Classification and transformation of sequential event data

A system for processing events, the system comprising:

• a memory storing a set of computer-executable instructions;
• and
• at least one processor interfacing with the memory and configured to execute the set of computer-executable instructions to cause the at least one processor to:
• receive a current indicator corresponding to a current event,
• append the current indicator to a sequence of indicators comprising previously received indicators sequentially ordered in a received order, wherein the sequence of indicators corresponds to a series of sequential events to form a current sequence of indicators,
• generate, for each indicator in the current sequence of indicators, a set of current milestone probabilities including a probability of rejection,
• reference a milestone graph defining a valid ordered sequence of transitions to milestones and associated constraints,
• generate a current search space graph comprising a set of current valid paths through a set of current milestones in response to the milestone graph and the current sequence of indicators, wherein the current search space graph is a culled version of a complete search space graph generated by applying a set of constraints associated with the milestone graph,
• select, for each indicator in the current sequence of indicators, a current milestone probability of the set of current milestone probabilities in response to finding a current shortest path through the current search space graph, and
• classify, using a machine learning technique, each indicator in the current sequence of indicators according to the current milestone probability that was selected to generate a classification result for each indicator in the current sequence of indicators and to improve an efficiency in finding an optimal solution in a tractable manner.

A non-transitory computer readable medium storing a set of computer instructions executable by at least one processor, the set of computer instructions comprising:

• instructions for receiving, by the at least one processor, a current indicator corresponding to a current event;
• instructions for appending, by the at least one processor, the current indicator to a sequence of indicators comprising previously received indicators sequentially ordered in a received order, wherein the sequence of indicators corresponds to a series of sequential events to form a current sequence of indicators;
• instructions for generating, by the at least one processor for each indicator in the current sequence of indicators, a set of current milestone probabilities including a probability of rejection;
• instructions for referencing, by the at least one processor, a milestone graph defining a valid ordered sequence of transitions to milestones and associated constraints;
• instructions for generating, by the at least one processor, a current search space graph comprising a set of current valid paths through a set of current milestones in response to the milestone graph and the current sequence of indicators, wherein the current search space graph is a culled version of a complete search space graph generated by applying a set of constraints associated with the milestone graph;
• instructions for selecting, by the at least one processor for each indicator in the current sequence of indicators, a current milestone probability of the set of current milestone probabilities in response to finding a current shortest path through the current search space graph;
• and
• instructions for classifying, using a machine learning technique by the at least one processor, each indicator in the current sequence of indicators according to the current milestone probability that was selected to generate a classification result for each indicator in the current sequence of indicators and to improve an efficiency in finding an optimal solution in a tractable manner.