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.

There have been recent attempts to identify emerging technologies by using topic-based analysis, but many of them have methodological deficiencies. First, analyses are unsupervised, and unsupervised methods cannot incorporate supervised knowledge that is needed to better identify technological domains. Second, those methods lack semantic interpretation, as many of them still remain at word-level analyses, we developed a novel technology-identification method that uses a semi-supervised topic clustering model (Labeled Dirichlet Multi Mixture model) to integrate technological domain knowledge. The model also generates a sentence-level semantic technological topic description through the topic description method (Various-aspects Sentence-level Description) on information extraction. We used this novel method to analyze the technology of the 3D printing industry, and successfully identified emerging technologies by differentiating new topics from the traditional topics, the results effectively demonstrated the semantic technological topic description by showing sentences. This method could be of great interest to technology forecasters and relevant policy-makers.