Tikalon Blog by Dev Gualtieri

Patent Mining

January 22, 2018

Science has two competing ideals. While replication of experiments is important, you're not going to get ahead in your field by duplicating other people's work. Scientific papers are expected to have a long list of references, presumably the result of a thorough search of the topic area, lest you "reinvent the wheel." Unintentional duplication of something that's already been done is wasteful of your time and your funding agency's money. Software development takes a lot of resources, so code reuse and free and open-source software (FOSS) are examples of using the wheels that have already been invented.

In the distant past, before the Internet, doing original research merely meant carefully scanning the one, or two, topical journals in your specialty, attending one or several topical conferences each year, and maintaining contact with a small group of scientists who work in your niche area. I did all these when I was active in research on magnetic materials. However, today, things are more difficult. There's a plethora of online materials to scan, most of which are found, at best, to be not that relevant; or, at worst, completely wrong. There's also Google, which gives way-too-many leads, and commercial databases.

While every scientist wants to do research in exciting topic areas, "hot" research areas come and go. The first decade of my tenure in corporate research involved magnetic bubble memory materials, a hot research area at that time. Attendance was huge at conferences at which magnetic bubble research was presented; but, alas, semiconductor technology superseded any advantage that magnetic bubble memory offered, and I needed to migrate to other research areas. If I had learned anything from my undergraduate economics course, there's a point at which buggy whip manufacturers need to quit.

A magnetic bubble memory module.

This memory module, apparently date-coded for March, 1983, was manufactured by Intel, which believed at the time that magnetic bubble memory would be an important technology.

(Photo by the author, via Wikimedia Commons.)

In the past, when there was no Internet and all books were collected in libraries, you could get an idea of what scientific topics were popular at various times by counting books and journal articles on those topics and graphing the numbers by date. Such insight into scientific and cultural trends became much easier in 2011 with the introduction of a new data mining tool from Google Labs called the Ngram Viewer.[1-2] The Ngram Viewer was developed over four years by a huge team of researchers from many departments at Harvard University, and from Harvard Medical School, MIT, Google, Inc., Houghton Mifflin Harcourt (the American Heritage Dictionary people) and Encyclopaedia Britannica, Inc..[1-2]

The Ngram Viewer team assembled a corpus of about 4% of all books ever printed, and they developed software for easy analysis of this database. The Ngram Viewer is essentially a concordance of a random selection of 4% of every published word, and the project has its own website, www.culturomics.org. Since this is a concordance, this database of 500 billion words collected from 5,195,769 books, is copyright-free. It should be noted that the words are from books, only, since the dating of periodicals in Google Books was very poor. The database extends to the year 2000, only.

I've written about the Ngram Viewer in a few previous articles:

• Culturomics, January 13, 2011
• Physics Top Fifty, July 19, 2011
• Word Extinction, August 17, 2011
• Modeling Scientific Citation, December 16, 2011
• Plotting Emotions, April 10, 2013
• As You Write, So You Are, August 21, 2013

The following graph shows how useful the Ngram Viewer is in determining historical trends in science. In this example I queried the Ngram viewer with typical terms from chemistry (phenol), nuclear physics (nuclear reaction), and ubiquitous computing (microprocessor). The graph seems to mark the domains of the chemical age, the nuclear age, and the information age. Note that you need to be careful in your word selections. Just using nuclear will give you biology papers as well. The nuclear age doesn't appear all that important by this analysis, but you would likely need to average many terms to get the true picture.

chemical, nuclear, information ages (Google Ngram Viewer

The chemical, nuclear, and information ages, as determined by the Ngram Viewer. While the database stops at the year 2000, it's likely that the next age will be the age of artificial intelligence. (Graphs from the Ngram viewer, redrawn for clarity using Inkscape. Click for larger image.)

While it's entertaining to see historical trends, most people, especially entrepreneurs and venture capitalists, are interested in future trends. Artificial intelligence (AI) has become more important, but what topics in AI, for example, are worth a scientist's time and a venture capitalist's money? That's the problem addressed in a recent arXiv paper by researchers at the University of Electronic Science and Technology of China (Chengdu, PR China), the University of Fribourg (Fribourg, Switzerland), Bern University Hospital (Bern, Switzerland), the University of Bern (Bern, Switzerland), and the University of Oxford (Oxford, UK). Instead of an analysis of journal articles or books, this research team looked at United States patents, specifically how they are interconnected via citations.[3]

The importance of a scientific paper is judged by its citation impact; that is, how often it is cited in other papers. Albert Einstein's papers have been cited 111,939 times.[4] Einstein's most cited paper is his thought experiment on quantum mechanics, now called the EPR Paradox, that he co-authored with Boris Podolsky and Nathan Rosen.[5] The EPR paper has been cited 16,150 times.[4] Patent documents also include citations to previous work, including previous patents, to provide evidence for the novelty of the invention.

Do you see a trend? US patents granted each year from 1790-2008. (Wikimedia Commons image by Spitzl, modified for clarity using Inkscape)

As a cited inventor on many patents in my corporate research career, I know from experience that few patents make enough money to even cover the cost of the patenting process, and fewer still describe world-changing technology. Just a small fraction of patents represent important technological advances.[3] The objective of this study was a metric that allows for early identification of technologies, such as the smartphone, that lead to radical social changes. The trial metrics were tested using a list of historically significant patents.[3]

While it's known that, on average, important patents tend to receive more citations, the correlation is noisy and not that predictive.[3] While statistical analysis demands a large number of citations for best estimates, accumulating citations takes time. Statistics might be fine in historical studies of the importance of older patents, but not that useful for detection of recent important patents.[3]

As they say, "You're known by the company you keep," and that's the idea behind the PageRank concept used by Google in Internet search.[5] In the patent context, the idea is that important patents will be cited by other important patents.[3] It's found that highly-cited patents tend to cite other highly-cited patents, but one problem with both PageRank and simple citation counting is that they are biased towards old patents, and we're really interested in recent patents.[3] To remedy this, the research team used an "age-rescaling" approach that removes the age bias for simple citation counting and simple PageRank (see graph).[3]

Removal of age bias of algorithms for discovery of recent important patents.

Legend: Citation count (c), PageRank score (p), age-rescaled citation count R(c), and age-rescaled PageRank R(p).

(Fig. 2 of Ref. 3, via arXiv)

This study suggests that important patents build on other other important patents, the typical "standing on the shoulders of giants" effect.[3] There is some noise in the system caused by the fact that patent examiners add their own citations.[3]

Patent Mining

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