Name:
Publisher version
View Source
Access full-text PDFOpen Access
View Source
Check access options
Check access options
Abstract
Patents are key documents to support the commercial exploitation of inventions. Patent documents must claim inventiveness, industrial application, and novelty to be granted and may use citations and URLs to support these claims as well as to explain their ideas. Although there is much research into the citations used to support inventions, almost nothing is known about the cited URLs. This may hinder inventors and evaluators from deciding which URLs are appropriate. To investigate this issue, all 3,133,247 patents granted by the United States Patent and Trademark Office (USPTO) from 2008 to 2018 were investigated, and 2,719,705 URLs (patent outlinks) were automatically extracted using heuristics, and analyzed using link analyses techniques. A minority of patents included URLs (17.1%), with the percentage increasing over time. The inclusion of URLs differs between disciplines, with Physics (especially the subcategory Computation) having the most URLs per patent. Patents are generally embedded in the “other citations” patent section (referring to academic publications) and the “description” section (e.g., supplementary information and definitions). Online content-oriented resources (e.g., Wayback Machine, Wikipedia, YouTube), academic bibliographic databases (e.g., IEEE Xplore, Microsoft Academic, PubMed, CiteSeerX) and technological companies (e.g., IBM, Amazon, Microsoft) are often linked from USPTO patents. These findings show the broad roles that URLs can play when supporting a patent claim. Finally, in order to avoid bad practices found in the inclusion of URLs in patents, a list of recommendations to cite online resources from patents is provided.Citation
Font-Julián, C.I., Ontalba-Ruipérez, J., Orduña-Malea, E. and Thelwall, M. (2022) Which types of online resource support US patent claims? Journal of Informetrics, 16(1), Article No. 101247.Publisher
ElsevierJournal
Journal of InformetricsAdditional Links
https://www.sciencedirect.com/science/article/pii/S1751157721001188Type
Journal articleLanguage
enDescription
This is an accepted manuscript of an article published by Elsevier in Journal of Informetrics on 05/01/2022. Available online: https://doi.org/10.1016/j.joi.2021.101247 The accepted version of the publication may differ from the final published version.ISSN
1751-1577ae974a485f413a2113503eed53cd6c53
10.1016/j.joi.2021.101247
Scopus Count
Collections
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/