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首页> 外文会议>2014 International Conference on Data Science and Advanced Analytics >Recommending missing citations for newly granted patents
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Recommending missing citations for newly granted patents

机译：推荐缺少新授予专利的引文

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摘要

The U.S. recently adopted a post-grant opposition procedure to encourage third parties to challenge the validity of newly granted patents by providing relevant prior patents that are missed during patent examination (i.e., missing citations). In this paper, we propose a recommendation system for missing citations for newly granted patents. The recommendation system, based on the patent citation network of a newly granted query patent, focuses on paths that start with the references of the query patent in the network. Our approach is to identify the relevancy of a candidate patent to the query patent by its citation relationship (paths) that are distinguished based on the direction, topology and semantics of the paths in the network. We consider six different types of paths between a candidate patent and a query patent based on their citation relationship and define a relevancy score for each path type. Accordingly, we rank candidate patents via a RankSVM model learned by using those relevancy scores as features. The experimental results show our approach significantly improves the average precision and recall performance compared to two baseline methods, i.e., Katz distance and text similarity.
机译：美国最近采用了授权后异议程序，以鼓励第三方通过提供在专利审查过程中遗漏的相关在先专利（即缺少引文）来质疑新授予的专利的有效性。在本文中，我们提出了针对新授予的专利缺少引文的推荐系统。基于新授予的查询专利的专利引用网络的推荐系统，重点关注以网络中查询专利的引用开头的路径。我们的方法是通过引用关系（路径）来识别候选专利与查询专利的相关性，这些引用关系基于网络中路径的方向，拓扑和语义进行区分。我们根据引文关系考虑候选专利和查询专利之间的六种不同类型的路径，并为每种路径类型定义相关性得分。因此，我们通过使用相关分数作为特征学习的RankSVM模型对候选专利进行排名。实验结果表明，与两种基准方法（即Katz距离和文本相似度）相比，我们的方法显着提高了平均精度和召回性能。

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