Patents are critically important for a company to protect its core business concepts and proprietary technologies. Effective patent mining in massive patent databases not only provides business enterprises with valuab...Patents are critically important for a company to protect its core business concepts and proprietary technologies. Effective patent mining in massive patent databases not only provides business enterprises with valuable insights to develop strategies for research and development, intellectual property management, and product marketing, but also helps patent offices to improve efficiency and optimize their patent examination processes. This paper describes the patent mining problem of automatically discovering core patents （i.e., novel and influential patents in a domain）. In addition, the value of core patent mining is illustrated by revealing the potential competitive relationships among companies in their core patents. The work addresses the unique patent vocabulary usage which is not considered in traditional word-based statistical methods with a topic-based temporal mining approach that quantifies a patent＇s novelty and influence through topic activeness variations. Tests of this method on real-world patent portfolios show the effectiveness of this approach over state-of-the-art methods.展开更多
Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R＆D knowledge, reduces new product development tim...Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R＆D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R＆D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification （IPC） codes B25C, B25D, B25F （categories for power hand tools） and B24B, C09G and H011 （categories for chemical mechanical polishing） are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.展开更多
基金the National Natural Science Foundation of China (No. 61272227)the National Key Technology Research and Development Program(No. 20121857860)the Tsinghua University Initiative Scientific Research Program (No. 20121088071)
文摘Patents are critically important for a company to protect its core business concepts and proprietary technologies. Effective patent mining in massive patent databases not only provides business enterprises with valuable insights to develop strategies for research and development, intellectual property management, and product marketing, but also helps patent offices to improve efficiency and optimize their patent examination processes. This paper describes the patent mining problem of automatically discovering core patents （i.e., novel and influential patents in a domain）. In addition, the value of core patent mining is illustrated by revealing the potential competitive relationships among companies in their core patents. The work addresses the unique patent vocabulary usage which is not considered in traditional word-based statistical methods with a topic-based temporal mining approach that quantifies a patent＇s novelty and influence through topic activeness variations. Tests of this method on real-world patent portfolios show the effectiveness of this approach over state-of-the-art methods.
基金supported by National Science Council research grants
文摘Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R＆D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R＆D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification （IPC） codes B25C, B25D, B25F （categories for power hand tools） and B24B, C09G and H011 （categories for chemical mechanical polishing） are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.
