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