Visual Token Trend Analysis for E-commerce Image Search

The patent segments images into multiple feature dimensions including color histograms, texture features, shape descriptors, and semantic tags. Each dimension is processed independently to extract specific visual characteristics, allowing comprehensive image analysis without requiring a single complex processing system. This segmentation enables accurate image search by comparing multiple feature vectors rather than relying on simple metadata.

The patent implements preliminary action by pre-processing images to extract and store visual tokens (color palettes, texture descriptors, shape features) in a database before trend analysis is needed. When consumer trends need to be identified, the system queries pre-extracted visual tokens rather than analyzing raw images in real-time. This approach enables rapid trend identification by comparing pre-computed visual features across large image collections without the time penalty of real-time image processing.

The patent implements a universal image classification system that extracts multiple types of visual features (color, texture, shape, semantic content) from a single image analysis process. These multi-functional visual tokens serve multiple purposes: they enable accurate image search, support trend identification, and facilitate out-of-stock product suggestions. By making the image classification system universal and multi-functional, the patent achieves reliable product suggestions without requiring separate specialized classification processes for each application.