Know your competitors : Case study on Strava patents (Revisited)
April 02 2024 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 utilized to produce a list of similar patents to the patent portfolio of Strava by running probability on the network of first and second-degree citations using AI. No risky assumptions about keywords, patent owners, or class codes were required. Unlike traditional approaches that rely on keywords, semantic analysis, or class codes, Ambercite's AI-driven probability analysis focuses solely on the network of citations.
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 uses and AI approach for finding similar patents to one or more patents. This 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 million patent families and 180+ 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 Google Patent suggests that Strava currently has 22 patents to their name, indicating a slight decrease from the previous count in 2017, and falling into 10 patent families.. We entered representative family members from these patent families into our patent search software Ambercite Ai, and looked for similar patents filed within the last five year, using the simple query shown below.
Note that we are running a 'licensing' search, one of three options we have for patent searching. The reason why we did this is that results are listed with the licensing index for each result, which can help when reviewing these patents.
This produced 500 results, representing 500 patent families - the five most similar results are shown below
We can also look up the most recent published applications, as shown below. All five of these patents have some relationship to the Strava technology and being published by Apple.
Note too that a lot of results are 'unknown' citations - which in fact are not direct citations, but instead similar patents which are AI algorithm suggest might be similar to the Strava patents but which have not been directly cited - hence they are unknown.
This distinction is important. If we look closer at the data, in fact there are only 129 known citations in this list - and 371 unknown citations. So if were to look only at known citations, a huge amount of potentially valuable data would be missed.
This now helps Strava identify some of the most similar and recent patents, whether they have been picked up as direct citation connections or with a high probability from their indirect citations. But who is filing these patents?
In our exploration of the competitive landscape surrounding Strava, we present two tables detailing essential aspects of patent ownership. The first table offers a detailed breakdown of licensing potential by summing the licensing index for each patent owner, providing valuable granularity for competitive analysis. Meanwhile, the second table offers a straightforward comparison by counting the number of patents held by each owner. Both tables contribute to our understanding of the competitive environment surrounding Strava, offering insights into the breadth and depth of patent ownership among key players in the industry.
The significance of licensing potential cannot be overstated. Icon Health and Fitness, with its dominant market share and unwavering focus on fitness equipment and technology, serves as a prime example. Through our patent analysis tool, we've unearthed compelling evidence showcasing Icon Health's superior licensing potential compared to industry giants like Apple. This insight underscores the invaluable role our licensing potential index plays in accurately gauging the market strength and growth prospects of companies operating within specialized niches.
Discussion
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
Free of any errors caused by assumptions of what owners, keywords or class codes the founds patents should fall into.
Note - This blog is an updated version of a similar blog first published in May 2017. We have updated this to demonstrate some of the new features in Ambercite since May 2017..