AmberBlog

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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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

Cover via Amazon

The collective intelligence of crowds is an increasingly recognised within the business community, partly inspired by the 2004 book on ‘The Wisdom of Crowds’ by New York journalist James Surowiecki. A number of examples of this collective wisdom are given, such as ability of a fairground crowd to estimate the weight of a bull in an agricultural fair (the average guess of the crowd was very close to the actual weight of the bull). And in the patent world some websites are being set up with the purpose of accessing this wisdom, for example by requesting that contributors help uncover prior art for patents being litigated.

But what if there was a way to tap into this collective wisdom of (patent) crowds without even asking them? And a method that also avoided the various risks of crowds as discussed in the now seminal 1841 book “Extraordinary Popular Delusions and the Madness of Crowds”, by Scottish journalist James Mackay?

We believe that Network Patent Analysis (NPA) may be that method. As discussed on the Ambercite website, NPA analyses the collective intelligence of patent applicants as expressed by their choices to file patents for particular types of inventions. These opinions are grouped by patent citations. Up to a million patent citations can be analysed in an NPA study, and the result is a grouping and ranking of up to 250,000 patents. NPA is an objective means of grouping and summarising many subjective opinions.

But how can we be sure that NPA is drawing upon the wisdom and any the madness of (the patent filing) crowds? According to Surowiecki, a wise crowd shares the following characteristics:

•         Diversity of opinion

•         Independence of opinion

•         Decentralisation

•         A mechanism to aggregate diverse opinions of the crowd

Do patent applicants share the first three of these characteristics? It is easy to believe that, on the whole, patent applicants act and think independently of each other, particularly in commercially sensitive areas where they try not to share information with each other. Some of the independence of the patent data might be comprised by large organisations filing lots of patents in some areas, but important technology areas are full of patents filed by a range of different and competing organisations.

And as for the fourth characteristic, aggregation of opinions, this is exactly what NPA does. NPA aggregates vast amounts of patent citation data to create a collective opinion on grouping and ranking of patents and their related technologies.

Want to know more? Check out our white papers available on the Griffith Hack or Ambercite websites, or download copies of reports including a recently prepared analysis of a new patent litigation against hybrid car market leader Toyota, along with our popular report on litigated smartphone patents. All reports are now available without registration – but feel free to contact us if you would like to apply the benefit of NPA to your business.

A very well written article in The Deal Magazine last week has highlighted the rapidly changing value of patents. David Marcus reminded us that the recent very high prices being paid for some patent are a new phenomenon, peaking at the $750,000 paid per patent for the recently sold Nortel portfolio.

Among other things, these sorts of prices have led to some to criticise the apparent divergence of patent owners into ‘good’ patent users and ‘bad’ non-practising but patent owning entities, but of course divisions such as this are never that black and white. For example the non-practising entity NTP who famously sued RIM in the earlier part of the last decade were asserting patents developed by the inventors when working at an earlier but failed telecommunications firm. This makes sense when you considering that developing patentable inventions is hard, and normally needs strong knowledge of the underlying technology in a field.

In the final paragraph of this article, Marcus notes: 

Focusing on the excesses in the patent system obscures how the separation of manufacturing from product development has inevitably made intellectual property a much more liquid asset -- and, just as inevitably, increased the importance of lawyers and valuation experts in a region that still doesn't know quite what to do with them

The ongoing value of patents is no doubt a topic that will lead to a lot more debate going forward. However one factor that may help this situation is improved transparency of the patent system and patent data. This is an area where the high quality insights available through the application of NPA may be able to make a real contribution.