Leading Within Digital Worlds by Grindrod Peter;

Author:Grindrod, Peter;
Language: eng
Format: epub
Publisher: Emerald Publishing Limited
Published: 2019-09-25T00:00:00+00:00

Protecting the Secret Sauce

One of the problems facing many companies is protecting their intellectual property so that rivals cannot simply “me-too” it, and there is a barrier to market entry. This is especially true when you are in start-up mode and seeking some equity investment. I have found that UK venture capital firms are especially risk-averse, “Where is your patent, where is your molecule, where is your widget, where is your gene?”. When I reply that we have algorithms and analytics they look unsure and disbelieving, having been previously fully assured by the wonderful IP protection available within, say, the biotech sector. In mathematics and analytics there is no such general protection available.

Twice, once in the United Kingdom and once in the United States, I was part of ventures that sought to protect our own proprietary methods with patents. Both failed despite spending tens of thousands of pounds/dollars on the patent lawyers (who always hold out hope – they have nothing to lose except your business so they tend to farm you).

The problem is that patents really protect invented objects that enable novel actions, so you have to argue that the outputs from your analytics (itself a data object) has unique properties not already available. How you actually get from inputs to such an output cannot be protected and is really just providing some assurance that the output data object has the desired, claimed, properties.

There is a number of alternative routes to protection for data scientific methods/applications. These include patents, for new inventions, that will be geographically limited; the use of copyright, for original creative work including written software code, that is available automatically; and via trade secrets, for valuable information and know-how, that is not known to the public, and which will require a fair amount of internal effort.

Of course, it is reasonable for investors and shareholders to expect the members of the data science team flags up potentially proprietary IP and should not inadvertently publish such ideas and know-how until any preferred route to protection is planned.

In general, patents could be available for inventions that:

are new and have not been described nonconfidentially before;

are inventive and not obvious modifications of some prior art;

are capable of industrial application;

do not fall within certain statutory exclusions.

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