Evaluation of prior art search methods for Chinese patents

patent or currently involving in a patentdecades in various countries. Patent offices of these areas have planned or are about to recruit more patent examiners for dealing with the ever-increasing applications. Before issuing a patent, the examiners need to conduct a prior art search in order to know whether the techniques revealed in the application meet the novelty requirement for a patent. On the other hand, any individual or enterprise, before applying a patent or currently involving in a patent lawsuit case, will also inevitably conduct a prior art search to make sure they will not infringe other's patent rights. Therefore, patent prior art search is a highstake task. Several research activities have been conducted in other countries. However, none has done for traditional Chinese patents. Based on the real-world patent collection, this study compares five models for prior art search, namely manual Boolean search, automatic search, pseudo relevance feedback, true relevance feedback, and human-machine interaction. Evaluated on 24 prior art search items, fully automatic pseudo relevance feedback was found to be able to achieve the effectiveness level of semi-automatic true relevance feedback. In addition, the human-machine interaction based on various terms suggested by the retrieval system performed un-expected low, even worse than the fully automatic models. However, this shows that the automatic retrieval techniques have reached a level that is able to help novice users in promoting their prior art search performance.