Abstract

Technology life cycle (TLC) analysis provides essential support for investment-related strategies and helps to technology trajectory tracing, forecasting, and assessment. The most typical method used to identify TLC is the S-curve fitting method. However, doubts about its accuracy and reliability have been raised owing to the single indicator problem and the missing link between TLC and indicators. K-nearest neighbors (KNN) and hidden Markov model (HMM)-based methods are two influential methods that have been developed. However, something could be improved with these methods. The emerging order of stages is not under control, and the impact of early technology development on the later stages has yet to be addressed. These issues led us to propose a new method to identify TLC using multiple indicators based on machine learning techniques. We extracted ten indicators from the incoPat patent database and utilized a long short-term memory (LSTM) network–conditional random field (CRF) to identify TLC stages with the probability of technology being in a particular stage at a point of the year and changing to other stages during the following year. Moreover, this study investigates the theoretical meaning and empirical performance of indicators. 3-Dimensional print technology was selected as a case study, and its TLC was analyzed and prospects discussed. Comparison of this method and other methods are made as well. The results of our method that fit with the actual progression of technology are relatively accurate. Our analysis showed that the proposed method could offer a smooth and stationary TLC pattern that is accurate and easily understood.

中文翻译：

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基于LSTM和CRF的新技术生命周期分析方法

摘要

技术生命周期（TLC）分析为投资相关策略提供必要支持，有助于技术轨迹追踪、预测和评估。鉴别TLC最典型的方法是S曲线拟合法。但由于指标单一、TLC与指标之间缺乏联系等问题，其准确性和可靠性受到质疑。基于 K 最近邻（KNN）和隐马尔可夫模型（HMM）的方法是已开发的两种有影响力的方法。然而，这些方法可以改进一些东西。新兴阶段的顺序尚未得到控制，早期技术发展对后期阶段的影响尚未得到解决。这些问题促使我们提出了一种基于机器学习技术的使用多个指标来识别 TLC 的新方法。我们从 incoPat 专利数据库中提取了 10 个指标，并利用长短期记忆 (LSTM) 网络 - 条件随机场 (CRF) 来识别 TLC 阶段，以及技术在一年中某个时间点处于特定阶段并发生变化的概率。到下一年的其他阶段。此外，本研究还探讨了指标的理论意义和实证表现。选择3维打印技术作为案例研究，对其薄层色谱进行分析并展望前景。并对该方法与其他方法进行了比较。我们的方法的结果符合技术的实际进展，是比较准确的。我们的分析表明，所提出的方法可以提供准确且易于理解的平滑且稳定的 TLC 模式。