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.
Highlights
The receipt of the patent application at the patenting office kicks off the patent prosecution, which is the process as part of which one or more patent examiners attempt to determine the patentability of the invention outlined in the application
The Manual of Patent Examining Procedure (MPEP) states that “The contents of an application, to be complete, must include a specification containing a written description of the invention . . . The example(s) and description should be of sufficient scope as to justify the scope of the claims.” [1]
Given the absence of premid-2008 data, we only focused on the subset of our focal data that were filed to the USPTO on or after January 19, 2006
Summary
The receipt of the patent application at the patenting office kicks off the patent prosecution, which is the process as part of which one or more patent examiners attempt to determine the patentability of the invention outlined in the application. Note that LDA is fit only once (based on M), after which for any given document the most likely topic is assigned to each term in claims and specification, respectively. This assignment is carried out by approximating p(zi|wd) using the variational posterior multinomial parameters di 1⁄4 ðdi; di2; . Because the novel measure of alignment is based on Hellinger distances between frequency distributions, high alignment corresponds to the claims and specification sections closely resembling each other in terms of the frequency of topics appearing in each.
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