In Support Vector Machines, what is the 'Kernel Trick' used to achieve?

Implicit high-dimensional mapping

The kernel trick allows SVMs to find a linear separator in a higher-dimensional feature space without ever explicitly calculating the coordinates in that space. It computes the inner products between the images of all pairs of data in the feature space.