Platform technologies and AI and machine learning (ML) hold great promise for addressing complex diseases. However, the patentability of these technologies remains uncertain, and ownership of the AI model or ML software, the data, and the end product must be carefully diligenced and their value critically negotiated. In this session, topics of discussion include:

• Identifying patentability and patent strategies for AI and ML innovations
• Asking where patent protection is the most needed
• Considering the timeline for patent approval against the rapidly evolving AI and ML landscape
• Analyzing novelty in a saturated space and how to distinguish from the prior art
• When does it make sense to generate your own data versus buying data
• Considering data bias and strategies towards ensuring the best data set is used
• Ensuring that the data is fully owned by the seller, can be used for the purpose you want the data for, and that no data privacy laws have been violated
• Best practices for negotiating ownership, inventorship, and royalties
• For IP generated through the project, rights to preexisting IP, and parties’ rights upon termination of the collaboration
• What if the output or discovery is more lucrative than either party anticipated?
• How to correctly name the inventors when multiple people are involved in developing and running the AI model or ML
• Considering exclusive and non exclusive licenses and assignments