School of Intellectual Property, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
School of Economics, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China.
PLoS One. 2024 Sep 3;19(9):e0309420. doi: 10.1371/journal.pone.0309420. eCollection 2024.
The digitalization of low-carbon energy technologies (LCET) provides important technical support for the transition to a greener energy system. Digitalization addresses the phenomenon of the growing application of information and communications technologies (ICT) across the economy, which is regarded as the technology convergence between ICT and other technologies. Scholars have revealed the signs that LCET and ICT are becoming increasingly interlinked, which raises the challenges for predicting and identifying the technology opportunities for innovations in the converged technology area. To address the challenges, this paper proposes a collaborative filtering approach to identify the digitalization technology opportunity of low-carbon energy technologies using patent classification and patent citation information. We applied the proposed collaborative filtering approach using a large LCET patent dataset derived from the United States Patent and Trademark Office (USPTO). The results indicate that the proposed method can effectively identify digitalization technology opportunities of LCET, and the current LCET digitalization technology opportunities identified based on this approach are mainly concentrated in the Energy storage field. The advantages of the proposed approach are that its underlying data are more readily available and its technical complexity is relatively lower, and thus, more replicable for other technology fields.
低碳能源技术的数字化为向更绿色的能源系统转型提供了重要的技术支持。数字化解决了信息和通信技术(ICT)在整个经济中日益广泛应用的现象,被认为是 ICT 与其他技术的技术融合。学者们已经揭示了低碳能源技术和 ICT 日益相互关联的迹象,这给预测和识别融合技术领域创新的技术机会带来了挑战。为了应对这些挑战,本文提出了一种协同过滤方法,利用专利分类和专利引文信息来识别低碳能源技术的数字化技术机会。我们使用来自美国专利商标局(USPTO)的大型低碳能源技术专利数据集应用了所提出的协同过滤方法。结果表明,该方法可以有效地识别低碳能源技术的数字化技术机会,并且基于该方法识别出的当前低碳能源技术的数字化技术机会主要集中在储能领域。该方法的优势在于其基础数据更容易获得,技术复杂性相对较低,因此更便于在其他技术领域复制。