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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 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. 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. Hence the take home lesson 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?

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.

Invention never happens in a vacuum, and instead tends to build on earlier work done by either the inventor or other inventors. It is possible to track this 'knowledge flow' by looking at patent citations, which may be among the most reliable sources of innovation related data. While some other patent analysis techniques also analyse patent citations, NPA adds two improvements to this process:

• Only citations between patents in the study of interest are considered. Some broad patents have disclosures that may be relevant to a number of different fields. However, NPA is focused on finding the strongest patents within a specific field of interest, and so only takes relevant patent citations into account.
• In any case, patent citations are not treated equally. NPA has a process for weighting patent citations, and these weighted patent citations are used when assessing the relative importance of patents.

There are many other potential applications for knowledge flow analysis, including patent litigation. In the recently released report Clearing the fog: Patenting trends for the treatment of Alzheimer's disease, we have investigated which patents have had the strongest influence on other patents in this field. Clearing the fog identifed 23 clusters of patenting activity, which in turn formed into two groupings of clusters, which we names the Amyloid Grouping and Tau Grouping in relation to the proteins these patents were targeting. The top three foundation patents, or most influential, in each grouping of clusters is shown in the Table.

This table shows some interesting results. The most influential patent in the Amyloid Grouping, the now expired US4666829 filed by the University of California, discloses the Alzheimer's Amyloid Polypeptide (AAP) which is the precursor of beta amyloid, and had 94 forward citations in the dataset. The next most influential patent was the number one ranked NPA patent of all.

In the Tau Grouping, the two most influential patents, US7265148 and US7332521, were both invented by Baihua Hu, a principle scientist at Pfizer, and refer to substituted pyrrole-indoles.

It should be noted that this type of analysis should not be confused with the general NPA patent ranking process, which also takes into account other measures of patent 'popularity'. Nonetheless analysis of foundation patents can help provide a unique perspective on the history of a technology, and its key influences and influencers.

(This blog post was based on material previously presented in the Clearing the fog report: used with permission)

Ambercite is very proud to be associated with the latest Griffith Hack NPA white paper Clearing the fog: Patenting trends for the treatment of Alzheimer's disease, which was released today. In this white paper Griffith Hack, working very closely with and applying the Network Patent Analysis (NPA) process developed by Ambercite, analyses over 48,000 patents to fiilter, cluster and rank these patents. Two separate NPA maps accompany the white paper, one NPA map showing an cluster focused patent landscape map, and one NPA map showing a time scale patent landscape map.

Clearing the fog is also the best publically available demonstration yet of the powerful ability of NPA to precisely cluster patents with a precision unavailable with keyword or IPC patent code clustering. While we have seen this precise clustering for the majority of the confidential NPA client studies we have delivered, this degree of clustering is stronger than in our previous two NPA white papers on hybrid car and smartphone patents.

Clearing the fog also also demonstrates several other features of NPA:

• the power of associative searching (page 5)
• the value of a NPA time scale map (page 15, and available as a separate download)
• the concept of foundation patents (page 16)
• the ability of NPA to identify what could important future patents (page 17)
• and even some natural limitations of NPA (page 21)

As well as a detailed discussion of the leading patents, patent owners and inventors in the area of Alzheimer's disease, an increasing important disease which may impact many of our elderly and the people that care for them.

Interested in learning more, or how NPA can be applied into your business? Come back to us, and we can share more about the NPA process and deliverables.