Forward patent citations as predictive measures for diffusion of emerging technologies

M. Fallah, E. Fishman, R. Reilly
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引用次数: 17

Abstract

An extensive literature traces diffusion of knowledge and inventions using backward patent citations — i.e. patents cited by a new patent. Backward citations show how knowledge imbedded in past inventions influences new inventions. What can we learn from forward citations? Can forward citations be used to forecast the evolution of emerging technologies? In this study we analyze forward citations for three classes of technology — biotechnology, telecommunications and alternative energy. We use patent metrics to measure the rate of technological innovation. Following the existing literature, we examine the incremental advances in the three selected technological fields as indicated by forward patent citations. Our initial proposition is that forward citations should follow a classic S-curve distribution. The paper examines this proposition. Our study entails: selecting groups of patents issued at least 20 years, and some as far as 30 years ago; extracting the number of forward citations issued each year for each patent through 2008; and fitting the cumulative forward citations to a variety of models such S-, linear, quadratic and logistic curves. The analysis and findings are discussed in the paper.
前瞻性专利引用作为新兴技术传播的预测措施
大量文献使用反向专利引用(即新专利引用的专利)追踪知识和发明的传播。反向引用显示了过去发明中包含的知识如何影响新发明。我们能从前向引用中学到什么?前瞻引用可以用来预测新兴技术的发展吗?在本研究中,我们分析了生物技术、电信和替代能源这三类技术的正向引用。我们使用专利指标来衡量技术创新的速度。根据现有文献,我们考察了三个选定的技术领域的增量进展,如前向专利引用所示。我们最初的假设是,正向引文应该遵循经典的s曲线分布。本文考察了这一命题。我们的研究需要:选择至少20年的专利组,有些甚至是30年前的专利组;提取每个专利在2008年之前每年发布的引文数量;并将累积正向引用拟合到各种模型,如S曲线、线性曲线、二次曲线和logistic曲线。本文对分析结果进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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