Transformer-Based Patent Novelty Search by Training Claims to Their Own Description

In this paper we present a method to concatenate patent claims to their own description. By applying this method, bidirectional encoder representations from transformers (BERT) train suitable descriptions for claims. Such a trained BERT could be able to identify novelty relevant descriptions for patents. In addition, we introduce a new scoring scheme: relevance score or novelty score to interprete the output of BERT. We test the method on patent applications by training BERT on the first claims of patents and corresponding descriptions. The output is processed according to the relevance score and the results compared with the cited X documents in the search reports. The test shows that BERT score some of the cited X documents as highly relevant

Introducing DynaPTI–constructing a dynamic patent technology indicator using text mining and machine learning

Patent data is an established source of information for both scientific research and corporate intelligence. Yet, most patent-based technology indicators fail to consider firm-level dynamics regarding their technological quality and technological activity. Accordingly, these indicators are unlikely to deliver an unbiased view on the current state of firm-level innovation and are thus incomplete tools for researchers and corporate intelligence practitioners. In this paper, we develop DynaPTI, an indicator that tackles this particular shortcoming of existing patent-based measures. Our proposed framework extends the literature by incorporating a dynamic component and is built upon an index-based comparison of firms. Furthermore, we use machine-learning techniques to enrich our indicator with textual information from patent texts. Together, these features allow our proposed framework to provide precise and up-to-date assessments about firm-level innovation activities. To present an exemplary implementation of the framework, we provide an empirical application to companies from the wind energy sector and compare our results to alternatives. Our corresponding findings suggest that our approach can generate valuable insights that are complementary to existing approaches, particularly regarding the identification of recently emerging, innovation-overperformers in a particular technological field