Identifying the technology convergence using patent text information: A graph convolutional networks (GCN)-based approach
Identifying the technology convergence using patent text information: A graph convolutional networks (GCN)-based approach
复制标题
使用专利文本信息识别技术融合：基于图卷积网络 (GCN) 的方法
DOI：
10.1016/j.techfore.2022.121477
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发表时间：
2022
影响因子：
12
通讯作者：
Motohashi Kazuyuki
中科院分区：
文献类型：
--
作者：
Zhu Chen;Motohashi Kazuyuki
技术融合所创造的新价值和新产品对现有行业和市场的颠覆性改造潜力很大。在这方面，公司必须尽早了解和确定潜在的融合模式，以便及时制定战略计划。这项研究提出了一种新的语义方法，展示了如何使用图卷积网络模型来监测技术融合。特别是，该模型经过训练，可以生成专利和技术关键词向量，从中可以得出新的指标。我们验证了这些新的指标，并表明，该方法优于现有的研究，使用信息的交叉引用和国际专利分类类同现。此外，我们使用人工智能（AI）和分布式账本技术（DLT）之间的融合案例研究，展示了所提出的方法在监测技术融合方面的有用性。结果表明，AI和DLT之间的收敛主要是通过将AI用于DLT来驱动的，并且每个关键字（子域）在收敛过程中的作用也被提出。
The potential for new values and products created by technology convergence to disruptively transform existing industries and markets is high. In this regard, it has been crucial for companies to understand and identify potential convergence patterns as early as possible to make timely strategic plans. This study proposes a new semantic method by showing how a graph convolutional network model can be used to monitor technology convergence. In particular, the model is trained to generate patents and technology keyword vectors from which new indicators are derived. We validate these new indicators and show that the proposed method outperforms existing studies using information regarding cross-citations and co-occurrence of international patent classification classes. Furthermore, we presented the usefulness of the proposed method to monitor technology convergence using a case study of the convergence between artificial intelligence (AI) and distributed ledger technology (DLT). The results show that convergence between AI and DLT is driven mainly by employing AI for DLT, and the role of each keyword (sub-domain) in the convergence process is also presented.