Traditional search engines often struggle with complex queries involving multiple objects or spatial relationships, but this technology uses a generative model to break queries down into specific search criteria. These criteria act as a ranking rubric or a set of binary filters used to evaluate candidate images retrieved from a database. An AI-based auto-rater or classification model then scores each image based on how well it satisfies the requested features, such as specific actions or settings. The system then adjusts search rankings to ensure the most relevant, fully responsive images are displayed to the user. This framework can also be used to generate high-quality training datasets for improving other machine-learned models and embedding spaces.
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