Deep learning claims held abstract where the network itself was off the shelf

2026-07-07Fed. Cir.

Dental Monitoring SAS v. Align Technology, Inc., No. 2024-2270 (Fed. Cir. July 7, 2026) (nonprecedential); U.S. Pat. Nos. 11,049,248 and 10,755,409

The claims covered methods of using a deep learning device to analyze images of dental arches, to evaluate how an orthodontic aligner fit and to measure separation between teeth. Applying the two-step Alice test, the Federal Circuit held the claims were directed to collecting and analyzing information and supplied no inventive concept. The specification described the deep learning device as selectable from a list of well-known and available neural networks, and the court declined to treat the training methodology or the claimed accuracy gains as a technical improvement.

What it changesAccuracy and speed figures do not rescue a claim. What survives is a claimed technical mechanism — a specific architecture, a specific training-data construction, a specific way the model is integrated with the rest of the system — written into the specification before filing, not argued for the first time in a response.

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