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Computer input is more complex than a sequence of single mouse clicks andkeyboard presses. We introduce a novel method to identify and represent theuser interactions and build a system which predicts - in real-time - the actiona 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 isenough to train the RNN on a user's activity over approximately a week toachieve an accuracy of 34.63 % when predicting the next action from a set ofalmost 500 possible actions. Specific examples for how these predictions may beleveraged to build tools for improving and speeding up workflows of computerusers are discussed.

A Click Ahead: Real-Time Forecasting of Keyboard and Mouse Actions using RNNs and Computer Vision