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This invention outlines a method for optimizing complex processes. A computer breaks down a process into individual tasks, maps out how these tasks relate to each other in a network, and then, based on a specific goal or priority, determines the best set of tasks to perform. This selection can be based on identifying characteristics of tasks, such as resources needed or outputs, and choosing those most likely to meet the objective.
Why it matters: Filed before the widespread adoption of advanced AI and machine learning. Modern AI, especially large language models, could now automate the parsing of process descriptions, inferring task relationships, and dynamically optimizing task selection based on complex criteria, which would have been a significant manual or rule-based effort in 2013.
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