1. Home
  2. |Insights
  3. |Indefiniteness Determined In Context Of Entire Specification

Indefiniteness Determined In Context Of Entire Specification

Client Alert | 1 min read | 01.31.06

In Energizer Holdings, Inc. v. International Trade Commission , (No. 05-1018; January 25, 2006), the Federal Circuit reverses and remands the International Trade Commission's holding of invalidity for indefiniteness. The claims at issue for zero-mercury-added battery cell recite “an anode gel comprised of zinc as the active anode component, wherein … said zinc anode has gel expansion of less than 25% after being discharged for 161 minutes to 15% depth of discharge at 2.88A.” The Commission held the claims indefinite for lack of antecedent basis for the recitation of “said zinc anode” and requiring every cell to meet the specified discharge parameters, whereas the discharge parameters are intended to apply only to a test cell.

The Federal Circuit begins its analysis by recognizing that an analysis of claim definiteness “focuses on whether those skilled in the art would understand the scope of the claim when the claim is read in light of the rest of the specification.” The Federal Circuit notes that the Commission and the Intervenors did not argue that they did not understand the claim scope because of the lack of antecedent basis. Concluding that the claims are amenable to construction, the Federal Circuit holds that the claims are not invalid for indefiniteness due to the lack of antecedent basis for the zinc anode. Although not specifically addressed, the Federal Circuit appears to agree with the appellant's contention that when read in context of the specification one skilled in the art would recognize that the discharge parameters are intended to apply only to a test cell.

Insights

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. ...