Patenting AI Models: Strategies to Overcome Subject Matter Objections
In celebration of Small Business Month, the Vector Institute for Artificial Intelligence, in collaboration with Smart & Biggar LLP, released a guide aimed at helping Canadian startups and research professionals navigate the complex intersection of artificial intelligence and intellectual property. The article addresses the significant legal challenge of patentable subject matter objections, which frequently hinder software and AI patent applications. It emphasizes that while generic automation of known processes is difficult to patent, inventions demonstrating clear technical character are more likely to succeed. The authors suggest using the 'Does an engineer think it’s cool?' test as a practical heuristic for assessing technical merit. To avoid being classified as abstract mathematical ideas, inventors must define a specific technical purpose, such as solving a computer problem or improving hardware efficiency. The piece provides concrete examples, including novel model structures like Recurrent Neural Networks with Auxiliary Sentinel Gates and specialized audio processing layers. By detailing the technical problems solved and the essential hardware or software components, developers can better secure patent protection. This analysis serves as a strategic resource for integrating IP into business strategies within the evolving AI landscape.
Wire timeline
Patenting AI Models: Strategies to Overcome Subject Matter Objections
In celebration of Small Business Month, the Vector Institute for Artificial Intelligence, in collaboration with Smart & Biggar LLP, released a guide aimed at helping Canadian startups and research professionals navigate the complex intersection of artificial intelligence and intellectual property. The article addresses the significant legal challenge of patentable subject matter objections, which frequently hinder software and AI patent applications. It emphasizes that while generic automation of known processes is difficult to patent, inventions demonstrating clear technical character are more likely to succeed. The authors suggest using the 'Does an engineer think it’s cool?' test as a practical heuristic for assessing technical merit. To avoid being classified as abstract mathematical ideas, inventors must define a specific technical purpose, such as solving a computer problem or improving hardware efficiency. The piece provides concrete examples, including novel model structures like Recurrent Neural Networks with Auxiliary Sentinel Gates and specialized audio processing layers. By detailing the technical problems solved and the essential hardware or software components, developers can better secure patent protection. This analysis serves as a strategic resource for integrating IP into business strategies within the evolving AI landscape.
Vector Institute for Artificial Intelligence