Measuring the drafting alignment of patent documents using text mining

Davit Khachatryan and Brigitte Muehlmann

PLOS ONE, 2020, vol. 15, issue 7, 1-20

Abstract: How would an inventor, entrepreneur, investor, or patent examiner quantify the extent to which the inventive claims listed in a patent document align with patent specification? Since a specification that is poorly aligned with the inventive claims can render an invention unpatentable and can invalidate an already issued patent, an effective measure of alignment is necessary. We define a novel measure of drafting alignment using Latent Dirichlet Allocation (LDA). The measure is defined for each patent document by first identifying the latent topics underlying the claims and the specification, and then using the Hellinger distance to find the proximity between the topical coverages. We demonstrate the use of the novel measure for data processing patent documents related to cybersecurity. The properties of the proposed measure are further investigated using exploratory data analysis, and it is shown that generally alignment is positively associated with the prior patenting efforts as well as the tendency to include figures in a document.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0234618 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 34618&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0234618

DOI: 10.1371/journal.pone.0234618

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().