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首页> 外文会议>International Conference on Social Networks Analysis, Management and Security >An Empirical Study on Patent Novelty Detection: A Novel Approach Using Machine Learning and Natural Language Processing
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An Empirical Study on Patent Novelty Detection: A Novel Approach Using Machine Learning and Natural Language Processing

机译：专利新奇检测的实证研究：一种利用机器学习和自然语言处理的新方法

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Patent, a form of intellectual property often be in the first place when it comes to securing an invention. The legal boundaries created then will become key stages of turning an invention into a commercial product. In recent years, the unprecedented growth of patent applications has induced a great challenge to patent examiners. Novelty detection is one major step considered before and after filing a patent application to assure claimed inventions are new and non-obvious. This itself is considered as a salient stage of prior art search by patent applicants, patent examiners, patent attorneys, patent agent professionals. Management in terms of critical analysis of such a large scale of documents has become a challenge since missing an optimal, effective, and efficient system. To this end, we come up with a novel experimental case study to foster highly recursive and interactive tasks. We developed and investigated more than 50 machine learning models on the considered dataset. The contributions of this work include: (1) outlined and anticipated the importance of novelty detection in the patent domain, (2) develop various baseline models for novelty detection, (3) utilize immense contributions of deep learning towards NLP to improve baseline models, (4) assess the performance of every model by using different word embeddings like word2vec, glove, fasttext, and domain-specific embeddings, (5) a novel application of NBSVM algorithm on our dataset, and considered as exceptionally good of our models. We articulated the fulfillment of models using training and validation curves to prove seemingly negligible overfit or no overfit, in the hope that effective automation in novelty detection helps in driving down the routine prior art search efforts.
机译：专利，在确保发明方面通常是首先是第一名的形式。所产生的法律边界将成为将发明转变为商业产品的关键阶段。近年来，专利申请前所未有的增长对专利审查员造成了巨大的挑战。新奇检测是在提交专利申请以确保所要求保护的发明之后考虑的一个主要步骤是新的和非明显的。这本身被专利申请人，专利审查员，专利代理专业人员所涉及现有技术搜索的突出阶段。由于缺少最佳，有效和高效的系统，因此管理在批判性分析方面已成为挑战。为此，我们提出了一种新的实验案例研究，促进高度递归和互动任务。我们开发并调查了在COMED DataSet上的50多种机器学习模型。这项工作的贡献包括：（1）概述并预测了专利域内新奇检测的重要性，（2）开发新颖性检测的各种基线模型，（3）利用深度学习对NLP的巨大贡献来改善基线模型， （4）通过使用Word2VEC，手套，FastText和域特定于域的不同单词嵌入式来评估每个模型的性能，（5）对我们数据集的NBSVM算法的新建应用程序，并被视为我们的模型非常好。我们阐述了使用培训和验证曲线的模型实现似乎可以忽略不计的措施或没有过度装备，希望有效的自动化在新奇检测中有助于驾驶常规现有技术搜索努力。

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