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'Patent landscape' is a term often used by a range of people in the IP profession. But what exactly is a patent landscape in practice? And why are they increasingly being produced?

Why are patent landscapes so useful?

Patent landscapes can provide a very unique and valuable perspective on a technology and its commercial interests.

Patent applicants file patents in order to protect their innovation. Governments grant patents for patentable inventions in order to a) encourage investment in innovation and b) to encourage publication of new ideas. This second effect should not be underestimated, as even the most secretive of companies can file thousands of patents, providing access to their thinking that that otherwise would be impossible to obtain. Further helping the quality of patent data is the fact that patents can cost significant amounts to file, meaning that applicants only file patents for what they perceive to be potentially commercially valuable and worthwhile inventions.

According to the latest data available from WIPO, 1.9 million patents were filed in 2010, to add to the more than 70 million patents already filed. Patents are often made available from government run databases in a highly systematic manner, making them ideal for future analysis. Patent landscapes can provide an technology and commercial overview simply unavailable from any other source.

What exactly is a patent landscape?

WIPO has defined 'patent landscape' as meaning 'an overview of patenting activity in a field of technology, and published a range of patent landscape reports on their website

By reviewing these and other published patent landscape reports, the most common approaches start to come out:

1) The objective of the patent landscape is defined. This normally has some commercial, public policy or marketing objective, and in some cases can include some non-patent perspectives on the objective

2) The objective is used to define a relevant technology

3) The definition of the technology is used to create a patent search aimed at finding patents relevant to the technology. The search query can include a combination of keywords and patent classification (IPC or USPTO). Patents can be limited to those filed in certain countries, or in recent years. Identified patents can be enhanced by adding patents linked by forward or backward citations to the identified patents

4) In some landscapes patents are combined into patent families, for example when the same patent is filed in a number of different countries. In other landscapes, the individual patents are kept separate.

5) A process can be used to remove 'false positives', i.e. patents picked up in the search terms that are not relevant to the project objective. This can be automated (for example based on looking for certain keywords or patent codes that are clearly irrelevant to the objective) or manual, where a reviewer manually removes irrelevant patents. Alternatively a combination of automated and manual processes can be used.

6) An attempt is made to standardise the owners of patents. For example, patents owned by TOYOTA USA may be combined with patents owned by TOYOTA JAPAN on the basis that they belong to the same overall company.

7) Patents are group in a meaningful way. For example a patent landscape report in the area of renewable energy patents might cluster patents into by the type of renewable energy. In some cases, this taxonomy of patent groups can include sub-groups, for example the aforementioned set of renewable energy patents would include a grouping of solar energy patents, and then could include sub-groups for different types of solar energy production. Clustering can be done in an automatic fashion, for example by keywords or patent codes. Or patents can be manually assigned to clustered, or again a combination of automated and manual processes can be used. Patents can also be grouped by owner, country of origin, age, and status, and in some cases grouped using a combination of parameters, i.e. "solar cells patents filed by Japanese companies". Arguably this grouping and sub-grouping of patents is one of the most important parts of patent landscaping, as this help uncover patterns in the patent data.

8) Trends graphs can be produced to look at major filing trends, either for the overall number of patents, or just for certain groupings.

Figure 1): Filing trends for hybrid car patents. © Griffith Hack 1999. Griffith Hack images and examples will be used in this many examples in this blog instead of images from other and equally worthy authors simply to avoid any copyright issues.

9) The leading patent owners in a technology area can be identified and listed. This helps to identify who are likely to be most important companies in the technology. An alternative and also worthy approach is to identify the leading patent owners in specific groupings of patents

Figure 2:  Leadings owners in patent landscape study of Alzheimer's patents, including a breakdown into patent groupings. © Griffith Hack 2012.

10) The leading inventors in an area, or for an applicant, may be identified

11) The leading sources (where the patents have come from) and destination (in which countries the patents have been filed in) may be identified

Figure 3: Leading country of origin for carbon trading related patents. ©Griffith Hack 2012.

12) An attempt can be made to rank the patents in terms of quality. Rankings based on forward citation count and family size are the most commonly used, but different analysts can use a variety of ranking techniques. Network Patent Analysis (NPA) ranks patents based on the influence they have on a network of related patents, and has been shown to be powerful predictor of patent quality.

13) Details can be provided of some particularly interesting patents

14) Citation analysis can be used to show relationships between patents or patent owners

Figure 4:  Forward and backward citations for Motorola (now Google owned) patent US6,246,862. ©Ambercite 2012.

15) An attempt to be made to find 'white space' in the patent landscape, i.e. areas in the patent landscape where the relative number of patent filings is low.

Figure 5: NPA white space analysis. © Ambercite 2012.

Patent landscaping outputs

Outputs of patent landscapes can be in several forms

a) Reports

b) Graphs

c) Spreadsheets listing patent details, which including patent quality rankings and details of patent groupings

d) Patent landscape images, in which similar patents are clustered together . These come in two forms. The most common form of patent landscape maps cluster patents by keywords. At Ambercite, we group patents using a unique process of clustering patents based on patent citation linkages as we believe that this is more precise and inclusive than clustering patents based on keywords.

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)

Cover of William Shakespeare

"What's in a name? That which we call a rose

 By any other name would smell as sweet"

Wrote William Shakespeare in one of his most well known play's "Romeo and Juliet" many centuries ago. But does the same apply to patent searching?

Names, and particularly technical words are particularly important when searching patents. For this reason a whole field had developed to improve searching using keywords, sometimes referred to as 'semantic searching". Keyword based searching does have a couple of important natural limitations to deal with though:

• Different patent applicants (or their attorneys) use different technical words for the same concept. For example, if we take the easy to understand area of patents for cardboard boxes, different applicants can use the technical words 'box', 'carton', 'container', 'package' and 'carrier' all to describe the same concept.
• A technical word or combination of technical words can include a wide variety of inventions. Returning to the world of cardboard boxes, there are thousands of different patents that cover different variations of cardboard boxes. However a patent attorney when drafting such patents is limited in the range of technical words they can use to distinguish one invention from another - for example 'top', base', 'side', 'flap' etc (and in fact every technical area has a limited set of technical words that are combined in different ways to describe different inventions). So a given patent search using a given combination of technical words can bring up a wide range of inventions.

Combining a keyword search with a International Patent Classification (IPC) or US patent classification codes can improve matters, but again limitations appear:

• A given IPC patent code can cover a range of a inventions within a given technical area.
• IPC codes can be imperfectly assigned, particularly when codes are translated from one classification system to another, for example from USPTO patent codes to IPC codes, or vice-versa.

But of course, we all know this. Anybody who has ever reviewed a list of patent search results would have waded through a whole set of mostly irrelevant patents before finding the patents they were looking for. Patent examiners can spend hours seaching for relevant patents, even using some very sophisticated patent searching tools.

This is an alternative though, and more and more people are using citation analysis to augment their keyword searches. During patent examination either patent applicants or examiner identiify similar patents, and the resulting patent citations are publicly available. The advantage of citation analysis is that links can be made between patents based on the inherant subject of the patents, i.e independently of the technical word used. A patent examiner instinctively knows that a 'box' is a 'carton' is a 'container', to give but one example. Similarly, patent citations are generally made to patents disclosing similar inventions, but not to every patent in the technical field. So inherently, using patent citations to identify similar inventions has the potential to bring a lot of precision to the patent searching task.   

However, even citation searching has its limitations:

• Some individual patents can have hundreds of patent citations, creating an additional large group of patents to wade through in order to find the patents you are looking for.
• Sometimes patent examiners, particularly US patent examiners, can identify an invention which is similar to the patent being examined, but which might be a missing a key feature (hence the patent being examined is novel over the prior art patent). The examinar then identifies a second patent disclosing the missing feature from what can be a different technical field, and then states words to the effect 'if you combine these two otherwise unrelated patents, the invention being claimed is not inventive". Besides being frustrating to patent applicants, this type of prior art analysis also creates what can be a citation link to a very different invention, in what can be a different technical field.    

Network Patent Analysis (NPA) uses a different approach to analysing citation connection. It looks for supporting evidence (similar citation links) to weight the importance of a given citation link. Weighting of citation links is then used to help identify clusters of patents of very similar subject matter, with the relative importance of these patents emphasised. Weakly connected patents (due to being in another subject matter area) are ignored. Results are presented in a visually insightful manner, as shown in the figure below. The visual cluster can even be used to suggest similar patents that are not directly citationally connected to the patent in question.

Figure: Example of cluster analysis from an upcoming NPA white paper on Alzheimer's patents. Another examples of cluster analyis, applied to smartphone patents, is found here.

This relieves the patent analyst, attorney or lawyer of the burden of having to wade through what can be hundreds or thousands of unrelated or only loosely patents. Instead they can concentrate on the most similar and most influential patents in the field, saving time and money, and reducing the risk, of missing key patents.  Or in other words, NPA brings a new level of both:

• precision (focusing in particular inventions) and
• generality (by bringing in inventions that can have different keywords or patent codes, and allowing objective analysis of tens of thousands of patents).

into patent searching.

Or in other words, we believe that the words of Shakespeare can be applied to patents - a patent by another (technical) name can smell as sweet... 

By citing prior art or related technologies, patent citations form the 'river beds' that connect the old with unchartered territories, across patent publications and non-patent literature alike. These evolutionary paths that fork and reconnect, that grow into powerful streams and at other points subside, tell the story of technological advancement and the flow of knowledge.

Automated and comprehensive processes such as NPA™ patent analysis augment a patent search result by also considering directly or indirectly related patents, that our proprietary process deems relevant. These patent trees are then agglomerated to form large networks of inter-related patents. This process is extremely comprehensive and will pick up on patents, that even a sophisticated patent search would have overlooked. Our research consistently shows that between 20% and 30% of the most relevant patents would not have been picked up in the initial patent search based on words, phrases and IPC codes.

An inherent quality of NPA™ patent analysis is the visual and insightful mapping of technologies and patents that cluster together based on the many citations they may share with others. Quite often these are not patents of the same patent family, or even same patent owner, yet NPA™ patent analysis recognised their similarility. Upon zooming into the NPA™ maps, the use of color codes and labels visualling the age of the patents can be used to show the development of a technology.

Further insights can be obtained by looking at the direction of citations, or we describe as the 'knowledge flow'. A citation reference from a later patent to an earlier patent brings with it the possibility that the later inventor built on or benefited from the knowledge disclosed in the earlier patent. This has applications such as illustrating the development of the technology, finding prior art, and predicting potential patent infringements.

As an example of this, consider Figure 1 which shows the very most central patents from an analysis of 60,000 patents in the hybrid car field. In this diagram, node colors differentiate between patent owners, with a special focus on two key actors. Green patents are from the US hybrid drive-train developer, Paice Corporation, and red patents are from Toyota. The label on each patent refers to the overall dominance of a patent in the hybrid car field, and it’s resulting rank within the owners patent portfolio, as well as the year of publication. For example, the P1(2000) patent designation indicates that this was the highest ranked Paice patent with a publication date in 2000. In fact this patent was the highest ranked hybrid car patent in the whole study, followed by the second highest ranked Paice patent, P2.

Note that the P2(1994) patent filed by Paice Corporation has arrows pointing towards the T4 and T5 patents for Toyota, both published in 1998, suggesting an apparent technology flow. While it is probably impossible outside of a court of law to prove that technology did flow from Paice Corporation, Toyota’s hybrid cars have been found in the US to infringe the Paice patent P2,US patent 5,343,970. This suggests that NPA™ patent analysis can be used to predict possible patent infringements. To confirm these apparent technology flows a review process using traditional and subjective infringement analysis methods is advised.

Figure 1. Apparent technology flows at the center of the hybrid car patent set

Doris Spielthenner