion of an order fulfillment process for purchase orders. The order handling system may also include a data store for storing state information of the purchase orders. To process a given purchase order, the order handling system may utilize each processing component to complete a respective portion of the order fulfillment process for that purchase order. The order handling system may also utilize each component to, based on the respective task performed, update state information corresponding to the aforesaid purchase order in the data store. The order handling system may, subsequent to an interruption of the processing of the purchase order, resume the processing of the purchase order based on the updated state information.

Abstract: A Pattern System Environment is formed. The Pattern System Environment is a representation of information technology (IT) resources of a customer's business environment. The Pattern System Environment is based on information associated with the IT resources of the customer, and that information indicates how the IT resources are utilized during a business cycle of the customer. Pattern System Environments are usable in many environments, including those that support business resiliency.

Abstract: A valuation optimization method includes generating offeree decision information (buyer decision information, by way of illustrative example) by presenting a sequence of mechanisms to a sequence of offerees wherein the mechanisms comprise menus of transaction offers (sale offer menus, by way of illustrative example). Actual transactions (sale transactions, by way of illustrative example) are conducted responsive to acceptances of transaction offers by buyers. At a selected time in the generating, an offeree valuation distribution belief and the current mechanism are updated optimize an offeree's utility. The offeree's utility comprises an offeree's utility function constrained by a robust learning constraint computed based on a local differential of an earlier offeree's utility function with respect to the earlier offeree's valuation.