AmberBlog

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.

As discussed here, NPA analyses vast networks of patents to rank and group patents. While we have previously shown patent rankings in a simple fashion (1st, 2nd, 3rd, 4th, etc), we can also calculate patent rankings in terms of their relative strength. Just like with tennis pro earnings and patent portfolios, we can 'normalise' these values so that the highest ranked patent has a value of 100. We have done this for the three public domain NPA patent studies we have published, namely for hybrid cars, smartphones and Alzheimer's treatments (will be published soon).

Figure 1 shows that the drop-off in relative values was slower for patents than for tennis pros (and therefore patent portfolios). The three patent distributions appeared to have similar curves, suggesting a similar distribution of relative values. 

Practically, we can interpret Figure 1 (and its underlying data) to show that:

• The 10th ranked patent is worth between around 1/3 to 1/2 of the top ranked patent (35% to 52% in our data)
• The 50th ranked patent is worth around 1/6th of the top ranked patent (13 to 18%)
• The 100th ranked patent is worth around 1/12th of the top ranked patent (8 to 10%)
• The 200th ranked patent is worth around 1/20th (4% to 6%)

This drop-off in relative value was actually slower than we expected, which shows the value of analysing patent data rather than speculating.

We can also use this data to show the rise in cumulative patent value as we move from the highest ranked patent. Figure 2. What is astounding however is that regardless of the size of the datasets, 58,000 down to 7,000, the curves are very similar.

There are slight differences between the curves, but I don't think that three curves are enough to draw any firm conclusions about these differences. Again, we can use this data to draw some practical conclusions regarding patent value:

• The top 100 patents represent between 43 to 53% of the total predicted value of the patent dataset
• The 200th patent takes you to around 60% of the highest value
• About 80% of patent value in a given dataset, regardless of its size (!), appears to come from the top 500 patents
• About 90% of patent value is given by the top 900 patents
• At about 1350 patents, we have covered around 95% of the patent value, with the curve flattening off significantly
• 2000 patents should pick up around 98% of the value in the dataset 

In other words, if you want to be on top of the critical patents in a given field, you need to analyse a targeted dataset of 500 to 2000 patents, depending on the level of understanding you require. While this may seem like a lot of work, the ability to group, rank and visualize patent connections can make this task a lot more manageable, particularly if combined with more traditional patent analysis methods. 

Mike Lloyd and Doris Spielthenner

Image via Wikipedia

Becoming a tennis professional and touring the world sounds like a great way to make a living, but like many ways to make money, the rewards are far from evenly distributed. For example, Tennis.com can tell you that the Serbian Novak Djokovic leads Spaniard Rafael Nadal as the leading money earner for the ATP tour. Novak has earned over $10 million in the last 12 months compared to the $6.4 million earned by Rafael. In contrast 99th ranked Frederico Gil of Portugal earned has earned $292,000 this season, a mere $2,000 ahead of Jeremy Chardy of France.

When we look at this distribution of earnings, Figure 1, we can see that they fit into a power law distribution (great blog post by John Hagel), which has a strong bias toward the top earners, but then shows a flattening off for the lower ranked players.

Power law distributions are also quite common in business, including for patent and portfolio values. For example, the Network Patent Analysis (NPA) being developed by Ambercite has the ability to assign rankings, and patent scores to individual patents, using such powerlaw algorithms. Different patent owners can be compared in terms of their patent portfolio score. In Figure 2, we compare the relative rankings in three different fields of patent ownership (smartphones, hybrid cars and Alzheimer treatments) to the power law curve we have just seen for men’s tennis players.

Figure 2 confirms the patent portfolio scores are spread just as unevenly in patent portfolio rankings as tennis professionals. But they in fact true power law curves?

The mathematicians among us will confirm that a true power law distribution will produce a straight line when plotted on a logarithmic curve. When Figure 2 is replotted in this way, we can see a ‘softening’ of the power law effect for all four distributions after about the 20th rank, Figure 3. This softening is much stronger though for tennis pros than for patent owners. We can speculate that the ATP (Association of Tennis Players) may try to manage the tour to ensure that some of the money is spread to lesser ranked players. Of course there is no such equivalent organisation for patent owners, which means it may harder to be a lowly ranked patent owner than a lowly ranked tennis player!