AmberBlog

Patent thickets are a hot topic at the moment, but are often defined in a conceptual rather than objective sense. For example, Carl Shapiro of Berkely defined a patent thicket in 1991 as:

An overlapping set of patent rights requiring those seeking to commercialise new technology obtain licenses from multiple patentees.

These days, we might use a slightly amended definition:

An overlapping set of patent rights requiring those seeking to commercialise new technology obtain licenses from one or multiple patentees.

Many of us would probably claim to be able to recognise a patent thicket when we see one, but it is also useful to be able to objectively quantify the presence and strength of a patent thicket. Network Patent Analysis (NPA) is ideally suited to this, due to its algorithms that can precisely cluster similar patents together, independently of whether these patents share keywords or IPC codes. Other algorithms are able to define the relationship strength between each of the individual patents in the cluster, and the ranking of these patents within the cluster.

A typical cluster is illustrated below - this shows the leading patents and their owners in the mobile data access cluster of our smartphone report.

Ranking scores in turn can be used to assign 'patent points' to each patent in the cluster, so in a cluster of 5 patents, the top ranked patent has 5 points, and the last ranked patent 1 point. This in turn allows the comparison of individual patent owners within larger clusters in a more meaningful way than just counting patent portfolios.

So how can we apply these algorithms to patent thickets? A patent cluster can be regarded as a type of patent thicket. By looking at the average relationship strength within these clusters, we can estimate the degree of overlap, and therefore come up with a figure for 'Cluster thicket density'.

And by looking at the relative 'patent point' scores for different patent owners, we can start to understand the relative dominance of these clusters by individual patent owners. And since patent thickets are a legal concept, we might limit such analysis to the patents in the cluster that are less than 20 years old, since patents older than this will be expired (although a patent historian might include this in their analysis).

In the figure below, we have looked at cluster densities for the three largest clusters for a range of projects, being the published smartphone and Azheimer's reports, and three unpublished projects. These value range from 24.3 for the largest cluster (Peptides and antibodies) down to 1.6 for a mining project we have done. 

We were initially surprised to see that smartphone technologies did not have the highest cluster density. However what has made the smartphone patent wars so complex is that companies are litigating over a wide range of technologies that make up a smartphone. In comparison, a smaller range of technologies might be found within a drug, but the potential very high value of this drug might encouraging a plethora of patent filing around these key techologies.

We can also look further at the details of these clusters. In the table below, we have only looked at the largest cluster for each technology, but it is self-evident that the analysis could be extended into any of these clusters within the project.

It should be noted too that the size of the clusters, as we have defined them, depends heavily on one of our settings in our process (which defines how many patents we show in the final patent landscape map). If we use different setting we would end up with a different cluster size, and so this figure is given for completeness only. However the other parameters should be far less dependant on this process setting.

So as you can see, there are a range of results. Cluster size ranges from 65 to 941 patents (but note the dependence on NPA process settings). The proportion of 'patent points' owned by the leading patent owner in the top cluster ranges from 45% for an ICT project, to just 6.2% for a mechanical engineering project. There does not appear to be any direct relationship between cluster size, and cluster density. Similarily, there is no relationship between the proportion of the cluster owned by the leading patent owner, and other parameters. In short, every cluster (patent thicket) is different and needs be considered as such.

But in summary, yes it is possible to quantify a patent thicket (otherwise known as cluster), in any of technology, and to determine how strong the leading patent owner is within this cluster.

The recently published NPA white paper on Alzheimer's patents analysed the 48,000 Alzheimer's patents in a variety of ways. But which patent applicants had the strongest patent portfolios?

Traditionally such analysis has been done by counting the patents filed by different applicants. However it is self-evident that all patents are not equal in value or importance, and NPA uses the wealth of information contained in the citation links between patents to rank the expected relative value of patents. In this particular study we identified the leading 2,153 patents based on their strength of citation connections, and ranked all of these patents in order. A number of these patents were found to have equal rankings (for example, there were two 5th ranked patents, two 16th ranked patents, etc) meaning that there were 915 unique patent rankings. The highest ranked patent was given 915 'patent points' in our analysis, the second ranked patent 914 points, and so on until the last ranked of the 2153 leading patents was assigned 1 point. By adding up the patent points for given patent applicants, it is possible to rank patent portfolios in a way that takes into account the relative quality of the patents in these portfolios.

We also aimed to agglomerate the patent portfolios of subsidiaries of larger patent owners into the parent company. While is was impractical to determine the ultimate owner of all 2153 patents, we did identify the current ultimate owner of all of the largest portfolios. For example, Wyeth patents are now all controlled by Pfizer, and there are many other examples of similar consolidation. Similarily we combined all patents owned by agencies of the US Government under the one owner "US Goverment Agencies".

But enough of the background - what did we find? Details from the top 20 portfolios are shown in the table below.This shows the 20 patent applicants with the strongest portfolios, their relative strength in relation the leading applicant, the number of patents in the leading 2153 patents, and some details of their top ranked patent in this study. There are also some details of the patent cluster (grouping of similar patents as determined by the NPA algorithms) where the leading patent is found, along with the most important cluster for each applicant (further details of these clusters are found in the white paper).

Many of the leading applicants, such as Pfizer, GlaxoSmithKline, Merck, Astrazeneca, Johnson and Johnson, Bayer and Bristol Myers Squibb are well known pharmaceutical companies and their placement in this table may not surprise observers. The position of Pfizer and Johnson & Johnson is further enhanced by their share of the portfolios jointly owned along with Elan.  

There are also some smaller and more specialised companies with strong portfolios. These smaller companies are led by Elan, which describes itself as 'a neuroscience-focused biotechnology company headquartered in Dublin, Ireland', and which owns a strong portfolio of Alzheimer's patent both by itself and together with larger pharmaceutical companies. Other smaller companies include Vertex Pharmaceuticals and Acadia Pharmaceuticals, both of the US, Boehringer Ingelheim of Germany, Bellus Health of Canada, and Teva Pharmaceuticals of Israel.

The Alzheimer's NPA report also noted that the clusters formed into two 'Groupings' of clusters (best seen in the Alzheimer's NPA landscape plots). One grouping of clusters were related to the Amyloid protein, and the second grouping to the Tau protein. In the figure below, the leading ten applicants in the above list are compared in terms of where their patents fall within these two Groupings.

This image shows that the large pharmaceutical companies Pfizer, GlaxoSmithKline and Merck (and to a lesser extent AstraZeneca) all have patent portfolios divided between the two Groupings of clusters. In contrast, the other applicants in this top ten list are focussed in just one of these groupings. Elan (and its partners) and Acadia Pharmaceuticals are both focussed on the Amyloid Grouping, while Vertex Pharmaceuticals is focused on the Tau Grouping.

But will these patent portfolio's translate to commercial success? One of the pleasing results from the Alzheimer's NPA white paper was the relatively young (compared other NPA studies we have done) age profile of the leading patents, which suggests a lot of recent research and related patent filing activity. The flip side of this recent activity is that even the more promising of these patented drugs will still be going through drug trials, and so we can only wait to find out which of these patented drugs are commercially successful.