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Correlating Patents with Litigation Data to Determine Legal Risk

by Ryan Rozich on October 01, 2008

Rather than looking at different information sources in isolation, one of our guiding philosophies is that the answers to many business level questions are found at the intersection of different types of data. So what does this mean from the perspective of someone looking to do real-world analysis? Let’s use correlated information to examine the litigation risk involved in a certain technology landscape.

Imagine we are a company that either (a) makes tooth whitening products, (b) is looking to start a new business unit in this area or (c) is considering acquiring another company in this area. One thing that we may want to consider is the risk of IP litigation – in which technology areas and from which companies. Simply searching patent data will tell us how many patents are in this area and possibly who owns them. Searching litigation cases is not much help without putting those cases in the context of which patents or technology areas are at issue. By correlating the patents to the litigation cases we can do advanced analysis around which technology are most heavily litigated, who the top plaintiffs are, who is being sued most often and which patents are at issue more than others.

Using Innography we perform a simple search for ‘tooth whitening’ and return a set of results for the patents that match this query. Then we may want to know which of the technology areas are most heavily litigated; we can see that patents involving using light to cure a whitening composition – and the composition itself – are litigated more often than tooth bleaching trays. We also see that the top litigating companies are Ivoclar Vivadent, P&G and Dental Concepts, and that some patents are litigated disproportionally more than others. We can then correlate the litigation parties with financial data to discover which are the large companies and which are the small or unknown litigators. Finally, we can correlate and pivot on many different dimensions of this data to do decision tree-like analysis to determine the risk factors and how we could mitigate risk to aid in making our decisions.

All of this analysis is possible only when we have patent, litigation and financial data available in a format that is able to be analyzed in context of each other rather than in isolation.