Summary - Smart patent owners are long used to the concept of monitoring their competitive landscape by monitoring new patents. However existing methods could produce either too much data, or not enough. Ambercite now has the capability to provide a sophisticated and yet to use list of new and relevant patents similar to those of a portfolio, or even a single patent.
In this case study, Ambercite was used to produce a list of similar patents to the patent porfolio of Strava, all filed in the last five years. No risky assumptions about keywords, patent owners or class codes were required - and this analysis returned 281 patent families - as opposed to the 4 patent families that would have been returned by conventional citation monitoring.
Why should patent owners monitor other patents in their areas?
Patent owners have long known that new published patent can be a valuable source of information about their known and not-yet recognised competitors:
- what these competitors are developing
- who could they partner with
- which of these competitor's represent a legal issue and should be challenged.
Previously, patent owners could monitor new patents in their field by one or both of two methods:
- By running conventional queries based on the likes of patent owner, keywords and patent codes. These can work, but equally can create a large amount of often not relevant results, thereby creating a lot of work - and sometimes this means that the technology scanning would not be done.
- By looking for new forward citations from the patents in the portfolio. This can also work, but is dependent on the examiner or applicant recognising the relationship between the forward citation patent and portfolio patents. This may not happen all of the time - and hence relevant and newly filed patents can be missed.
So patents owners could end up with either too many patents to look at - or not enough (thereby key patents could be missed). Clearly, an improvement is required.
Ambercite is continually updating its processes, and recently has improved that they way we manage recent patent applications. This now makes it an excellent tool for technology monitoring, as it can monitor both known citations - and 'unknown' citations.
'Unknown' citations, in this case, are potentially relevant patents that are not yet recognised as citations - but which are found when our algorihms are applied to our network of 53 million patent families and 156 million patent citations.
And the benefit of our algorithms is that, when compared to conventional or semantic searching, the results delivered are much more precise - thereby saving your valuable time by ignoring irrelevant patents. And you can avoid the forced errors of running searches based on what could be quite risky assumptions about keywords, owners or class codes.
So how does this work in practice? The case study below might help explain this.
Case study on Strava patent portfolio
Strava is an app that can allow users to record their cycle or running trips with their smartphones, and then compare their performances with their friends and other people who use the same routes, as shown in the image below.
A search on Patentlens suggest that Stava have 45 patents in their name, falling into 7 patent families. We entered these 45* patents into Ambercite Cluster Searching, and looked for similar patents filed within the last five year, using the simple query shown below (only some of the Strava patents are shown in this list).
* Patent queries can contain anywhere between 1 and 200 starting patents, depending on your requirements.
This produced 281 results, representing 281 patent families - the five most similar results are shown below
These show the most similar patents. We can also look up the most recent applications, as shown below. Not surprisingly, at least one of these patents relates to vehicle analysis, but again this is route tracking. It is also interesting that two of these recent patents are from China, and one from Germany.
Note too that the results are a mixture of known and unknown citations. In fact, if we look for known citations filed in the last 5 years, there are only 4 such direct citations found - which clarifies the limitations of the normal citation analysis offered by other vendors.
In contrast, we also found 275 unknown citations, many of which may be directly relevant to Strava, such as the 5 most similar unknown patents shown below:
This now helps Strava identify some of the most similar and recent patents. But who is filing these patents? This are no surprises here, as shown in the figure below which counts up the number of patents filed the most prolific patent filers in the results. We produced this graph after downloading the table of results into an Excel spreadsheet.
This simple case study show how it easy it is now is to monitor the technology landscape using Ambercite. All you need is a list of your patents - this can be the whole of your portfolio, part of your portfolio or even just one or two of your patents. The patents returned by this process can be highly relevant to your company's objectives, and be
- Much more precise than conventional patent searching - saving you the time and expense of reading irrelevant patent publications
- Much more data rich than conventional citation searching - if conventional citation searching had been used in this case study, it would have returned just 4 recent and similar patent families - as opposed to 277 patent families generated by Ambercite - as suggested by the image below.
- Free of any errors caused by assumptions of what owners, keywords or class codes the founds patents should fall into.