Transition Document for the United States Patent and Trademark Office
Summary
This transition document provides over 25 actionable recommendations on the future of the United States Patent and Trademark Office (USPTO), in order to support future federal leadership and enable their success. The document is the result of collaboration between the Day One Project and a group of veteran policymakers who convened virtually to produce recommendations related to the following three categories:
- Identifying specific policy and governance ideas that can be pursued in the first days and months of the next administration.
- Gathering “lessons learned” from those who have previously served in government to learn from past challenges and better inform future initiatives.
- Understanding key science and technology staffing and “talent” needs, and related challenges for the USPTO that can be addressed in the next administration.
The document also includes a cover memo which highlights some of the overarching key considerations for the future of the USPTO.
Contributors
- Margo A. Bagley
- Sharon Barner
- Brian Cassidy
- Colleen V. Chien
- Mark Allen Cohen
- Ayala Deutsch
- Ben Haber
- Philip G. Hampton
- Justin Hughes
- David J. Kappos
- Quentin Palfrey
- Arti K. Rai
- Teresa Stanek Rea
- Robert L. Stoll
- A. Christal Sheppard
- Saurabh Vishnubhakat
- Stephen Yelderman
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
The clearinghouse will also neutrally mediate the assignment of auditors to audits—strengthening independence, trust, and ethical governance across the AI ecosystem.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
Jobseekers, employees at smaller organizations, and local public workers need more than a weekend crash course in AI. They need practice choosing useful tasks, protecting data, testing outputs, and explaining where human judgment remains necessary.