With an ever-increasing client interest in international patents, we wanted to explore how patent ranking for EP patents has been conducted and how those ranking systems can be integrated to allow comparison and ranking with US patents. Here are our findings.
“Finding the Best Patents – Comparative Patent Ranking Systems – Citations Still Matter.” Richardson et. al. IPWatchdog (August 2016), available here.
In the second part of our two part blog post series at IP Watchdog, we document our efforts to develop, and refine, a patent ranking system based on our unique database tracking over $7B of patents that companies are trying to sell, or have sold.
This part covers our analysis of number of claims, the usage of means claims, claim 1 word count, and family size. Also, this part shows how the different individual ranks are combined into a balanced total rank.
How do you find the highest quality patents reliably and efficiently? We share our methodology and have made the code and model available under a Creative Commons license. Here, we’ve identified five primary factors for consideration in patent ranking.
“Finding the Best Patents – Forward Citation Analysis Still Wins.” Oliver et. al. IPWatchdog (March 2016), available here.
We’ve identified five primary factors for consideration in patent ranking. In this article, we’ll analyze how independent claim count, claim 1 word count, and family size & international filings can help to eliminate less useful patents quickly and efficiently.
“High Value Patents: Does Family Size Matter When Looking for Better Patents?” Oliver et. al. IPWatchdog (March 2016), available here.
“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?
In our two part blog post series at IP Watchdog, we document our efforts to develop and refine a patent ranking system based on our unique database tracking over $7B of patents that companies are trying to sell, or have sold.
When analyzing a large patent portfolio it is often difficult to determine where to spend diligence dollars first. In order to direct us to the specific assets that are more likely to be interesting we developed an in house heuristic ranking system. It is based on static patent characteristics. The image above describes the specific component scores for each of five characteristics. We used this system to rank the assets in Intellectual Venture’s patent portfolio (IAM Magazine, Issue 66 July/August 2014).
These component scores are then summed to create a raw rank (max 100) and bucketed into groups of interest. High > 85, Medium > 75 and everything else is rated as Low. It should be noted that theses rankings are intended for the general grouping of patents by likelihood of value, not for direct patent-to-patent comparison. It is possible for a specific low ranking asset to be of more interest and value than a high ranking one.
We have used this method across projects for over five years and have found that when compared to both alternative (more complex) scoring systems and initial human review (< 1 min per asset) of 1000+ asset portfolios it yields similar, though not identical, groupings. Overall, this ranking method has given us a very high return while maintaining simplicity.
Determining whether a patent is a “good” asset can be tricky. With a patent’s value typically being realized almost ten years after its grant, how can we know the value of any newly-granted patent? We offer concrete metrics as to how to gauge patent value at each point of its life cycle.
“Imagine There’s No ‘Bad Patents’ – Patent Life Cycle Metrics.” Richardson. Strategic Intellectual Property Leadership Conference (SLIC) (April 2007), available here.