Abstract
Patent claims usually embody the core technological scope and the most essential terms to define the protection of an invention, which makes them the ideal resource for patent topic identification and theme changes analysis. However, conducting content analysis manually on massive technical terms is very time-consuming and laborious. Even with the help of traditional text mining techniques, it is still difficult to model topic changes over time, because single keywords alone are usually too general or ambiguous to represent a concept. Moreover, term frequency that used to rank keywords cannot separate polysemous words that are actually describing a different concept. To address this issue, this research proposes a topic change identification approach based on latent dirichlet allocation, to model and analyze topic changes and topic-based trend with minimal human intervention. After textual data cleaning, underlying semantic topics hidden in large archives of patent claims are revealed automatically. Topics are defined by probability distributions over words instead of terms and their frequency, so that polysemy is allowed. A case study using patents published in the United States Patent and Trademark Office (USPTO) from 2009 to 2013 with Australia as their assignee country is presented, to demonstrate the validity of the proposed topic change identification approach. The experimental result shows that the proposed approach can be used as an automatic tool to provide machine-identified topic changes for more efficient and effective R&D management assistance.
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Notes
- 1.
Data accessed in March 2014.
- 2.
All plant patents are seen as having one same USPC for calculation convenience.
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Acknowledgments
The work presented in this paper is partly supported by the Australian Research Council (ARC) under Discovery Project DP140101366 and the National High Technology Research and Development Program of China (Grant No. 2014AA015105).
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Chen, H., Zhang, Y., Zhu, D. (2016). Identifying Technological Topic Changes in Patent Claims Using Topic Modeling. In: Daim, T., Chiavetta, D., Porter, A., Saritas, O. (eds) Anticipating Future Innovation Pathways Through Large Data Analysis. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-39056-7_11
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