North America: Intellectual Property

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Intellectual property law and copyright law thought leadership, articles, podcasts, videos and webinars from expert sources across the legal world. Explore insights covering topics such as licensing and syndication, patent law, trade secrets and trademark law.
Article
Federal Circuit: ITC Experts May Rely On Source Code Not Admitted At Hearing
On May 11, 2026, the US Court of Appeals for the Federal Circuit issued a precedential decision in Bissell, Inc. v. International Trade Commission, affirming the ITC’s final determination in Investigation No. 337-TA-1304, preventing respondent Tineco from importing its original wet/dry surface cleaning device but allowing the importation of its redesigned devices. Among other issues, the court held that an ITC expert may rely on source code produced in discovery even if the source code is never introduced as a hearing exhibit.
United States IP
M
Mintz
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Patents
TD
Trademark Disputes
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Trademarks
PL
Patent Litigation

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Patent

Article
Federal Circuit: ITC Experts May Rely On Source Code Not Admitted At Hearing
On May 11, 2026, the US Court of Appeals for the Federal Circuit issued a precedential decision in Bissell, Inc. v. International Trade Commission, affirming the ITC’s final determination in Investigation No. 337-TA-1304, preventing respondent Tineco from importing its original wet/dry surface cleaning device but allowing the importation of its redesigned devices. Among other issues, the court held that an ITC expert may rely on source code produced in discovery even if the source code is never introduced as a hearing exhibit.
United States IP
M
Mintz
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Trademark

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Licensing & Syndication

Article
Critical Considerations For AI Model Licensing Agreements In Healthcare
AI licensing in healthcare involves complex decisions about asset definition, control allocation, and accountability as models and data evolve. This white paper examines the practical contracting challenges that arise when AI models intersect with health data, exploring how organizations can structure agreements that account for messy datasets, model artifacts, and the reality that machine learning systems resist traditional ownership frameworks.
United States Healthcare
FH
Foley Hoag LLP
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