Institutional Success IndeX (ISIX) is a internally developed measurement of PTAB success by a law firm.   The index permits anyone to easily measure how successful a law firm has historically been at being instituted and successfully invalidating at least 1 claim of a challenged patent.   It measures any law firm with more than 50 institution decision to ensure there are enough results.   The formula is: (A) +1 point for any successful institution (excluding joinder), (B) -1 point for all non-institutions (on the merits), and (C) -2 points for all Final Written Decisions (FWD) in which all challenged claims are found patentable.  The resulting score is divided by the total number of institution decisions of (A + B).  Generally speaking, the higher the ISIX score for a petitioner counsel the more successful they have been.  The lower the ISIX score for a patent owner counsel, the more successful they have been.




Advanced Patent IndeX (APIX) is an internally developed tool to measure patent validity.  APIX uses a machine learning algorithm to rate the chances the PTAB may institute on a patent based on its similarity to all past institution decisions.  Patents are ranked from D (least likely) to AA (most likely) to survive a PTAB challenge.   The most important result from APIX was that PTAB has instituted more often on patents from examiners and art units with a higher allowance rate.  For more information on the complete variables results contact Unified Patents.


CITation IndeX (CITX) is an internally developed tool to measure patent value.  Studies have found market value is strongly correlated to Forward Citations.  Every patent issued from the same year is indexed based on Forward Citations measured against a normal curve.  Ranking of D (lowest value) to AA (highest value) is then determined by how many standard deviations a patent is relative to the mean (similar to APIX).


BRoadness IndeX (BRIX) uses the uniqueness of claim language in a Cooperative Patent Classification (CPC) to ascertain patent broadness or narrowness.  It is based on 2 assumptions:


1. Claims with fewer terms relative to their CPC patent class tend to be broader.

2. Claims with fewer unique terms relative to their CPC patent class tend to be broader.


Every claim is given a separate BRIX score of between 0 to 100 based on a vector of number of terms and uniqueness of each term.  The higher the score, the broader the claim relative to other claims in that class.


All the claims are then combined in a patent to create an aggregate BRIX score for that patent which is graded in quartiles based on their overall broadness.  A patent with a green ranking is the broadest relative to that CPC patent class while a red is the narrowest.

APIX and CITX RATINGS explained

A raw score is generated for all US patents.  Patents are then rated based on how many standard deviations they are from the mean.  For example:  an "A" APIX score means that patent is in the top 16% of all patents for validity.  But given the institution rate, an "A" APIX score means  it has an almost 50% chance of being instituted on if challenged.


Perc. of
Total Patents






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3 unique aspects about APIX

1. Proprietary PTAB training set.  No other training set provides so many decisions on validity.   PTAB institution decisions are clear determinations by domain area experts.  The high volume and the over 90% of which are in litigation provides the only available large data set on validity. Therefore, there is a strong interest in a successful PTAB challenge due to high cost and a negative result can strengthen a patent.


2. More patent data than previously used.   The roughly 30 variables include not only post-issuance patent attributes such as citations, family size, and words, but also pre-issuance patent prosecution data such as examiner data and office actions which has not typically been used in tools.  The combination provides a much higher accuracy than ever before.


3. Machine learning algorithm (AI) that provides much higher precision than previous tools.   AI uses thousands of computations which can vary based on key initial variables and provides much higher accuracy available through linear regressions.   This enables APIX to provide a much higher level of significance and handle many more variables than before.