On Term Selection Techniques for Patent Prior Art Search

Abstract

In this paper, we investigate the influence of term selection on retrieval performance on the CLEF-IP prior art test collection, using the Description section of the patent query with Language Model (LM) and BM25 scoring functions. We find that an oracular relevance feedback system that extracts terms from the judged relevant documents far outperforms the baseline and performs twice as well on MAP as the best competitor in CLEF-IP 2010. We find a very clear term selection value threshold for use when choosing terms. We also noticed that most of the useful feedback terms are actually present in the original query and hypothesized that the baseline system could be substantially improved by removing negative query terms. We tried four simple automated approaches to identify negative terms for query reduction but we were unable to notably improve on the baseline performance with any of them. However, we show that a simple, minimal interactive relevance feedback approach where terms are selected from only the first retrieved relevant document outperforms the best result from CLEF-IP 2010 suggesting the promise of interactive methods for term selection in patent prior art search.

Automating the search for a patent’s prior art with a full text similarity search

“…A potential drawback of such an approach, however, is that the thesaurus itself has to be manually curated and extended [72]. Another line of research focuses on pseudo-relevance feedback, where, given an initial search, the first k search results are used to identify additional keywords that can be used to extend the original query [18,19,38]. Similarly, past queries [62] or meta data such as citations can be used to augment the search query [17,39,40].…”

“…Current search approaches for prior art therefore require a significant amount of manual work and time, as given a patent application, the patent officer or attorney has to manually formulate a search query by combining words that should match documents describing similar inventions [5]. Furthermore, these queries often have to be adapted several times to optimize the output of the search [19,66]. A main problem here is that regular keyword searches do not inherently take into account synonyms or more abstract terms related to the given query words.…”

Multi-field query expansion is effective for biomedical dataset retrieval

“…They have shown that while query reduction techniques have a mitigated impact on mid-length queries, they are very effective on long queries such as an extended abstract or a description. Also, in ( 42 ), authors have shown that a simple and minimal interactive relevance feedback approach outperforms the best result from the CLEF-IP 2010 challenge ( 43 ), which was a sophisticated and very advanced system that utilized a very important feature of patents. This suggested the promise of interactive methods for term selection in patent prior art search.…”