Summary: High utility pattern mining is an important research topic in data mining. Because of the additional inner/outer utility processing workload, its computational complexity increases, and the improvement of its temporal efficiency is vital. To address this issue, a new pattern-growth mining algorithm is proposed for high utility pattern mining named HUPM-FP. This algorithm can mine high utility patterns from a global tree without generating candidate itemsets. Six classical datasets were used in our experiments for comparing with the state-of-art algorithm faster high-utility itemset mining (FHM). The proposed HUPM-FP out-performs its counterpart significantly, especially for time efficiency, which is up to 1 order of magnitude faster.