本研究以專利分析探討美國製藥公司之獲利性，並以人工類神經網絡的方式進行分析，探討專利數（Patent Counts）、自我專利引證數（Self-Citations）與最重要技術領域之相對專利定位（Relative Patent Position of Most Important Technology）對公司獲利性之影響。本研究三個研究假說均獲支持，因此最重要技術領域之相對專利定位會對公司獲利性產生正向影響，而專利數與自我專利引證數會對公司獲利性產生倒Ｕ型影響，故美國製藥業公司若想要提升公司獲利性的話，應提高其最重要技術領域之相對專利定位，並調整其專利數與自我專利引證數至最適點。

This study uses patent analysis to explore the corporate profitability of the American pharmaceutical companies by use of artificial neural network (ANN). Besides, this paper explores the nonlinear influences of patent-counts, self-citations, relative patent position upon corporate profitability of the American pharmaceutical companies. The results show that relative patent position of most important technology has a nonlinearly and monotonically positive influence upon the corporate profitability. However, patent counts and self-citations have inverse U-shaped influences upon corporate profitability. Therefore, if the American pharmaceutical companies want to enhance their profitability, they should enhance their relative patent position in their most important technological fields and adjust their patent counts and self-citations to the optimal points.