Humans have unique motion preferences when pursuing a given task. These motion preferences could be expressed as moving in a straight line, following the wall, avoiding sharp turns, avoiding damp surfaces or choosing the shortest path. While it would be very useful for a range of applications to allow robot systems or artificial agents to generate paths with similar specific characteristics, it is generally very difficult to capture and reproduce them from observed information since user trajectories can not be easily generalized. To address this, this paper introduces an approach that modifies a harmonic function path planner to model the user's motion preferences as parameters which could then be used to generate new paths in similar environments without the risk of collisions or incorrect paths. Given a small set of user-specific trajectories and starting from an initial, generic parameter configuration, this approach incrementally minimizes the difference between the direction of the user trajectory segments and the gradient of the parametric harmonic function by modifying its underlying parameters, thus capturing the trajectory preferences. Subsequently, these parameters could be transferred to new, locally similar environments and used to generate new paths. The use of harmonic function parameters to represent the user preferences not only facilitates customization of the path planner but also assures that the customized planner remains complete and correct.