  Understanding the topical evolution in industrial innovation is a challenging
problem. With the advancement in the digital repositories in the form of patent
documents, it is becoming increasingly more feasible to understand the
innovation secrets -- "catchphrases" of organizations. However, searching and
understanding this enormous textual information is a natural bottleneck. In
this paper, we propose an unsupervised method for the extraction of
catchphrases from the abstracts of patents granted by the U.S. Patent and
Trademark Office over the years. Our proposed system achieves substantial
improvement, both in terms of precision and recall, against state-of-the-art
techniques. As a second objective, we conduct an extensive empirical study to
understand the temporal evolution of the catchphrases across various
organizations. We also show how the overall innovation evolution in the form of
introduction of newer catchphrases in an organization's patents correlates with
the future citations received by the patents filed by that organization. Our
code and data sets will be placed in the public domain soon.
