Nvidia Patents Software That Tells AI Exactly How to Fill In Missing Video Frames
Nvidia is patenting the communication layer between software and its AI frame-generation technology, specifically the handshake that tells a neural network what size and shape each video frame should be before it starts filling in the gaps.
What Nvidia's frame-interpolation API actually does
A game renders 60 frames per second, but your screen wants 120. Something has to invent the missing frames on the fly. Then connect it to your experience: when that invented frame looks wrong, you see a ghostly smear or a stuttering image.
Nvidia's patent covers the messaging system that sits between a game or application and the AI doing that frame-inventing work. Specifically, it defines an API (a standard set of instructions one piece of software sends to another) that passes along details like frame dimensions before the neural network starts generating. Think of it as the AI getting a proper briefing before it begins, rather than having to guess the assignment.
The idea is that if the neural network knows the exact frame size up front, it can allocate memory and set its own parameters more accurately, which should reduce errors in the final image you see.
determine, via an application programming interface (API) call, one or more storage locations for one or more frame interpolation parameters to perform frame interpolation using one or more neural networks …
Translation: It uses software commands to find where the instructions for filling in missing video frames are stored.
How the API passes frame parameters to the neural network
The patent describes a processor that carries out three coordinated steps using a defined API.
- Determine storage locations: An API call tells the processor exactly where in memory to find the parameters it needs for frame interpolation (the process of generating new frames between existing ones).
- Retrieve those parameters: The processor reads values like frame width, height, and related size metadata from those locations.
- Run the neural network: One or more neural networks then perform the actual frame interpolation using those retrieved parameters as configuration inputs.
The core insight is about coordination. Right now, software that uses AI-based frame generation may pass information about frame size in ad hoc or inconsistent ways. A formal API creates a standard contract: the application always deposits its frame-size data in a predictable place, and the neural network always knows where to look before it starts work.
This matters because neural networks that generate or predict video frames need to know the output dimensions early in the process, similar to how a printer needs to know the page size before it starts laying out a document. Getting that information late or incorrectly can force expensive recalculations or produce artifacts in the output.
… an application programming interface (API) is performed to indicate frame size information using one or more neural networks …
Translation: Software commands are used to pass video resolution details to the artificial intelligence.
What this means for AI-powered frame generation in games
For everyday users, this sits well below the surface. You would never interact with an API directly. But the quality of AI-generated frames in games and video applications depends on how cleanly the underlying software components talk to each other. A standardized handshake like this could reduce the edge cases where frame generation produces visual glitches, particularly at unusual resolutions or when switching between display modes.
Nvidia's run of frame-generation filings points toward building a more complete software stack around its DLSS and related technologies. Locking down the API layer is the kind of infrastructure work that makes those features more reliable across a wider range of games and hardware configurations, without the user ever noticing it happened.
That makes this Nvidia's 27th filing we've tracked since July in our GPU rendering race watchlist, adding to work like extra frame insertion and faster 3D geometry tools.
When AI software sharpens a video game's image, it needs to know the exact size of the frame it is working with. Lose that basic fact between software layers, and moving objects smear, edges break apart, and the picture falls apart in ways that players notice immediately.
That communication breakdown happens constantly in modern games because each software component hands off information to the next, and those handoffs are where simple facts go wrong. The damage is visible and immediate, not buried in a log file.
Standardizing exactly how frame-size information travels between components attacks the failure at its source rather than chasing each symptom after players report it. The fix matches the problem: narrow, structural, and aimed at preventing a whole category of visual failures before they appear.
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The drawings
85 drawing sheets from US 2026/0260314 A1 · click any drawing to enlarge
Show the other 52 sheets
Sheets 61 to 85 are in the full USPTO filing.
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