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.

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

Image via Wikipedia

Becoming a tennis professional and touring the world sounds like a great way to make a living, but like many ways to make money, the rewards are far from evenly distributed. For example, Tennis.com can tell you that the Serbian Novak Djokovic leads Spaniard Rafael Nadal as the leading money earner for the ATP tour. Novak has earned over $10 million in the last 12 months compared to the $6.4 million earned by Rafael. In contrast 99th ranked Frederico Gil of Portugal earned has earned $292,000 this season, a mere $2,000 ahead of Jeremy Chardy of France.

When we look at this distribution of earnings, Figure 1, we can see that they fit into a power law distribution (great blog post by John Hagel), which has a strong bias toward the top earners, but then shows a flattening off for the lower ranked players.

Power law distributions are also quite common in business, including for patent and portfolio values. For example, the Network Patent Analysis (NPA) being developed by Ambercite has the ability to assign rankings, and patent scores to individual patents, using such powerlaw algorithms. Different patent owners can be compared in terms of their patent portfolio score. In Figure 2, we compare the relative rankings in three different fields of patent ownership (smartphones, hybrid cars and Alzheimer treatments) to the power law curve we have just seen for men’s tennis players.

Figure 2 confirms the patent portfolio scores are spread just as unevenly in patent portfolio rankings as tennis professionals. But they in fact true power law curves?

The mathematicians among us will confirm that a true power law distribution will produce a straight line when plotted on a logarithmic curve. When Figure 2 is replotted in this way, we can see a ‘softening’ of the power law effect for all four distributions after about the 20th rank, Figure 3. This softening is much stronger though for tennis pros than for patent owners. We can speculate that the ATP (Association of Tennis Players) may try to manage the tour to ensure that some of the money is spread to lesser ranked players. Of course there is no such equivalent organisation for patent owners, which means it may harder to be a lowly ranked patent owner than a lowly ranked tennis player!

A recent story which many of us might have seen, highlighted the importance of visualizations in jury trials. In many ways the value of visualizations are self-evident. Yet many of us in the patent space are slow to embrace the power of visualizations to make the complex stories we tell easier to explain.

An analogy we like to use in presentations is the hypothetical new visitor to New York. They do their homework - they determine that the subway is a good way to get around. But they have a choice on how they receive information about the subway system - either as a spreadsheet list of subway stations, or a subway map showing how the stations link together.

Of course this hypothetical visitor is almost always going to select a subway map over a list of subway stations. Yet the majority of patent search results are supplied as a list, or use simple visualizations that might show how a given patent is connected to their nearest neighbors, but no more than that.

The technology now exists to turn patent data into sophisticated visualizations that turn data into insights. Consider the smartphone patent 'battlefield map', which summarises 7100 smartphone patents that have either been litigated, or are closely linked to patents that have been litigated. This single map, produced using Network Patent Analysis (NPA™) provides a unique perspective on a complex legal space in one picture. Further examples of these types of visualizations can be found in the smartphone patent white paper.

Improved insight can help companies make better decisions, but this may only be one benefit. Just as NPA can help our clients understand how their patents fit into the technology environment they are competing in, NPA can also help patent owners market their IP to the world. In the first public NPA™ study we published, we highlighted the importance of hybrid car patents owned by Paice Corporation, an early mover in the hybrid car space. This was not because Paice was a client - to this date there has been no commercial relationship between Paice and either Griffith Hack or Ambercite. We were just reporting on what we found. However Paice were quick to pick up on our endorsement of their patents, and now use our analysis of their patents on their website (and with our permission).

The ability of NPA™ patent analysis to provide compelling marketing support for NPA™ owners is being picked up by our clients, and understandably. We all face the challenge on telling our story in a busy world. Any improved delivery of this story has the potential to make a positive impact on how the story is received by your various stakeholders, such as licensing targets, commercial partners, suppliers, clients, board of directors etc.

Want to know more? Check out our smartphone or hybrid car papers. However this is only part of what we can now do, and where we are going to take patent visualization in the future. Our aim is to change the way you think about patents and patent data, and we are already heading down this path.

If you think you are ready to move your IP marketing to the next level, contact us to arrange a presentation of what we can do, and our client case studies. You just might surprise yourself about what is now possible with the right tools.