Summary
Google has been trying to get their batch normalization technique for training DNN’s patented since 2015.  Earlier this year the US-Patent Office denied that application after extensive thought and review. They cited 14 prior-art references in an unusually long 58-page response.  You can read a legal-dissection of that rejection here.  That did not disway Google from still pursuing it and resubmitting - It’s currently being reconsidered.

Commentary 

Patenting algorithms is like patenting salt in the kitchen.  If this trend continues, we may see ourselves needing to obtain licenses for implementing algorithms or end up in a headache akin to what Oracle did with Java to everyone. Google has apparently not sued over IP before though except in the Lewandowski case, and some may think they are acting as a benevolent overlord by obtaining the patent before a patent troll files it.  It might be furthermore difficult to prove when an algorithm has been trained with batch normalization, reducing the success rate of IP infringement. Either way, the risk of patents in this space could slow down development of new algorithms if it is hard to build upon previous ones, or it could frighten business stakeholders from implementing ML if they perceive an infringement risk.

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