Active monitoring of your competitor's patents - how to find out what others don't know. Case study on Uber.
Ambercite is a unique tool developed to find what can be very similar patents to one or more starting patents. It does so by applying a very sophisticated algorithm to patent citations.
Other patent databases also list citations, if in a much less sophisticated manner, and among the services offered is the ability to monitor recent citations to a patent portfolio.
But Ambercite offers a much more sophisticated approach - and an approach that can find recent publications of similar patents that would be missed by conventional citation analysis.
As an example of this, consider Uber. A patent search in Patentlens showed that Uber have 507 patents in 187 patent families. This patent listing is all that is needed to follow the activities of their patents.
To demonstrate this, we downloaded representative patents from each family, and entered this list of patent number into Ambercite - and so set up a query so that it would return patents published in 2017. The resulting query looked like this
Although only 5 patents are shown in this iimage, this query does include 198 Uber patent numbers.
This query is pretty self-explanatory, apart from the 'validity' search option, shown in red - choosing a 'validity' search will lead the listing of publication dates in the results.
This query returned 42 patents, i.e. there were 42 patent families found that were predicted to be similar to these 198 Uber patents, and which were published after 2016.
The three most similar of these patents are shown below, as ranked by our Similarity Score. All three patents are relevant to Uber (click on this table for an enlarged view).
OK, this becomes a simple way of analyzing a large patent portfolio, a query that took seconds to run. But there is an added benefit.
If we look at the above results, they are all known results - i.e known citations. And of these 42 citations found, 12 are known.
There are also 30 citations ( i.e 71%), which are not known citations (they are instead predicted citations). And some of these 30 citations are very relevant indeed, as shown below:
If we look at these three patents, there are both highly relevant to Uber, and yet would have been missed by conventional citation analysis. And all of this valuable information was found via a process that took seconds to run, and yet produced highly relevant information.
Interested in following the activities of your competitors in a simple and efficient manner? If so, we recommend contacting us, at the details below, about a demonstration and free trial of Ambercite Ai.