John Oliver did a brilliant and hilarious takedown of patent trolls on his April 19, 2015 edition of his Last Week Tonight show. He raved about the absurdity of such companies who buy up patents and yet produce nothing much themselves other than lawsuits to enforce theses patents. As he said, this is a form of “extortion” that impedes progress and ends up costing the defendants in these actions a great deal of money. If you did not see the show or have not seen the video yet, please have a look and a laugh.
Then compare and contrast that economic fear and needless cost of using patent data in such a negative manner with the publication of a paper last week about how US patent filings are now being used in an entirely opposite, innovative and productive manner. The contrast could not be more dramatic. Indeed, as presented in a new paper published online on April 15, 2015 on PLoS One entitled Quantitative Determination of Technological Improvement from Patent Data, by MIT researchers Christopher L. Benson and Christopher L. Magee, mining recent filings in the US Patent and Trademark Office’s (USPTO) massive database using their new methodology, can determine which technologies are genuinely advancing and at what relative rate.
This very exciting news was reported and analyzed in an article posted on Phys.org on April 15, 2015 entitled New Method Uses Patent Data to Estimate a Technology’s Future Rate of Improvement. I will sum up, annotate and add a few questions to this. I highly recommend clicking through on both this article for the details of how this prediction tool was developed and the full-text of the PLoS One paper for the granular details of how it actually works.
(For cross-reference purposes, this advancement follows on and partially mixes together the topics of two previous Subway Fold posts, one on April 9, 2014 entitled Comprehensive Visualization of Future Paths of Technological Innovations and another on August 8, 2014 entitled New Visualization Service for US Patent and Trademark Data.)
Benson and Magee have devised an analytical means to sift through the USPTO database for precisely choosing the latest patents that “best represent” 28 specific technological domains. These include, among others, “solar photovoltaics, 3-D printing, fuel-cell technology, and genome sequencing”. Then, applying their methodology, based upon the number of subsequent citations in other new patent filings, they were able to determine those some of the relevant patents displayed an increased likelihood in predicting “a technology’s improvement rate”. In effect, the higher the rate of subsequent citation of Patent X, the higher the rate of innovation. The equations in their predictive tool also include some other patent characteristics.
Among the 28 technologies, those showing the highest rates of advancement were “optical¹ and wireless communications, 3-D printing, and MRI technology²“, while others with slower rates of advance included “batteries, wind turbines, and combustion engines”.
Benson believes that his prediction method could possibly be useful to venture capitalists, startups³. Magee hopes that it may be applied as a form of “rating system” for investors searching out potential “breakthroughs”. Both developers also foresee the possibility that public and private laboratories could use it to investigate potential new areas for research. Furthermore, Magee believes that their approach can be applied to lower the level of uncertainty about the future of a particular technology to “a more manageable number”.
My questions are as follows:
- Would the accuracy of the predictions from this new system be enhanced by applying its underlying equations to add in other data sources such as online news, social media mentions, and citations to other relevant industry news publications? (X-ref to the December 2, 2014 Subway Fold post entitled Startup is Visualizing and Interpreting Massive Quantities of Daily Online News Content.)
- Could the underlying equations be applied to other fields such as law to predict the possible outcomes of cases based upon the densities and propensities of cases cited in similar matters and jurisdictions? What about possible applications in medical research or the financial markets?
- Can levels of probability be quantified with this new system? For example, can it derive a 70% probability that driverless cars will continue to gather technological momentum and then commercially succeed in the marketplace? If so, how might such probabilities be used by the public, governments, researchers and investors?
1. Could references to patents for optical technologies also be considered, well, cites for sore eyes?
2. X-ref a September 3, 2014 Subway Fold post entitled Applying MRI Technology to Determine the Effects of Movies and Music on Our Brains.
3. There are currently 22 Subway Fold posts on a broad range of startups in many industries.