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.
LSA is Great Theory
by Roji John on July 21, 2008
Well, it may be all right in practice, but it will never work in theory. --Warren Buffett
Since its inception in the 80s (US4839853), many search applications have trended towards LSA as a method for correlating concepts. We often find that what sounds great in theory falls short in practice. In the realm of Intellectual Property (IP), Latent Symantic Analysis (LSA) seems one of these situations.
LSA can be described as a technique using statistical analysis to find associations between terms. Without getting into the mathematics, documents with similar yet uncommon terms are considered semantically close.
In theory, the methodology should be excellent for classifying text without actually reading or understanding it. In fact, for general text as may exist on the internet, LSA can prove useful in finding a handful of very related results.
Our task in the IP arena is quite different. The very specific terms used in these technical documents can often confuse LSA systems. But we also have a key advantage within IP—it’s been classified by an expert examiner at the patent office. That examiner knows precisely the concepts described in the invention.
Many users in this community are already very familiar with the US and IPC classification systems. Each system has its pros and cons. Making both classification systems available and approachable is a basic function that every patent application should provide. In addition, being able to leverage the knowledge embedded in the classification systems is key. When a document has several classifications tagged, they must all be taken into account by the application to accurately contain the invention.
Another problem with LSA is scalability. True LSA requires growing matrices that become difficult to manage and scale with large document sets. Shortcuts can be taken, but queries that are not pre-calculated can take minutes to hours to days. With today’s demand for instant information, users cannot be expected to endure such delays. Instant access to the data is a must.
Finally, LSA provides little room for user control. Its algorithms are static until updated by the provider.
I believe applications that learn from the user are better than ones that try to teach the user. Providing a responsive, accessible and repeatable method to teach the application keeps control in the hands of the expert—you!
These are all commitments we take seriously as we strive to provide the best application possible.
