Pill Identification Using Image Embeddings and Claims Data

The patent combines multiple data sources (image data and claims data) and multiple machine learning models into a unified identification system. The image processing component generates image embeddings while the claims data processing component generates claims embeddings, and both are integrated into a single identification model that outputs pill identification results. This merging of multiple information sources resolves the contradiction by improving accuracy through comprehensive data analysis while managing complexity through integrated architecture.

The patent transitions from traditional image classification to an embedding-based approach where images and claims data are transformed into high-dimensional vector representations. This dimensional transformation allows the system to capture subtle differences between similar pills that conventional classifiers miss, improving identification accuracy by analyzing data in a higher-dimensional space where similar pills can be better differentiated.

The patent applies image augmentation and embedding generation as preliminary processing steps before the actual identification task. By pre-processing images through augmentation (rotating, flipping, adjusting brightness) and converting them to embeddings, the system prepares robust representations that are less sensitive to variations in lighting, background, and angle. This preliminary action ensures reliable identification even when input images vary in quality.

The patent introduces embedding vectors as intermediary representations between the raw image data and the final identification decision. These embeddings serve as a mediator that captures essential features while filtering out noise from variations in lighting, background, and angle. The claims embeddings similarly mediate between raw claims data and the identification process, providing stable intermediate representations that improve reliability.

The patent merges image data with claims data to create a more comprehensive identification system. By combining visual characteristics from images with contextual information from claims (such as prescription data, patient history, and medication records), the system achieves superior differentiation accuracy for confusingly similar pills. This merging of diverse data types provides multiple evidence streams that resolve ambiguities that single-data-source systems cannot address.

The patent creates a composite identification approach by integrating multiple data types (image embeddings and claims embeddings) into a unified model. This composite strategy combines the strengths of visual analysis with the informational value of claims data, producing a more accurate and robust identification system that leverages the complementary nature of different data sources to differentiate similar pills effectively.