Patent evaluation methodologies reveal hidden information that enables business decisions. Many of these methodologies may be grouped into a prior art citation dependent category.
The problem with this category is citation noise. Citation noise obscures an evaluation leading to very limited and erroneous results. Citation noise is also a gap not well understood by scholars.
The empirical multi-case explanatory approach of this research examined 719 citations and found 87% of the citations were noise and 13% had interdependence with a patent. This research further found that
interdependency between a citation and patent eliminates citation noise and identifies pertinent and dominant citations.
The theoretical implications are a new understanding of citation noise and dependence, a novel interdependency framework and noise pertinence and dominant citation constructs.
The practice implications are a novel unencumbered patent evaluation methodology where pertinent and dominant citations provide useful, meaningful evaluations and enable better stakeholder decision-making capabilities.