Generating Tailored Classification Schemas for German Patents

Patents and patent applications are important parts of a company’s intellectual property. Thus, companies put a lot of effort in designing and maintaining an internal structure for organizing their own patent portfolios, but also in keeping track of competitor’s patent portfolios. Yet, official classification schemas offered by patent offices (i) are often too coarse and (ii) are not mappable, for instance, to a company’s functions, applications, or divisions. In this work, we present a first step towards generating tailored classification. To automate the generation process, we apply key term extraction and topic modelling algorithms to 2.131 publications of German patent applications. To infer categories, we apply topic modelling to the patent collection. We evaluate the mapping of the topics found via the Latent Dirichlet Allocation method to the classes present in the patent collection as assigned by the domain expert.