AmberBlog

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!