Abstract
Deep learning can be used to forecast emerging technologies based on patent data. However, it requires a large amount of labeled patent data as a training set, which is difficult to obtain due to various constraints. This study proposes a novel approach that integrates data augmentation and deep learning methods, which overcome the problem of lacking training samples when applying deep learning to forecast emerging technologies. First, a sample data set was constructed using Gartner’s hype cycle and multiple patent features. Second, a generative adversarial network was used to generate many synthetic samples (data augmentation) to expand the scale of the sample data set. Finally, a deep neural network classifier was trained with the augmented data set to forecast emerging technologies, and it could predict up to 77% of the emerging technologies in a given year with high precision. This approach was used to forecast emerging technologies in Gartner’s hype cycles for 2017 based on patent data from 2000 to 2016. Four out of six of the emerging technologies were forecasted correctly, showing the accuracy and precision of the proposed approach. This approach enables deep learning to forecast emerging technologies with limited training samples.
Highlights
Forecasting emerging technologies is important for governments and enterprises to identify strategic opportunities in the face of technological change
Hassan et al (2018) compared deep learning and the classical statistical supervised learning models for classifying the importance of a citation using the same dataset, and the results showed that deep learning with all 64 features had a higher accuracy than Support Vector Machines (SVMs) and Random Forests (RFs) using the 29 best features
The forecasting results for the emerging technology (ET) samples in 2017 showed that our method based on generative adversarial network (GAN) and deep neural network (DNN) could forecast emerging technologies 1 year before they emerged with high precision and few samples
Summary
Forecasting emerging technologies is important for governments and enterprises to identify strategic opportunities in the face of technological change. Chang and Breitzman (2009) used the clustering of patents to identify emerging and high-impact technology clusters and trends; Chiavetta and Porter (2013) proposed the basic idea of tech mining to forecast emerging technologies using text and data mining, Choi and Jun (2014) developed a Bayesian model for patent clustering to forecast emerging technologies; Breitzman and Thomas (2015b) proposed the “Emerging Clusters Model” based on patent citations to identify emerging technologies across multiple patent systems; and Zhou et al (2019a, b) developed a framework through citation network and topology clustering to reveal the convergence process of scientific knowledge to forecast emerging technologies All these approaches utilized unsupervised learning to probe valuable text-based data. Unsupervised learning methods cannot incorporate external domain knowledge during the machine learning process, and the results need to be professionally interpreted by domain experts that are usually rare, costly, and sometimes biased
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