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Disclosed Species Within A Count Establishes Constructive Reduction To Practice

Client Alert | 1 min read | 09.20.07

A Federal Circuit panel in Frazer v. Shlegel (No. 06-1154; August 20, 2007) reverses the Board of Patent Appeals and Interferences award of priority and remands the case to the U.S. Patent and Trademark Office. The Board held that Frazer was not entitled to the benefit of an early Australian application because the disclosure “did not provide a described and enabled anticipation under 35 U.S.C. § 102(g) of the subject matter of the count.” The Federal Circuit reverses, finding that Frazer is entitled to the priority date of the Australian patent application. In particular, the Federal Circuit determines that “the Australian application contained complete details of the method that is the subject of the interference count, and depicts the [virus] particle of the count with full disclosure of how to produce it.” As such, the panel concludes that the Australian application’s “description of the procedures used, and the successful production of the virus-like particles there achieved and reported, disclose and enable a species within the count.” Thus, the Board erred in ruling that Frazer’s Australian application did not prove conception of a species within the count. Frazer’s Australian application was thus a constructive reduction to practice of the invention and Frazer is entitled to the priority benefit of the Australian filing date.

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Client Alert | 9 min read | 10.21.26

Artificial Intelligence in Life Sciences: Reliability of AI-Generated Evidence in Drug Discovery and Development

The life sciences sector is uniquely vulnerable to the inherent risks of AI-generated evidence in drug development and discovery. This client alert focuses on five core challenges: hallucinations, skewed predictions from unrepresentative training data, the opacity of AI reasoning, input sensitivity that undermines reproducibility, and adversarial manipulation of training data. To address these risks, companies should use experimental validation and human oversight as the common legal and regulatory safety net. This entails among other measures auditing AI outputs, characterizing training data, and structural oversight. Companies also should prepare for evolving frameworks and guidance from the U.S. Food and Drug Administration (FDA) and the European Union (EU) that address use of AI in drug discovery and development .  ...