Computing US 12,482,227 B1 Not in force

Explanatory model architecture for image scoring reasoning

This invention describes a system for evaluating how different colors look in a specific part of an image. It takes an image and a marked area, then generates many new versions of that image, each showing a different color in the marked area. A machine learning model then scores how well each of these new images performs, ranks them, and saves the best-scoring ones. The marked area can be identified automatically or by a user.

Why it matters: Between 2024 and 2026, generative AI models have become significantly more capable and accessible, making the creation of diverse and realistic image variations much simpler and higher quality. Concurrently, advancements in image scoring models mean the 'performance score' can be more accurate and nuanced, enhancing the system's effectiveness.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, machine learning models, image processing

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