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• W3115750078 abstract "Recent research applies patent autoclassification using machine learning to map the technological landscape of an industry value chain. However, when these methods are applied to emerging industries, the available patent sample data are small-scale and unevenly distributed, which cause overfitting and reduce the accuracy of patent classification. Therefore, this article proposes a framework to map the technological landscape of an emerging industry value chain through patent analysis with deep learning, which integrates a generative adversarial network as a data-augmentation method to overcome the problem of low-quality emerging-industry patent samples, and a deep neural network as a patent classifier. Based on this framework, this article conducts an application case of the 3-D printing industry. The evaluation results show that the integrated framework can effectively classify the patents with small-scale and unevenly distributed sample data, and depict the technological landscape of an emerging industry value chain. This article develops an efficient, reliable framework for patent autoclassification of emerging industries to overcome the lack of high-quality training samples, and it sheds light on the emerging industry value chain analysis with deep learning." @default.
• W3115750078 created "2021-01-05" @default.
• W3115750078 creator A5002990257 @default.
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• W3115750078 date "2022-12-01" @default.
• W3115750078 modified "2023-10-16" @default.
• W3115750078 title "Mapping the Technological Landscape of Emerging Industry Value Chain Through a Patent Lens: An Integrated Framework With Deep Learning" @default.
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• W3115750078 doi "https://doi.org/10.1109/tem.2020.3041933" @default.
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