Machine learning-based systems and methods for predicting a digital activity and automatically executing digital activity-accelerating actions

A method for machine learning-informed surfacing and automated execution of digital activity-accelerating actions, the method comprising:

• constructing a digital activity-accelerator registry that includes a plurality of composite activity sequences, wherein each of the plurality of composite activity sequences relates to a distinct collection of interdependent computer-executable tasks;
• identifying a target digital artifact;
• and
• based on identifying the target digital artifact:
• computing, by one or more computers operating an ensemble of machine learning models, a plurality of activity-accelerating inferences for the target digital artifact, including:
• (1) a first activity-accelerating inference that relates to an estimated intent of the target digital artifact,
• (2) a second activity-accelerating inference that relates to an estimated domain of an activity associated with the target digital artifact, and
• (3) a third activity-accelerating inference that relates to an estimated sub-domain of the activity associated with the target digital artifact;
• searching the digital activity-accelerator registry based on the plurality of activity-accelerating inferences, wherein searching the digital activity-accelerator registry includes:
• constructing a composite activity sequence search query that comprises a plurality of search parameters, wherein the plurality of search parameters includes:
• (A) a first search parameter that causes the search query, when executed, to search for composite activity sequences in the digital activity-accelerator registry that include a computer-executable task relating to the estimated intent of the target digital artifact,
• (B) a second search parameter that causes the search query, when executed, to search for composite activity sequences in the digital activity-accelerator registry that relate to the estimated domain of the activity associated with the target digital artifact, and
• (C) a third search parameter that causes the search query, when executed, to search for composite activity sequences in the digital activity-accelerator registry that relate to the estimated sub-domain of the activity associated with the target user artifact;
• and
• executing the composite activity sequence search query, wherein executing the composite activity sequence causes one or more target composite activity sequences to be returned from the digital activity-accelerator registry if the one or more target composite activity sequences satisfy the plurality of search parameters;
• extracting at least a subset of the computer-executable tasks included in the one or more target composite activity sequences based on one or more pre-defined action-surfacing criteria;
• and
• displaying, via a graphical user interface, a digital representation corresponding to each computer-executable task included in the subset, wherein each digital representation displayed in the graphical user interface is selectable and, when selected, causes a corresponding activity-accelerating task to be automatically executed by the one or more computers.