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Claim Differentiation Doctrine Fails To Trump Interpretation Supported By Intrinsic Evidence

Client Alert | 1 min read | 05.23.06

In Inpro II Licensing, S.A.R.L. v. T-Mobile USA, Inc. (No. 05-1233; May 11, 2006), the Federal Circuit affirms a district court's claim construction and holding of non-infringement.  The claims at issue are directed to a digital assistant module, which includes a host interface.  The district court held that both intrinsic and extrinsic evidence limited this interface to a direct parallel bus interface, even though this limitation is not specifically recited in the claims.  The patentee objected, contending that because other, unasserted claims specifically limit the host interface to a direct-access parallel bus the doctrine of claim differentiation requires the recitation of the host interface in the claims at issue to be interpreted more broadly than a direct parallel bus interface.  Like the district court, the Federal Circuit, however, disagrees.  Noting that the patent specification disparages the serial interface that the patentee asserts the claims cover, identifies the direct parallel bus interface as a “very important feature,” fails to describe any other type of bus for the host interface, and describes a serial connection for a different bus, the Federal Circuit holds that employing different words to describe the same element does not necessarily change the scope of the claims where the surrounding evidence fails to support different interpretations.

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Client Alert | 7 min read | 09.14.26

AI in Life Sciences: Ten Legal Considerations and Risks of AI Use in Drug Discovery and Development

Over the past several years, the biopharmaceutical industry has embraced artificial intelligence and machine learning (AI/ML) in near lockstep with the pace of AI/ML innovations. Today, industry leaders are using AI/ML to, among other things: discover and assess biological pathways, target chemical structures and sequences; design proteins; model pre-clinical and clinical trials; recruit and screen potential patient populations; evaluate clinical trial results and biomarker data; prepare regulatory filings; and manage supply chains. Deployment of new AI/ML models promises extraordinary advances in pharmaceutical development. However, as with any technological and scientific advances, the use of AI/ML also poses substantial legal risks that life sciences companies need to consider and proactively manage. ...