AmberBlog

Ambercite's Ben Palmer, our Director of Software Development, presented on 'Big Data" to the Churchill Club this week (a group focused on IT and startups). Ben was standing in for our Managing Director Doris Spielthenner, who was unavoidably detained elsewhere working on another big data project for the Australian government.

Ben was one of three excellent speakers. Ian Holsman, consultant & Former Chair of the Apache Hadoop project, spoke about the work he had done on data mining for a large consumer news website. This at one stage included real time feedback from the newspieces they were posting on the website, allowing the website to fine tune articles of interest for their audience.

Peter Buckingham from Spectrum Analysis Australia spoke the work he did in predicitng the best location for new franchise outlets, based on sophisticated data analysis. He also discussed a recent court case in Australia, where a franchisor was successfully sued by a franchisee over misleading representation made by the former over likely sales for a new franchise. When asked on what basis these predictions were made, the franchisor admitted to 'scientific guessing'. The judge was not impressed.

Ben spoke about some earlier work he and his team had done for the Australian government on predicting air traffic movement all over Australia, and in particularly deviations from expected movements, in real time, i.e. while these planes were in the air. Ben also previewed recent developments in our patent searching software. 

Despite the variation in areas of interest, some common themes emerged from all three speakers and the followup discussion:

• The real value in big data is not the data itself, but what we do with it. Visual presentation of the resulting analysis can help end users make the most of this value.
• Data quality, even from reported reliable sources, can be very patchy. There will be lots of hole. This in turn creates opportunities to identify this missing data, which can be very important.
• Timely analysis can be very important

All of these concepts have been taken on board in our development program:

• We have been told that our visualisations are the best in the business, but we aim to go beyond anything we have done before. 
• Like others, we also need to clean up the data we are using. We are tackling the problem of missing data in ways that we think will surprise and delight many people. Certainly our internal panel of patent attorney testers are more than excited by what we are showing them, and the additional information being made available when compared to other patent data sources.
• And while we don't necessarily need to process patent data faster than planes can fly, we understand that clients want results in real time when use patent analysis software, and that new patent data is published every day that our clients will want to know about.

Interested in knowing more? Come back to us, and we will keep you fully informed about future product releases.

I am lucky enough to have recently returned from the PIUG Conference in Denver, where I presented on what Ambercite was doing. While I was there I listened to some excellent presentations, met some great people, and come away feeling positive about the future of patent analytics, and the role of Ambercite within this future. 

Among the most thought provoking of these presentations was by Barry Brager, titled "If You Can't Use It, Why Do It? Tailoring Client-Specific IP Analysis & Visualization". This presentation should be published by PIUG in due course, but in short Barry raised ten challenges to suppliers of patent analytics, and challenges which we are certainly aware of. Among these challenges is the issue of making the results as easy to understand as possible. 

Patent analytics is not the only field where the need to understand is paramount. On the same day that I presented in Denver, Gizmodo published an article titled: The Problem With Big Data Is That Nobody Understands It. This included the following quote from Hilary Mason, chief scientist for the URL shortening service Biy.ly:

a data scientist must have three key skills. "They can take a data set and model it mathematically and understand the math required to build those models; they can actually do that, which means they have the engineering skills…and finally they are someone who can find insights and tell stories from their data. That means asking the right questions, and that is usually the hardest piece."

While patents analytics is not big data per se, there can be some significant parallels, including the requirement to process large amounts of data to come up with a usable result in a meaningful time. Ambercite recognises and is responding to the types of challenges discussed by Ms Mason, including the last of these challenges. In particularly, the product development program of Ambercite is focused on developing products that present data in an easy to understand and intuitive fashion as possible, so that the stories in the data stand out, and without a large amount of time being required to run the studies.

Figure: Showing strong forward citation relationships from the highest rated Paice patents to highly rated patents from Ford and Toyota, and in turn suggesting a infringement risk which has been confirmed in litigation between Paice, Ford and Toyota. Taken from data compilled in our hybrid car patent study.

Ambercite looks forward to unrolling out some of the results of this work in the near future, including a new concept, building on the NPA principles, that it is both simple and yet provides a powerful new perspective on patent searching. We invite interested parties to register their interest with us, so that they can be among the first to gain access to the new tools