PatentSBERTa：一种基于深度NLP的专利距离和分类混合模型，使用增强的SBERT
PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
摘要：
这项研究提供了一种有效的方法，使用文本数据来计算专利（P2P）的技术相似性，并提出了一个混合框架，利用产生的P2P相似性的应用程序，如语义搜索和自动专利分类。我们基于专利声明使用Sentence-BERT（SBERT）创建嵌入。我们利用SBERT在创建嵌入距离度量方面的效率来映射大量专利数据中的P2P相似性。我们部署了一个简单的最近邻（KNN）模型，预测合作专利分类（CPC）的专利的基础上的类分配的K专利具有最高的P2P相似性的分类框架。因此，我们验证了P2P相似性捕获其技术特征的CPC重叠，并在同一时间证明了这种方法的实用性，自动专利分类的基础上的文本数据。此外，所提出的分类框架是简单的，结果易于解释和最终用户的评估。在样本外模型验证中，我们能够对1，492，294项专利在子类（663）水平上的所有指定CPC类别进行多标签预测，准确率为54%，F1得分> 66%，这表明我们的模型在基于文本的多标签和多类别专利分类方面优于当前最先进的技术。此外，我们还讨论了语义知识产权搜索，专利美化和技术智能的框架的适用性。最后，我们指出了未来的研究议程，利用多源专利嵌入，它们在应用程序中的适当性，以及通过创建领域专家策划的语义文本相似性（STS）基准数据集来改进和验证专利嵌入。
英文摘要：
This study provides an efficient approach for using text data to calculate patent-to-patent (p2p) technological similarity, and presents a hybrid framework for leveraging the resulting p2p similarity for applications such as semantic search and automated patent classification. We create embeddings using Sentence-BERT (SBERT) based on patent claims. We leverage SBERTs efficiency in creating embedding distance measures to map p2p similarity in large sets of patent data. We deploy our framework for classification with a simple Nearest Neighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of a patent based on the class assignment of the K patents with the highest p2p similarity. We thereby validate that the p2p similarity captures their technological features in terms of CPC overlap, and at the same demonstrate the usefulness of this approach for automatic patent classification based on text data. Furthermore, the presented classification framework is simple and the results easy to interpret and evaluate by end-users. In the out-of-sample model validation, we are able to perform a multi-label prediction of all assigned CPC classes on the subclass (663) level on 1,492,294 patents with an accuracy of 54% and F1 score > 66%, which suggests that our model outperforms the current state-of-the-art in text-based multi-label and multi-class patent classification. We furthermore discuss the applicability of the presented framework for semantic IP search, patent landscaping, and technology intelligence. We finally point towards a future research agenda for leveraging multi-source patent embeddings, their appropriateness across applications, as well as to improve and validate patent embeddings by creating domain-expert curated Semantic Textual Similarity (STS) benchmark datasets.
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