Abstract:Patent Landscaping, one of the central tasks of intellectual property management, includes selecting and grouping patents according to userdefined technical or application-oriented criteria. While recent transformer-based models have been shown to be effective for classifying patents into taxonomies such as CPC or IPC, there is yet little research on how to support real-world Patent Landscape Studies (PLSs) using natural language processing methods. With this paper, we release three labeled datasets for PLS-or… Show more
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