Here's what actually issued. On July 21, 2020, Adobe Inc. was granted US10719742B2, "Image composites using a generative adversarial neural network," with inventors including Elya Shechtman and Oliver Wang — names that recur across Adobe's research output on image synthesis. The CPC list runs G06K 9/66, G06N 3/04, and G06N 3/088 (unsupervised network training), which tells you immediately this is a vision patent built on a generative adversarial network.

The mechanism, in plain terms: a GAN pits a generator that proposes composited images against a discriminator that tries to spot the fakes, and the two train against each other until the generator's blends look convincing. Applied to compositing — dropping a person or object into a new background — the network learns to harmonize lighting, edges, and color so the inserted element does not look pasted in. For a company whose business is creative tooling, that is a directly monetizable capability.

“The present disclosure relates to an image composite system that employs a generative adversarial network to generate realistic composite images.”— U.S. Patent No. 10,719,742 source

This is the GAN era of AI patenting, before diffusion models displaced adversarial networks as the default for image generation. Reading a 2020 grant like this one is a reminder that the patent record lags the research frontier: by the time a GAN method issues as an enforceable grant, the field has often moved on to the next architecture. The claim is no less valid for that — it just reflects where the science was when the application was filed.

On scope, the discipline holds. Granted B2, enforceable, but the claims describe a particular compositing pipeline using a GAN. They do not cover every way to merge images, nor the GAN concept itself, which was already widely published. The boundary is in the steps the examiner allowed.

The takeaway: US10719742B2 shows Adobe doing what it does across its portfolio — patenting the specific generative technique that powers a creative feature, naming the same core researchers, and keeping the claims tied to a concrete method rather than a sweeping idea.