Every company must seek to know something of the opportunities and threats that can impact their operations.  The risk of being caught by a surprise is just too great.  This includes understanding government regulation, technology advances and competitors.  It's these last two where patent analysis comes to the fore. 

A company reveals a lot about itself, it's technologies and plans for the future in terms of product or service in a patent.  If the company wishes to protect the Intellectual Property (IP) of its inventions they form and submit a patent application.  This becomes a matter of public record. 

Many large companies churn out hundreds of patents per year.  Collecting and comparing large groups of patents between companies becomes a daunting challenge when each can run to hundreds of pages of dense technical and legal prose. 

Below is a small movie showing a quick way to begin analyzing such a mountain of data.  It starts by using a specially tuned version of a Natural Language Processing engine to look for the claims, novelties, authors, prior art, etc. resident in each application.  The construction of a co-occurrence matrix of several dimensions lends itself to the modern equivalent of "descriptive statistics" about what is in the patents and how they compare between two companies.

To protect the confidentiality of customers the example in the movie uses false data

Note how simple sorting once the dimensions of interest are built reveal similarities and differences between patents, or patent groups.  This is vital to understanding areas of concentration from a competitive point of view and shows where technological investment occurs.  Similarities also point out where you will likely be in legal conflict in getting a patent approved.  Differences on the other hand show uniqueness and potential areas of advantage both legally and in the marketplace.

Beyond this simple examination more sophisticated patent modeling work can include areas like the following;

  • Patent Vectors: Analysis of claims, methods, prior art etc. are modeled into vectors or directional motion to help understand speed and aim of the technological advance of a company. 
  • Patent Licensing: Patents can represent up to 85% of a company’s value.  Use of the vectors and modeling other industry participants or even competitors can be used to identify new candidates for out-licensing, or cross licensing, or suppliers for in-licensing and open innovation.
  • Patent Litigation:  Patent co-occurrence matrices and vector models can help locate other patents that may invalidate prior art and avoid lengthy litigation.
  • M&A Due Diligence:  Vector models and graph database connection analysis of patents can establish chain-of-title to determine if the assets you are buying or selling are encumbered by unforeseen entanglements and are worth the price.
  • Competitive Intelligence:  These models look at competitors’ IP strategy, portfolio strength, strongest patents, filing trends, and trademarks. This gives a concise view of where opportunities and threats lie.
  • Innovation & Patentability:  Using the vectors to uncover areas of new and unique novelties is the goal in this kind of model.  In conjunction with the Competitive Intelligence from about, you form your own IP strategy.

Comment