What is the difference between Similarity and AmberScore again? What about Licensing Potential?

Here at Ambercite, when using Cluster Searching, we like to show in results two calculated numbers:

  • “Similarity”
  • “AmberScore”

And sometimes “Licensing Potential”

One of the most common quesitons we get is ‘what is the difference?’ And why are all important?

 

Similarity

Similarity is exactly that, the predicted similarity between two patents based on our citation analytics. While similarity of course is a self-evident concept, imagine it being applied to houses. 

Somebody might use a similarity metric to predict that 

b2ap3_thumbnail_House1a.jpg

filler

  is similar to            

b2ap3_thumbnail_House2.jpg

 

AmberScore

AmberScore is the concept that leads the most questions. Essentially we developed AmberScore because we recognised that when it comes to searching patent, not all patents are equal. Some patents are more important than others, and to pretend otherwise would be misleading.

A good number of analyts have concluded that the number of forward and backward citations are important, and we agree. We also think that citation high connections with high similarity are also important. 

But why? Any patent costs money to draft and file. So imagine a ‘widget X’.

Widget.jpg

Without knowing anyhing about the widget, it is possible to make a prediction of the value of the widget based on the number of patent applications for that widget. If there are lots of patent applications, this almost always means that lots of applicants have spent lots of money on filing these patent applications. Most likely, there was lot of research and development beforehand. Clearly, plenty of people think this area is valuable enough to invest in the patents and the research and development. 

And that is without knowing what the widget it – so it is a purely objective test.

So if you were file a half decent patent for the same widget X, it would over time attract a reasonable amount of prior art citaitons (a crowded field) – and in turn later applicants would cite your patent. Its AmberScore would increase.

I should note that we include one further component to AmberScore – based on the number of recent patent application that cite the patent in question. Why? If lots of people are filing patents in the same area, it is clearly a ‘hot’ area – people are voting with their patent applications.

So that is the premise of AmberScore. In addition we normalise it so that the average AmberScore for US patents is 1.0. Anything above 1 is above average.  And values can get pretty high – for example US patent 7663607, filed by Apple for a Multipoint Touchscreen (now that is a type of widget that has attracted a lot of patent filings!) has an AmberScore value of 96.1

I should note that it can take a few years for the necessary citations to appear to identify a high AmberScore. So very recent patents may not have high AmberScore values. But it does not take that long.

In housing terms, a high AmberScore patent could look like this:

Big-house.jpg

 

Licensing Potential

Lets say you were interested in the monetization potential of patent. We would say that you could for later patents that were both 

  • similar to yours
  • had high AmberScore values – as a prediction of importance of values.

These are both positive metrics, so it makes sense to combine them. We have done so, to create the metric “Licensing Potential” – which equate to the square root of Similarity * Amberscore – for patent that are found in an “After” search , as done in Cluster Searching.

And in real estate terms? It is hard to find an exact analogy, but consider the house shown to the left in the picture below:

Potential.jpg

 

The house on the right suggests that the area is gaining value, which is why the owner has invested heavily in the house. You would imagine that this would have increased the value of the house on the left – which is similar (in terms of location at least) to the high value house on the right. 

 

So what?

Ambercite has created these three metrics of Similarity, AmberScore and Licensing Potential because we believe that all patents are not equal. Some are more relevant to your needs and some are more important.

The alternative is to regard all patents connected by citation connections as being of equal relevance to your query – and all patents being of equal importance and relevance. 

Which is a premise that, quite simply, we reject. We can do better than this. 

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