How anyone can win an argument through analogy using PTAB big data

A distinguishing feature of a good patent practitioner is his or her ability to analogize or distinguish. But the usefulness of such analogizing/distinguishing is not limited to patent prosecutors. In fact, patent litigators are increasingly using ex parte appeals data in court with the same approach. 

First some context. Patent practitioners have long used a technique of analogizing to rules and case law to bolster their position. For example, a patent lawyer can use the universe of MPEP, case law or other USPTO guidance to point out that an examiner’s rejection is improper. A problem with this is that there is only finite hours to spend to sift through the thousands of pages of such resources for such analogizing information. This is where help from big data comes in. 

Ex parte PTAB appeals data can help patent practitioners analogize to overcome a particular rejection. With Anticipat Research, a savvy practitioner can simply input an examiner name, art unit or technology class of interest, along with the rejection type, filter for all reversals, and then quickly distill which argumentation and case law the Board found persuasive in overturning an application’s current rejection. 




Board panels often analogize favorable case law and distinguish unfavorable case law, specific to the present claims. With the structure presented, the practitioner can piggy-back off of this work.

Of course, just as reversals can be helpful for those seeking to overturn a particular rejection, so can affirmances be used on the other side of patent prosecution. An examiner can use Anticipat to easily find supporting argumentation and case law that supports his or her position by locating similar decisions where the PTAB affirmed a particular rejection. 

Analogizing using this kind of patent prosecution data (ex parte appeals) predictably flows for patent prosecution responses (Office Action responses, appeal briefs) because both the data and the responses are integrally rooted in patent prosecution. But the use case can extend in an even less intuitive way: to patent litigation. 

For patents that some say should never have been granted in the first place, a patent litigator can use ex parte appeals affirmance data to quickly find argumentation and case law for invalidating a patent. Take a broadly claimed software patent allowed in an era pre-Alice. Actually, take one of the Alice patents that the Supreme Court held was invalid. 

aliceAny issued patent (including the above patent) will have an examiner, an art unit, and a technology class. One can simply input any of that patent’s metadata (e.g., technology class, examiner, art unit, technology center) into the Anticipat engine and filter for affirmances and you instantly have very recently decided cases where the Board applied relevant case law for this particular technology.


So rather than keeping current on all Section 101 case law across all technologies (GUIs, business methods, algorithms, diagnostics, etc.), a practitioner can simply use effectively proven outcomes in related applications. Some discretion will be needed to see whether the Board decision claims are similar to the claims of interest (to be analogized to), but Anticipat gets you close to the needed resources at your disposal.

Big patent data is incredibly useful in finding the best and most persuasive course of action to advance your particular case. This applies to the patent prosecutor, the examiner and the patent litigator. Sign up for a 14-day free trial and see for yourself. 


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