  We introduce a framework for adapting a virtual keyboard to individual user
behavior by modifying a Gaussian spatial model to use personalized key center
offset means and, optionally, learned covariances. Through numerous real-world
studies, we determine the importance of training data quantity and weights, as
well as the number of clusters into which to group keys to avoid overfitting.
While past research has shown potential of this technique using
artificially-simple virtual keyboards and games or fixed typing prompts, we
demonstrate effectiveness using the highly-tuned Gboard app with a
representative set of users and their real typing behaviors. Across a variety
of top languages, we achieve small-but-significant improvements in both typing
speed and decoder accuracy.
