AmberBlog

A study of 14 of the largest subway networks around the world has discovered that they all tend to end up in common network structure, once you consider the distribution of lines, stations and total distances. For example, the number of stations was proportional to the square of the number of lines. The core of the networks had a similar number of neighbors in the network, with about half of the stations found outside the core. Most interesting, the different networks all appear to converge to this optimal structure over time, regardless of how the network began.

So what has this got to do with patents? Here at Ambercite, we believes that patents are best asssessed in terms of their role in the network. We have only recently republished the image below from our smartphone report, but it remains an excellent example of how individual patents can be assessed in terms of the patents around it.

In this particular case, we can see how this particular Motorola patent is connected to a broad range of backward citattion patents, but also there is a broad range of foward citations links to a strong clusters of key Apple patents.

However not all patents have this same degree of valuable connections. In fact, virtually all of the networks we have studied have converged to  a similar distribution of patent value, (again previously presented) in which only a small number of patents have the majority of the value. 

So when the academic study found that subway networks too converged to a common network structure, this did not surprise us, and in fact we would expect to see convergence in a variety of network structures, such as for example the Delta  airline route map, which is a great example of networks in daily life . We await with interest to see the details of these other types of networks being published. 

I have previously discussed the limitations of using the count of forward citations and some other parameters to predict patent quality. But some new evidence is suggesting that these limitations are stronger than we first thought.

Many analysts in the patent landscaping field use forward citation count, either alone or in combination with othe fields such as the size of the patent family, number of claims etc, to predict patent quality. But can forward citation count be trusted?

The argument for forward citation count as an important is that a higher number of forward citations suggest that the patent being analysed is important, because

•  applicants (or even the applicant for the patent being analysed) are also filing patents in the same area,
• the applicant or examiner thought that the patent being reviewed is similar to these later patents.

In fact, these are arguments for citation analysis in general, which is at the heart of Network Patent Analysis. But at Ambercite, we believe that you need to consider all of the citations in the patent network to understand the importance of individual patents, and not just the direct citations. By only relying on forward citations (or even only forward and backward citations) you may be missing out on a lot of valuable information in the rest of the network.

The evidence for this is coming from the area of Alzheimer's treatments. We have previously reported on a white paper we have published on the patents in this area. 48,000 patents in the area of Alzheimer's treatment were whittled down to a final dataset of 2153 patents, which formed into 23 clusters. The very highest rated patent of all was US7189819, which protects the pahse III trial Alzheimer's treatment bapineuzumab, being marketed by Pfizer . In 14th position was US7195761, which protects the Eli Lilly phase III trial drug solanezumab. What we have not reported to date is that another promising alzheimers drug, which is now in phase II trials, ended up with its main patent in around the 200th position (but for commercial reasons we are not able to say any more than this about this drug or patent).

When we wrote this white paper, we decided that one of the internal tests of whether NPA worked was whether it was able to rank highly the drugs being tested in phase I, II or III Alzheimer's trials. So these results were pleasing. But would we have got the same results if we had simply counted forward citations? Table 1 below helps to answer this. And in this table, I have also added data for these three patents for backward citation count, total citation count, and the number of members in the INPADOC patent family, which is also used by some analysts as predictor of patent quality. I have also 'normalised' forward citation count by the how long the patent application has been published for, in order to determine the number of forward citations per year, which is thought to be an important parameter by some.

Table 1: NPA and other rankings for known Alzheimer treatment candidate drugs

As you can see, relying on forward or backward citation count, or the number of family members, would have given a very different ranking of these obviously important patents. The importance of these patents would have been missed. However the special algorihms used by NPA was able to rank these Alzheimer's drug very highly due to where they sat within the total patent network.

But what would you have ended up with if you had used these more conventional measures? I have reversed the question, and in Table 2 below asked which patents would have come up top in using the above measures:

Table 2: Highest ranked patents in Alzheimer's treatment data set based on non-NPA ranking measures

This of course is a very different set of patents to what NPA showed was important for Alzheimer's treatments.

Which raises the questions:

• if you are relying on metrics that rely on forward citation count to identify high value patents, what key patents are you (or your clients) missing?
• What impact could this missing information make on your business decisions?
• What business risks are you taking due to this missing information?

Contact us for further information about how we provide an unique NPA patent quality ranking for your area of business.

A lot of the interest in Network Patent Analysis (NPA) comes from clients who are interested in so called 'white space' between patent clusters.  At Ambercite we think that both clusters and white space are important:

• clusters show areas of technology investment, which is an important signal,
• white space shows the opportunities for new technologies.

 But we also know that the white space is often not completely empty, with what we have defined as 'broker patents' acting to bridge different technologies. For example, the figure below which shows the grey broker patents sitting in the middle of three different clusters.

But what if there are no broker patents? The figure below is a summary of the patent landscape from our recently published paper on Alzheimers patents - the full NPA landscape map is found here, and is well worth a look.

The structure of this patent landscape plot is very unusual in NPA terms. The patents form into very tight clusters, which in turn fall into two 'groupings' of clusters. This structure can be contrasted to say a more typical NPA map, where there is a central cluster and then a series of clusters around this with significant interactions, such as the smartphone NPA map shown here.

Investigation of the subject matter of the clusters and groupings in the Alzheimer's map suggest that, in very simple terms:

• The top grouping ('Amyloid Grouping') are focused on patents claiming drugs trying to prevent the buildup of beta amyloid, a protein which is known to accumulate in plaques found in the brains of Alzheimer's affected patients.
• The bottom grouping ('Tau Grouping') is focused on patents claiming drugs focusing on other aspects of brain chemistry, including the important Tau protein, which is present in nerve cells.

The connections between these two groupings were very sparse, comprising just three main patents, as shown in the figure below:

Within the Alzheimer's paper, the highest ranked patent of all in the was US7189819. This patent is thought to protect the drug bapineuzumab, which is undergoing stage III trials at the moment, and which is co-owned by Elan Pharmaceuticals, Johnson & Johnson, and Pfizer. This patent sits right at the centre of the largest cluster 'Peptides and antibodies targeting β-amyloid' in the top grouping of clusters. 

An important paper has just been published on the effect of bapineuzumab on Alzheimer's sufferers, and perhaps surprisingly, it was found that in the trial to have no significant effect on the amount of beta amyloid in the brain. But it was found to reduce the amount of so called 'phosphorylated tau' (p-Tau) in the brain, and this could be an important outcome as p-Tau can be broadly regarded as 'damaged tau', and so may contribute to reduced brain function and other Alzheimer's symptoms. Hence a reduction in p-Tau is possibly a good thing.

And this may not be a totally surprisingly result, as besides building up in Alzheimer patient's brains as plaques, beta amyloid is thought to cause tau to change from normal tau to p-Tau.

But bapineuzumab was supposed to relate to patents found in the Amyloid Grouping, not the Tau Grouping. So did NPA get it wrong?

Well, no. NPA is merely a way of looking at the patent landscape, a lens you could call it, and a lens can only show what is there. What NPA instead told us is:

1. all of the companies that have filed patent for bapineuzumab or similar drugs appeared to focused on managing beta amyloid, and
2. that there was unusual (compared to other technical areas we have looked it) low levels of linkages between the different clusters, and in particular the different groupings. This suggests that there might be opportunities to develop treatments that cut across the different clusters, opportunities that have not been heavily exploited to date. These treatments might be variations of pharmaceuticals targetting different mechanisms, or combinations of drugs to target more than one mechanism at once.

Hence the bapineuzumab p-Tau result has merely confirmed the value of white space analysis, particular when the white space is clearer than normal. The take home lesson from this is that such clear white space in an NPA landscape should raise the question - what opportunities for new technologies that cross over this white space are going begging?

I have had a lot of valuable discussions with people about measuring patent quality in the last few months. A number of these discussions have had people making statements along the lines: "To measure patent quality you have look at the claims, the patent wrapper, quality of the drafting, etc, and this best done by a patent attorney".

I have thought about this proposition, and come to the conclusion that it is completely true. These factors are very important, and are best done by competent patent attorneys or patent lawyers. However this is only part of the story.

Imagine buying a house. This is a big investment, particularly an existing house where you might not fully understand its history. And accordingly, and this is exactly what I did when I bought a house, you would be well advised to hire a builder or ex-builder to carefully go over the house to advise on how well it is built and how well it is likely to last. A builder is best qualified to advise on this as they are intimately aware of the intricacies of building homes.

However when I bought my house, I did not dream of asking the builder how much he thought the house was worth. And if I had done this, he probably would have refused to provide a figure, as this did not fit in with his area of professional expertise.

Instead to value our home we used a valuer. And what the valuer did was compare the overall quality of the home (in an approximate sense) to their databse of other houses in the neighbourhood where sale price data was available, and use this data to estimate the value of the house.

And both roles are very important. A builder to tell you that a house is well built - and the valuer to tell you that the house is worth buying (or keeping).

This is a key value proposition of Network Patent Analysis (NPA). NPA is not intended to replace the role of a patent attorney in reviewing the strength of individual patents. However NPA can give an objective comparison of individual patents to the other patents in the 'neighbourhood', and so provide a systematic basis for patent valuation.

As an example, consider the likely value of the Motorola patent shown in the figure below, which appears to be prior art for a number of highly ranked Apple patents.

Hence NPA can play an important role in the due diligence of patents, a role that goes beyond the more traditional roles of looking at patent validity and coverage as expressed by the claims and file wrapper of the patent. NPA is also useful as it can review up to tens of thousands of patents at a time, allowing the patent attorney to focus their valuable efforts at the most promising prospects. 

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.