通过全文相似性搜索自动搜索专利的现有技术
Automating the search for a patent's prior art with a full text similarity search
摘要：
技术发明比以往任何时候都更是我们社会进步的象征。专利保证其创造者免受侵权。一项发明若要获得专利，必须评估其新颖性和创造性。因此，需要对描述与给定专利申请相似的发明的已发表作品进行检索。目前，这种所谓的对现有技术的搜索是用半自动组合的关键字查询来执行的，这不仅耗时，而且容易出错。特别是，错误可能会系统地出现的事实，不同的关键字相同的技术概念可能存在跨学科。在本文中，提出了一种新的方法，其中给定专利申请的全文与现有专利进行比较，使用机器学习和自然语言处理技术来自动检测与提交的文档中描述的发明类似的发明。各种国家的最先进的特征提取和文件比较的方法进行评估。除此之外，当前搜索过程的质量是根据领域专家的评级进行评估的。评估结果表明，我们的自动化方法，除了加速搜索过程，也提高了搜索结果的现有技术方面的质量。
英文摘要：
More than ever, technical inventions are the symbol of our society's advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
