Retrieving patents with inverse patent category frequency

Patent has currently been captured strong attention as a key enabler for the knowledge and information centric companies and institutes. The higher the patent capability required, the more important an effective and efficient patent retrieval system needed. The conventional patent retrieval systems, however, have produced unsatisfactory results for the patent queries, since the inherent search systems would have come from the traditional keyword based models so that it has been inevitable to result in too many unrelated items. This has made the patent experts keep spending a lot of time to refine the results manually. We propose two dynamic ranking algorithms specialized patent-searching method, in which the dynamic interactive retrieval can be achieved. In the real USPTO dataset experiment, the dynamic ranking method shows substantial improvements with respect to time and cost over conventional static ranking approaches.