User Interface for Managing and Refining Related Patent Terms View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-06-09

AUTHORS

Girish Showkatramani , Arthi Krishna , Ye Jin , Aaron Pepe , Naresh Nula , Greg Gabel

ABSTRACT

One of the crucial aspects of the patent examination process is assessing the patentability of an invention by performing extensive keyword-based searches to identify related existing inventions (or lack thereof). The expertise of identifying the most effective keywords is a critical skill and time-intensive step in the examination process. Recently, word embedding [1] techniques have demonstrated value in identifying related words. In word embedding, the vector representation of an individual word is computed based on its context, and so words with similar meaning exhibit similar vector representation. Using a number of alternate data sources and word embedding techniques we are able to generate a variety of word embedding models. For example, we initially clustered patent data based on the different areas of interests such as Computer Architecture or Biology, and used this data to train Word2Vec [2] and fastText [3] models. Even though the generated word embedding models were reliable and scalable, none of the models by itself was sophisticated enough to match an experts choice of keywords. In this study, we have developed a user interface (Fig. 1) that allows domain experts to quickly evaluate several word embedding models and curate a more sophisticated set of related patent terms by combining results from several models or in some cases even augmenting to them by hand. Our application thereby seeks to provide a functional and usable centralized interface towards searching and identifying related terms in the patent domain.Fig. 1.Related patent terms. Related patent terms. More... »

PAGES

115-120

Book

TITLE

HCI International 2018 – Posters' Extended Abstracts

ISBN

978-3-319-92269-0
978-3-319-92270-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-92270-6_16

DOI

http://dx.doi.org/10.1007/978-3-319-92270-6_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104475492


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