These patents treat similar but non-identical products as a pricing intelligence problem because the goal is not just to clean a catalog or deduplicate items. The workflow is designed to compare a competitor offer against the subject retailer’s offer, learn how that retailer defines a non-exact match, and then use the resulting comparison to support competitive pricing decisions[1][2][3][4].
In other words, the system cares about how much two products overlap, which features differ, and what those differences imply for price, rather than whether the products can be forced into an exact catalog identity[5][6][7].
The workflow begins with a robot module, such as a web crawler or spider, extracting product data from a competitor product page. The patents describe pulling data such as images, prices, descriptions, and other offer terms, then comparing those attributes with the subject retailer’s own product data[8][9].
That comparison uses product matching rules with adjustable parameters, and the system can learn each retailer’s own definition of a non-exact match over time[10][11].
The patents make the threshold retailer-specific because the same pair of products may count as a meaningful comparison for one retailer and not for another. That is why the system is described as learning the retailer’s own non-exact matching definition, instead of relying on a universal catalog rule[20][21].
Once a non-exact match is found, the output can include the differences between the feature sets. The patents say those differences support statistical estimation of the value of individual features or feature groups across multiple comparisons[22][23].
This is the core pricing-intelligence move: the system is not simply recording that two items are similar, but using similarity gaps to infer what features carry price premium or discount value[24][25].
The patents also connect these comparisons to taxonomy decisions. They say the results can identify desirable feature combinations and can affect taxonomy choices if a different taxonomy supports a better price point[26][27].
One source adds that a product may even be moved into a business display taxonomy if that allows a premium price[28].
So the output is not just a matched pair. It is a pricing signal that can feed competitive price adjustments, product presentation, and assortment classification decisions[29][30].
The practical ecommerce implication is that non-exact matching helps retailers price comparable-but-not-identical products more intelligently, especially when feature differences, not catalog identity, explain why one item should be priced above or below another[31][32][33].
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