Patent attributes
Representative embodiments disclose mechanisms to automatically rank and select extensions triggered in a digital assistant. A sample set of extensions are executed against a set of curated queries in order to extract a set of features and/or statistics. The system trains a machine learning model based on the features and/or statistics to rank and select extensions based on their response to a query. New extension incorporated into the system are executed against a second set of curated queries to obtain a set of extracted features and/or statistics which are saved for use at runtime. At runtime, a query phrase received by the system triggers one or more tasks from extensions. Extracted features for the triggered extensions are combined with stored features/statistics and at least a subset of the results presented to the trained ranking and selection model. The model ranks and selects appropriate tasks which are presented to the user.
