  Computer input is more complex than a sequence of single mouse clicks and
keyboard presses. We introduce a novel method to identify and represent the
user interactions and build a system which predicts - in real-time - the action
a user is most likely going to take next. For this, a recurrent neural network
(RNN) is trained on a person's usage of the computer. We demonstrate that it is
enough to train the RNN on a user's activity over approximately a week to
achieve an accuracy of 34.63 % when predicting the next action from a set of
almost 500 possible actions. Specific examples for how these predictions may be
leveraged to build tools for improving and speeding up workflows of computer
users are discussed.
