Machine learning algorithms can, for example, spit out molecule combinations in the hunt for new drugs, map out schematics for novel chip designs, and even write code. She said Google had filed multiple patents describing a machine-learning technique used internally to automatically design and map out components in the company's custom AI accelerator TPU chips currently used in its servers. The uncertainty surrounding whether it's possible or how best to apply for patents protecting IP produced by algorithms can sometimes be a roadblock in developing new products, especially in the pharmaceutical and biotech industries. Companies that rely on using AI software to create new drugs or antibodies, for example, often need to secure patents before they can kick start clinical trials. "We at Google are definitely giving a lot of thought to the inventorship question overall… We're thinking through inventive contribution issues throughout the AI development process," Sheridan concluded.

The question of whether AI-generated outputs can be patented is impacting how technology companies can protect their intellectual property. Some of the most hyped up AI technologies are systems that can produce surprisingly creative outputs. Uncanny poems, short stories, and striking digital art have all been generated by machines. The human effort required to initiate these processes are often trivial: a few clicks or typing a text description can guide the machine towards producing something useful. Similar generative AI models are also being applied in scientific and technological applications.