AmberBlog

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.

While many people use forward citation count as a simple predictor of patent quality, the number of backward citations is also mentioned by some people as a predictor of patent quality. A commonly expressed hypothesis is that while lot of forward citations suggests a good patent, lot of backward citation probably suggests a weak patent. Suggested reasons for this might include that that a higher backward citations is a predictor of a weaker patent due to a) the examiner not liking the patent and trying harder to invalidate it or b) there is simply more relevant prior art, and so the likelihood of patent grant is lower. I myself was more or less accepting of this hypothesis until I realised that some of the very highest ranked patents in some of our NPA studies had high backward citations counts - despite some other evidence suggesting that these patents were truly important.

So I looked up the published literature on the effect of backward citation count. While lots of people were supporting this hypothesis because it sounded about right (it does, doesn't it?), I was looking for supporting data and not just subjective opinions. It turned out that the best data I could find was by Lanjouw and Schankermann, 2002, "Research Productivity and Patent Quality: Measurement with Multiple indicators", which correlated their measure of patent quality with various patent quality measures.

Lanjouw and Schankermann looked at a number of different indicators, including forward citation count which, not surprisingly, correlated with their measurement of patent quality. But so did backward citation count - in a positive fashion (see table 2 of the publication). In fact, in two of the seven technology areas studied (Biotech and Other Health), the influence of the backward citation count was greater than the number of forward citations (in the five years after patent filing date). But even in the remaining five technology areas, the effect of the number of backward citations was still significant, and in some areas not that much less than the forward citation effect.

So the opening hypothesis did not stack up to testing - but why? Why did a high backward patent count correlate with higher quality patents in this published study?

One possible explanation for this is that granted patent with a larger prior art base may disclose a broader and ultimately more valuable invention; 'standing on the shoulders of giants', as somebody once put it. 

There is something else to consider. Besides promoting Ambercite I manage a portfolio of (unrelated to Ambercite) patents, and work closely with good patent attorneys to do so. Like many patent applicants we have a policy of seeking the broadest possible patent claims (without being silly about it), particularly for what we think at the best inventions. Not surprisingly, sometimes the examiners push back at these broad claims by throwing lots of prior art at us, leading to the inevitable process of negotiation with the examiners until we end up with patentable claims, but as broad as possible to support our objectives. In other words, when assessing why some patents attract lots of prior art patents, this may be because some patent applicants try harder for what they think are their more important patents.  

I am not sure if other applicants have the same approach to patent prosecution as us (I would be interested in feedback on this) - but if so, this may only further confuse the relationship between patent quality and backward citation count. 

What do other people think of the likely relationship between patent quality and backward citation count, and factors driving this?

It is well known that there has been an increase in patent filings in many countries. But what can these patent filings tell us about the rate of technology change, as opposed to just the amount of patent activity?

One method of answering this is to look at the age of the backward citations. Patent examiners or applicants refer to patents in their search reports that they think are relevant to the patent being examined, and so we should be able to measure the rate of change based on how far back their citations go. We can measure this for either a single or group of patents in two different ways:

• By looking at the average age (difference in years between the patent and backward citations) of the backward citations
• By looking at the oldest backward citations, which tell us just how long patents thought to be relevent to the patent being examined have been around for.

From a statistical viewpoint, the second measure is flawed as just a single, potentially careless reference to a very old patent can distort the data. In fact we are mainly interested in the age of the majority of the citations - we may not need to know about the age of the very oldest citations to gain a reasonable idea of the rate of change. In practice, the age of the youngest 90% of the patent citations should give a reasonable idea of how long a particular technology has been around for (as only 10% of backward citations are older than this). We can call this measure 'Patent Turnover", being the maximum age of the youngest 90% of backwards citations.

We would also want to be a little selective about which backward citations we use to calculate these figures. As an example, imagine the development of a new drug diagnosis test with application for a wide variety of therapies, with patents filed for this new diagnosis. This development in turn may catalyse a number of new patents, including potentially in the area of Alzheimer's treatments (as just one example). The patent examiner or applicant reviewing this new Alzheimer's patent (that builds on this drug diagonsis invention) may quite rightly cite this drug diagnosis paten in their search report. But is the original drug diagnosis patent an Alzheimer's patent? We would argue not - it falls within another technology field, namely that of drug diagnosis, and so should be distinguished from any technology analysis of Alzheimer's patents.

Hence any analysis of the ages of the backward citations in any technology field should only consider the age of cited patents within the field itself - otherwise we are measuring the rate of change of other fields as well. Luckily, NPA lends itself very well to this type of analysis, as it is can be used to cluster patents with other patents within the same technology field, but to ignore patents cited outside of the nominated field.

Anyway, enough background -  so what is the rate of change for a range of different technical fields? We have taken this data in Figure 1 from a variety of projects. The 'n' values for each type of technology refer to the number of relevant backward citations (to other patents in the same field) and not to the number of patents. These values are affected by the way each project was set up, and should not be interpreted in any other way than to confirm that there is sufficient data for the calculated values to statistically robust. 

 Figure 1 tells us:

• The average age of backward citations ranges from about 6.5 years (an ICT project and for Alzheimer's drugs) up to over 15 years for a mining industry project
• The "Patent Turnover" (age of youngest 90% of the patents) is around twice the average age of the citations.
• Within the area of smartphones, patent associated with mobile data access (to emails etc) had a lower citation age than patents associated with touchscreens. In simple terms, touchscreens appear to have been around for longer than mobile data access. 

The varying Patent Turnover figures would suggest that companies may need to innovate at a faster rate within the ICT and pharmaceutical industries when compared to mechanical engineering and mining. Accordingly, a technology more than 10 years in the ICT industry may be obsolete, but technologies in the mechanical engineering and mining industries may be commercially relevant for much longer. These trends may not surprise many, but it is pleasing to see the patent data confirming what we probably all suspected.

We can also compare backward citation age gaps for different patent owners for a given technology, in this case for four leading owners in the mobile data access patent cluster in our previously published smartphone white paper, see Figure 2 (the number of backward citations used to calculate the Motorola values are less than ideal, and this may have affected this result). 

So what do these owner results mean? There are different potential interpretations. We do know that Palm (remember the Palm Pilot?) and RIM (makers of Blackberry) were among the first companies to offer portable devices that could be used to read your email. It is possible that the examiners for the earlier patents filed by these companies could not find too many (then) recently filed prior art patents, and so had to go further back into the patent database to find relevent prior art. In contrast, later entrants to this market had more (then) recently filed patents to choose from. In this case, we could say that the early patents in this field were 'game changing' patents, Figure 3.

There is potentiallly a lot more that can be gleaned from this type of analysis, and we will continue to develop analysis tools to better interpret the wealth of data within the world's patent databases.  

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