AmberBlog

Recent and valuable discussions on Linked In regarding patent quality has reminded us that timing can be all important when assessing patent quality. The main elements of timing in relation to patent analysis are:

• Timing with respect to the expiry date of the patent. Patents have a maximum (except for some pharma patents) term of 20 years, but it is still possible to assert patents after they have expired, providing the assertion relates to commercial activity by the alleged infringer prior to the expiry date (Obviously an injunction becomes a moot point after expiry).
• Timing with respect to the commercial success of the patented technology. Some technologies take years to reach significant commercial volume. As just one example, Paice Corporation, the developer of the hybrid car patent that has been successfully asserted against Toyota and Ford, filed this patent in 1992, which happened to coincide with a long term low in oil prices. By coincidence, Toyota started developing their hybrid technology at about the same time, but it was 1997 before the first Japanese hybrid Toyota was sold, and 2001 before the Prius was available outside of Japan. In very recent times hybrid drive trains have been commercialised by a number of manufacturers. However Paicie may miss out on the bulk of this boom, with their key US5343970 patent due to expire in 2012 (although Paice have other patents they may assert).

The first of these elements should be straight forward for any patent analysis, and can be determined by comparing the expiry dates of the patents being analysed to the commercialisation timeframe of the patented technology.

The second factor can be analysed as well. Advanced patent analysis techniques such as NPA can be used to group patents into clusters of patents of related subject matter. Patents cost money to file, so a large amount of activity in a given area means that the area has  value. If lots of patents are being filed in a cluster at around the same time, this means that that area become commercially interesting for the patents to become commercially valuable. Since clusters in an NPA analysis can be dominated by highly ranked patents, we can start to undertand when the key technologies in each area have been developed, and this should correlate with a commercial or technology boom in this area.

The value of this type of analysis becomes very clear when we consider a comparison of the patent timelines for the top ranked patents in a few different technology areas we have looked, see the figure below. In this figure, we have identified when the highest rated patents in a given analysis were filed, and grouped these filing dates (after 1990) into 5 year groups. It is easy to seen when each of these area has attracted the most attention. 

This figure shows that:

• The three clusters from the smartphone report occupied three of out the top four positions. This is not surprising when we consider the recent innovation and legal developments in this area.
• Hybrid car patents were second on this list. Hybrid cars have become a lot more popular in recent years, and more importantly a range of car manufacturers are now introducing hybrid cars to help meet fuel efficiency targets set by governments.
• There were a lot of recent patents for Alzheimer's treatments. Although Alzheimer's has been known for over a century, there has been a large amount of medical research done in the last decade.
• The ICT project we looked at was for a very new area of technology, and hence there was nothing filed of consequence prior to 1995. However activity had appeared to have slowed down in this particular area of technology in the recent years. This shows that this particular technology had a relatively narrow peak of activity.
• The mining project had a number of older patents, but then an increasing number of newer patents. The reason for this is there was a new development in this particular tecnnology, and this has catalysed more recent activity.
• The heavy engineering project had a similar story to the mining project, but the new development peaked in the period between 1995 to 1999, when a lot of the key patents were filed.
• In contrast, the food technology project was in a very mature area, which was not surprising given the specific subject matter (which I can't discuss in detail, but I can say it was for a type of food that many of us eat almost every day).

Having said all of that, this comparison of technology timelines should be considered an example only of what is possible, as all of this data was collected in slightly different ways (apart from the three smartphone patent clusters). Nonetheless, this gives a good example of what might be discoverable from a NPA patent timeline analysis.

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)