Digital employee experience improvement based on de-identified productivity data signals

Abstract

Embodiments provide a computer that includes one or more processors and instructions stored on one or more memory devices. The one or more processors executes the instructions to monitor, using one or more extractor application programming interfaces (APIs), a plurality of streams associated with user interactions with the computer, each of the plurality of streams including one or more events or activities associated with the user interactions, the user interactions being interactions with one or more different applications or platforms of the computer. The processor may extract contents and contexts from the plurality of streams using the one or more extractor APIs. The processor further determines one or more workflows associated with the user, the one or more workflows including the extracted contents and contexts, the workflows indicating the user interactions with the computer during a time series comprising a plurality of timestamps associated with times at which the user performed the interactions with the computer. The processor stores the one or more the workflows at the user computer. The processor determines one or more chunks of the one or more workflows, each chunk being a subpart of one of the one or more workflows and corresponding to a set of the plurality of timestamps associated with the subpart, each chunk including the content and contexts of the one or more workflows associated with the set of the plurality of timestamps. The processors evaluates the one or more chunks, the evaluating creating one or more inferred actions using the content and contexts of the one or more workflows associated with the set of the plurality of timestamps and automates one or more subsequent workflows associated with the user based on the one or more inferred actions.