Case Study | Mahle
12th March 2019 |
“Cipher Automotive makes unique use of artificial intelligence, and has excellent customer service which together improve the accessibility of complex patent data.”
Roger Gorges, Head of Innovation, Mahle
What is your role in Mahle and some of the challenges you face?
I’m Head of Innovation based in our UK Tech Centre and am responsible innovation for bearings and bushings. We’re part of the Engine Systems and Component business unit, the oldest and most established of the 4 units that make up the group.
Apart from the innovation role, I’m also a portfolio coordinator for what we call new products. This is an essential role at a time when we are undergoing one of the most radical changes in the role of an internal combustion engine that we’ve seen in the last 100 years.
How do patents form part of your role?
As part of my innovation role, I’m responsible for patents, which includes first filing and also maintenance and strategic consideration of competitor patents. We have to monitor these issues globally – not only Germany but the US, China and elsewhere. We are currently testing Cipher in different Tech Centres.
A key part of innovation management is to undertake patent monitoring of competitor patents. I issue an annual report analysing what our competitors and other companies of interest are filing, to give us an idea of where we can expect the competition to come from or what they will offer in the future. This competitor intelligence from patents is combined with other information that we collect from conferences, press releases, trade shows and so on.
Why did you select Cipher Automotive and how does it help?
I have known about Cipher for a few years now and what sets it apart from other patent software is the ability to build in artificial intelligence and so when we started to look for a new product I wanted to take a closer look.
One area that was particularly important is patent mining, especially in areas where we’re not active now and we don’t have like such an exhaustive overview and understanding of the markets. The idea in these new areas is to build on the core competencies we have: for instance, competencies in coating technologies or casting technologies or mass production.
Two things stood out when looking for new software. First, as I said, the technology, which is unique on the market and equally or even more important the support – you could really feel that at Cipher you don’t only want only to sell software and then job done. There is a lot of involvement from your automotive experts and from customer support. The fact that they are always reachable sets you apart.
We built into Cipher Automotive a comprehensive taxonomy of technologies, how does this help?
The taxonomy offers a good, ready entry point for us. In most cases, we don’t have to even narrow it down and in some cases, we work with your support team to customise the classifiers or build some of our own.
Another important aspect of the automotive package is the organisation grouping – being able to instantly search by reference to OEMs or top suppliers. Using standard approaches to patent search, the results include so much you don’t want to see. This helps us to quickly identify the patents that matter.
Our mission is to replace Boolean search with automation – will that save you time?
Yes, and it is certainly a better way to get relatively quick overviews or patent landscapes in a matter of hours rather than weeks.
The time and money consumed in outsourcing patent landscaping can be considerable. Cipher can really help to either replace that or at least handle the initial patent landscape – there’s a lot of speed and time gained by automating the process.
Do you think that by making patent landscapes more accessible this will increase the demand for and use of this information?
Definitely – the clustering algorithms and drill down tables make it much easy to analyse the patent landscape especially in fields which are new to you. For the last 10 years, we have focussed on a handful of major companies – and often the same inventors. We now need to move outside our comfort zone, as we engage with innovation outside the engine. It becomes fuzzier and it helps a lot to have trend data and indicative graphs – it’s just a more intuitive approach and interface.