Wednesday, April 10, 2019

Prior Art Searching: Reviewing the Landscape

In this blog post series, you have learned a litany of techniques for conducting an effective and efficient preliminary search of the prior art.  In this final post, you will see how to take a step back and conduct an assessment of the prior art landscape, which may ultimately prove crucial to the initial stages of brainstorming.  However, in contrast to the prior art searching described in the last few posts, landscape reviews are not focused on individual publications.  Rather, they contextualize larger datasets composed of several hundred, or thousand, publications.
There are several dimensions by which you may gain a broad understanding of the prior art landscape pertaining to your invention, such as publication trends over time, or applicants, jurisdictions, CPC subclasses, etc. with the most publications.  Due to the particular utility of the interface provided by The Lens patent search engine in regards to landscape reviews, all examples in this post will derive from there.
After providing The Lens with the original search query from the previous posts in this series, (electric) (unicycle), you can utilize the dropdown tabs on the left side of the results page to better understand the larger trends.
As a first example, clicking on the “Applicants” tab reveals the top ten most prolific applicants in the dataset, along with the total number of publications for each applicant.  In this case, two companies, Google and Waymo, dominate the landscape, having published 381 (~30%) of the 1273 publications found.
As a second example, clicking on the “Jurisdictions” tab will similarly bring up the top jurisdictions.  The US overwhelmingly dominates in this example, where 916 (~72%) of the 1273 publications found were published.
As a third example, on the right side of the results page, a plot titled “Publications by Year” is automatically generated, where you may view publication trends over time.  Hovering over a given data point will generate an inset with further information (as shown below for the year 2017).
Because of the drastic increase in filed patent applications in recent years, it is often more useful to restrict results to the last few years.  This also allows you to determine which companies are actively pursuing patents.  To restrict the publication date to the last ten years, select the “Date Range” tab, and fill in the appropriate fields.

Filtering subsets of results by CPC subclass can further help you see which inventions are being developed by which companies, or what space the most recent publications cover.  For example, selecting “Google Inc” from the “Applicants” tab will return those 242 publications filed by Google.

Then by clicking on the “Classifications” tab, you can view the top CPC subclasses for the results.  A plurality of publications (83) filed by Google are classified under G05D1/0088.

By hovering over a CPC subclass of interest, a yellow info icon will appear (as shown above, to the right of G05D1/0088).  Selecting this will generate an inset with information concerning the given CPC subclass.  In this case, you can determine that G05D1/088 is directed to inventions using artificial intelligence in automatic navigation systems.

Finally, for pursuing more extensive, tailored analyses, an “Export Results” option is provided near the top of the results page.

One last word of caution: there are numerous technical and legal pitfalls which fall outside of the scope of this blog post series and are left for an experienced attorney or agent.  These search techniques are merely provided to facilitate the brainstorming and preparation of your disclosure.  Always remember to rely on your legal counsel for the last word on substantive reviews of the prior art.


Note that the views expressed herein do not represent the views of any law firm or client, and may not even represent the views of the author. This blog is NOT legal advice and is for informational purposes only. No attorney client relationship can be formed by reading this blog or using any of the information provided. The accuracy of the information provided has not been verified.