AmberBlog

Patent quality is often assessed from patent data by counting the number of forward citations a patent has, and perhaps augmenting this data with other available statistical data such as the number of family members a patent has, and whether the patent has been litigated or not. While such analysis is increasingly possible from available patent data, are these necessarily the right measures? Just because something can be measured, does it have predictive value?

The thinking behind counting the number of forward citations from a given patent is that each forward citation shows that somebody else (or the patentee itself) is filing a patent for a similar invention, suggesting that the patent being anlaysed may have some value. This does make sense, and for the purpose of today's blog can be regarded as a basic network measure, in reference to the fact that most patents are connected to other patents by citation linkages, leading to a network of patents.

In addition, we define a set of prosecution measures (because these measures all relate to decisions made by the patent owner or their attorney about how to prosecute their patents):

• the number of family members,
• if the patent or any family members have been abandoned,
• the patent coverage in key market,
• length of the patent claims
• number of patent claims
• and other patent family measures.

The thinking behind the value of prosecution measures is that that the owner should be best qualified to judge the quality of the patents. Decisions made by patent owners during patent prosecution can provides a useful signal about how they percieve the quality of the patent.

A third category of patent quality predictor is what can be defined as litigation measures. These refer to whether the patent has been litigated, either during its prosecution or after it has been granted. And we can also define a fourth category, internal measures. This can include categories such as the number of inventors, if inventors come from different nationalities, whether the applicant is a big or a small company, the number and breadth of International Patent Classification (IPC) patent codes, all of which are thought by some to be predictors of patent quality. Internal measures' can be distinguished from prosecution measures in that internal measures are largely set by the parameters of the invention and can't be controlled by the patent owner - a patent owner can't, for example, in ordinary circumstances control the number of international inventors for a given invention.

The strengths and weaknesses of these different measures can be contrasted below. 

Of the above measures, the basic network measure of forward citation count is probably the strongest measure, even allowing for the its reliance on patent age (some analysts adjust the forward citation according to the patent age to compensate for this. Other analysts adjust the forward citation count to allow for different practices in different patent offices).

But what if you could draw upon the whole of the patent network when assessing patents? Let us take the example of a patent with 15 forward citations and 10 backward citations. Altogether this is 25 pieces of data used to assess this quality (assuming that the backward citation count combined with the forward citation count to assess the quality of the patent). But each of these citations in turn is connected to other patents, leading to what can be a vast network, as shown in the figure below. Logically, as in all areas of business, some patents will be more valuable than others, and these patents should be identifiable as those with the strongest influence to other patents in the same area of the network. The figure below shows the patents forming into a cluster, with the cluster showing a discrete area of technology, in the case the details of the composition of the product being analysed.

Figure:  Example of a patent cluster, as created using Network Patent Analysis

Network Patent Analysis (NPA) can rank patents based on their influence in what can be vast networks. Up to one million citations linkages can be used to form these networks, and to rank patents, making the ranking a lot more statistically robust than a simple forward citation count (because one million data points are being analysed in this example as opposed to 25). Even recently filed patents can be recognised as high value patents, due to their network similarity to other highly ranked patents.

NPA even picks up on the prosecution measure of large patent families - this will tend to push up the individual rankings of the family members. And this is appropriate, as large patent famiies indicate a large investment in the technology, and inventions that can be hard to go around due to what can be a number of variations claimed in the different family members

In the terminology I have used above, NPA can be regarded as an full network measure, because it draws upon the whole of the network rather than just a small part of the network, as in a forward or backward citation count. And because of the power of collective wisdom, we believe that NPA can provide a quality of patent assessment superior to many other measures.

Accordingly, we can now add a new row to the above table:

Ready to move your patent analysis beyond counting forward citations? Please contact us, and we can discuss how we have helped clients large and small gain a competitive advantage by an advanced understanding of their patent landscape.