“We would like you to find the best patents in this pile of 50,000 candidates. Oh, and we need it done for $30K.” We hear requests like this so often we’ve built processes and tools to help us address them. Our team has over 60 years of experience developing, evaluating, monetizing, litigating, and licensing patents; we’d like to share some of our experience and methodology with you.
Let’s return to that pile of 50,000 patents – how can we find the highest quality patents reliably and efficiently?
We’ve identified five primary factors for consideration in patent ranking (in order of weighting):
- Forward citations (45%)
- Age of patent from priority date (19%)
- Independent claim count (adjusted by number of means claims) (14%)
- Claim 1 word count (12%)
- Family size and international filings (10%)
We were surprised to discover that forward citations dominate the analysis. We evaluated millions of patents – and consistently forward citations were the biggest predictor of a higher value patent. More on this below.
What Ranking Tools Can Do
When we start projects sorting through thousands of patents, we first meet with the client to define success. In other words, what does it mean for the client to be successful in a project like this? Finding the best patents? Eliminating the worst patents?
Ultimately, we want to find patents that meet the clients’ needs. This means quickly eliminating 95% of the patents that are less likely to fit those needs. For this, we built a tool and ranking system that helps us identify the patents that are both most and least likely to be useful. With this smaller pool, we can start human review looking for the patents the client seeks.
ROL Group’s 2016 Patent Ranking System
Where do you look for benchmarks of better patents? We believe that patents that are bought and sold better reflect patents more likely to meet business needs. We started with our database tracking over $7B of patents that companies are trying to sell, or have sold. We also looked at the characteristics of patents that had been litigated.
Our requirements for a ranking system:
- Ranking system must be fully transparent – all aspects and formulas available to both ourselves and our clients for review, discussion, and per-project adjustments. Most existing commercial systems hide their ranking systems and are therefore precluded
- Factors based in data and intuitively explained – each factor we use should be based in data but also intuitively explainable to clients
In developing the new ranking system, we began with our previous heuristics-based system and tested the existing factors and others against our patent deals database as well as data sets of litigated patents. The new ranking factors were determined based on simulations comparing different potential weights.
We found that forward citations (later patents that cite the subject patent) were the most significant factor in identifying patents that were likely to be purchased. In fact, the patents that were sold—or even highlighted by brokers, e.g. the representative patent—in a brokered patent package exhibited an even more extreme number of forward citations than litigated patents.
Why forward citations? Why not claim length or any number of other factors? We believe that forward citations are a proxy for industry-wide R&D investment in a technology area. With more investment, there are generally more products. With more products, there is a higher chance of infringement. Infringement drives value and most likely meets a client’s needs. Specifically, a purchase either eliminates the client’s own infringement or provides a tool to use against someone else).
Our analysis focused on looking at forward citation counts for four primary sets of patents: (i) a set of all issued patents from 2005-2014, (ii) a set of litigated patents from the same period, (iii) a set of patents from the brokered market that were sold from 2009-2014, and (iv) the representative patents from brokered patent packages. The results were striking; the sold (set iii) and representative (set iv; e.g. the patent highlighted by brokers in packages) patents had exponentially more forward citations than the broad set of issued patents (set i).
Because there was evidence of significantly higher forward citations in the set of litigated patents (set ii) compared to the broad set of issued patents (set i), we decided to use the forward citations counts deltas between litigated patents and issued patents to set our ranking metric.
Turning to the chart, the light green line shows the forward citation count by years from publication date for litigated patents (set ii). The dark green line shows the forward citation count by years from publication date for the broader set of issued US patents (set i). As is evident in the first three years, there is minimal difference between the two data sets, but then a clear gap emerges.
For patents more than three years from the publication date, we identified four regions for ranking adjustments:
- Region A: The patent being ranked massively exceeds the number of expected citations for a litigated patent (rank = 1)
- Region B: The patent being ranked has more citations than expected for a litigated patent, it is defined to be the same size as region C
- Region C: The patent being ranked has more citations than expected for a typical patent, but not more than a litigated patent
- Region D: The patent being ranked has fewer citations than expected for a typical patent
Age of Patent from Priority Date
We know that our clients are generally looking to purchase patents that are actively adopted and in use in industry, but also are looking for sufficient remaining life to get the benefit of their purchase. For example, if a client is buying for a potential dispute that has not yet materialized, at least five years of remaining life is generally desirable.
From our time at Rambus, we know that patents in the range of 8-12 years from priority had the highest probability of being valuable in licensing. There is, additionally, a wealth of academic research on the timing of litigation vs. remaining life of patents. See, e.g. Brian Love, “An Empirical Study of Patent Litigation Timing” Univ. of Penn. Law Review, Vol 161, p 1309 (2013). As well as work by Mark Lemley together with John Allison and David Schwartz, “Understanding the Realities of Modern Patent Litigation”, 92 Texas L. Rev. 1769 (2014). Additionally, as seen in our prior article on Intellectual Venture’s (IV’s) patent portfolio and our forthcoming article (IAM Magazine Issue 77), IV’s purchase windows overlap heavily with the ranges we model as well.
We used the information from those papers as well as our experience to model this factor:
TO BE CONTINUED: Up next I will discuss the remaining factors in some detail, and explain why they are somewhat less helpful in determining ranking priority.