Just keep in mind, the kernel trick is a way to transform a data set so that "linear things work better"... although that's very vague I mean sure it's passable but it's also different from what was originally posted... the kernel trick doesn't transform your data into a space where that data becomes linear. It transforms your data into a space where it can be separated by a line/plane. The data is almost always non-linear in that transformed space but it's transformed in a way that a plane can cleanly separate that data.

Given that the kernel trick is pretty specific jargon used mostly in a specific circumstance, it's in your interest to use that term in that specific context. If you're interested in the more general term of making things work with respect to some function, which can be linear or Gaussian or some other form the term is "feature transformation".