document: COA : finding novel patents through text analysis In recent years , the number of patents filed by the business enterprises in the technology industry are growing rapidly , thus providing unprecedented opportunities for knowledge discovery in patent data . One important task in this regard is to employ data mining techniques to rank patents in terms of their potential to earn money through licensing . Availability of such ranking can substantially reduce enterprise IP ( Intellectual Property ) management costs . Unfortunately , the existing software systems in the IP domain do not address this task directly . Through our research , we build a patent ranking software , named COA ( Claim Originality Analysis ) that rates a patent based on its value by measuring the recency and the impact of the important phrases that appear in the `` claims '' section of a patent . Experiments show that COA produces meaningful ranking when comparing it with other indirect patent evaluation metrics -- citation count , patent status , and attorney 's rating . In reallife settings , this tool was used by beta-testers in the IBM IP department . Lawyers found it very useful in patent rating , specifically , in highlighting potentially valuable patents in a patent cluster . In this article , we describe the ranking techniques and system architecture of COA . We also present the results that validate its effectiveness .
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