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The patent examination process includes a search of previous work to verifythat a patent application describes a novel invention. Patent examinersprimarily use keyword-based searches to uncover prior art. A critical part ofkeyword searching is query expansion, which is the process of includingalternate terms such as synonyms and other related words, since the sameconcepts are often described differently in the literature. Patent terminologyis often domain specific. By curating technology-specific corpora and trainingword embedding models based on these corpora, we are able to automaticallyidentify the most relevant expansions of a given word or phrase. We compare theperformance of several automated query expansion techniques against expertspecified expansions. Furthermore, we explore a novel mechanism to extractrelated terms not just based on one input term but several terms in conjunctionby computing their centroid and identifying the nearest neighbors to thiscentroid. Highly skilled patent examiners are often the best and most reliablesource of identifying related terms. By designing a user interface that allowsexaminers to interact with the word embedding suggestions, we are able to usethese interactions to power crowdsourced modes of related terms. Learning fromusers allows us to overcome several challenges such as identifying words thatare bleeding edge and have not been published in the corpus yet. This paperstudies the effectiveness of word embedding and crowdsourced models across 11disparate technical areas.

Query Expansion for Patent Searching using Word Embedding and Professional Crowdsourcing