Sony · Filed Dec 18, 2024 · Published Jun 18, 2026 · verified — real USPTO data

Sony's New Patent Teaches AI to Write Its Own Game Tracking Code

Sony has filed a patent for an AI system that scans a video game's source code, figures out what the important pieces are, and then writes tracking code to monitor them — all without a human developer doing any of that work manually.

Sony Patent: AI That Writes Its Own Game Tracking Code — figure from US 2026/0166439 A1
FIG. 1A — rendered from the official USPTO publication PDF.
Publication number US 2026/0166439 A1
Applicant Sony Interactive Entertainment Inc.
Filing date Dec 18, 2024
Publication date Jun 18, 2026
Inventors John Afsal Elias Kuner
CPC classification 463/43
Grant likelihood Medium
Examiner DOSHI, ANKIT B (Art Unit 3715)
Status Docketed New Case - Ready for Examination (Sep 18, 2025)
Document 20 claims

What Sony's AI game-tracking system actually does

Imagine you're building a video game and you need to know exactly when a player picks up a sword, opens a chest, or completes a level — so you can study how people actually play. Normally, a developer has to sit down and write separate code by hand to log all of those moments. That's tedious, easy to miss, and has to be repeated every time the game changes.

Sony's patent describes an AI that does this automatically. It reads through the game's source code, spots the objects and actions that matter (like that sword or that chest), and then writes its own tracking code and plugs it directly into the game. The game then logs those moments during play without any extra effort from a developer.

The practical upside is that studios spend less time on bookkeeping work and more time building the game. It also means tracking stays up to date as the game evolves, since the AI can re-scan the code whenever something changes.

How the ML model finds and rewrites game code

The patent describes a pipeline built around one or more machine learning models that operate during the game's build process — the phase where source code gets compiled into a playable game.

Here's how the sequence works:

  • The ML model scans the game's source code and finds definitions of game content — the formal descriptions of objects like characters, items, or levels.
  • It generates a unique identifier for each piece of content it finds, essentially giving everything a trackable name.
  • It then identifies the functional components — the chunks of code that actually change the state of those objects when the game runs (for example, the function that removes a health point when a player is hit).
  • For each of those components, it generates an event definition: a structured description of what happened, tied to the identifier it created earlier.
  • Finally, it writes event instrumentation — small pieces of monitoring code — and inserts them directly into the relevant game functions.

The term "platform-specific" in the patent title suggests the system is designed to adapt to different hardware targets, likely including Sony's own PlayStation consoles, and can generate tracking code tailored to each one.

What this means for game studios and PlayStation data

For game studios, writing and maintaining analytics instrumentation is genuinely time-consuming grunt work. Every time a developer adds a new weapon type or reworks a level, someone has to go back and make sure the tracking code covers it. An AI that handles this automatically could meaningfully reduce that overhead, especially for large teams shipping to multiple platforms.

For Sony specifically, this fits a broader pattern of wanting richer data on how players interact with PlayStation games. Better instrumentation means better telemetry, and better telemetry informs everything from difficulty balancing to deciding which features to cut. Studios using Sony's development tools could see this baked into the build pipeline at the platform level.

Editorial take

This is a practical, unglamorous patent that solves a real problem — nobody writes about analytics instrumentation, but every studio deals with it. The AI angle here is plausible and well-scoped: reading code and generating boilerplate tracking hooks is exactly the kind of pattern-matching task where ML models are genuinely useful. It's worth paying attention to if you care about how Sony is embedding AI into its developer toolchain.

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Source. Full patent text and figures from the official USPTO publication PDF.

Editorial commentary on a publicly published patent application. Not legal advice.