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Cluster Searching has been designed by patent searchers, and is aimed at patent searchers - in particular as a  "Second Opinion Engine"  - where it uses advanced network citation analysis to find similar patents to the patents you are looking at.

There are a number of applications for this, including in patent litigation. There are of course many ways of looking for prior art, including the 'crowd-sourcing' techniques developed by the likes of Article One and others. So we wondered - how would the improved version of Cluster Searching work on a prior art study promoted by Article One?  

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5

Patent Searching can be a deceptively difficult, and yet vitally important exercise. There are over 70 million patents out there, and while there are very powerful keyword and class codes search engines that you can use, the results are only as good as:   

  • The keywords and class codes that you search by
  • The keywords and class codes used by the people who filed or examined the patents you are looking for. No matter how good your choices are, you are also reliant on these other people.

Millions or even hundreds of millions of dollars may depend on the outcome of patent searching -  due to the risk of unwanted litigation or wasted R&D and commercialisation costs. Clearly as patent searchers  we need to do the best possible search in the time that we have available. The same applies to patent examiners - the right search results can have large economic consequences on both the patent applicants and their competitors.

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6

Since its release in 2015 Ambercite’s Cluster Searching tool has picked up a wide range of users around the world who are impressed by its ability to quickly find patents missed by conventional searching processes. It has been used in over 6000 searches to date. 

These users have also provided some very valuable feedback to Ambercite, and part of this feedback is that these users sometimes end up seeing patents they have already seen in previous searches. This can slow down the reviewing of the results that Cluster Searching provides,  and can be inefficient.

It is of course impossible for us to know what patents that our users have already seen – but they will probably know this themselves. For this reason Ambercite has just upgraded Cluster Searching with a new feature that allows users to ignore previously seen results from the results that Cluster Searching shows. This operates through a second data entry box, shown below.

IgnoreBox2.jpg

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A client recently asked 'can you provide some examples where Cluster searching efficiently identified relevant citations, which were not identified using existing tools?'.

This is a great question. I was running a demonstration case the other day, and this was a perfect example.

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What is a super patent?

Back in 2008 Parchomovsky and Wagner of the University of Pennsylvania published an excellent paper "Patent Portfolios', found here. In this paper they introduced the concept of a "Super-Patent", which they defined as:

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