Shopify Inc.

Kshetrajna Raghavan

Peng Yu

Kyle Bruce Tate

Xiaoxiao Li

Niklas Itänen

Roni Gurvich

Niklas Itaenen

Siphumelele Langeni

A computer-implemented is disclosed. The method includes: receiving, via a client device, a product search query; identifying a first product attribute based on query terms associated with the product search query; obtaining at least one search result matching the product search query; obtaining a set of product images associated with the at least one search result; for each of the product images, determining a respective level of confidence based on an estimation as to whether the product image is associated with the first product attribute; ranking the product images based on the levels of confidence; selecting at least a subset of the product images based on the ranking; and providing, via the client device, an indication of the at least one search result and the associated subset of product images.

Systems and methods for providing product data on mobile user interfaces

Computer-implemented methods and systems for colour-based image tagging and colour-based searching. The method may include identifying, using image analysis, one or more dominant colours of a product based on an image of the product and receiving selection of at least one of the one or more dominant colours. In response to receiving the selection of the at least one of the one or more dominant colours, a search for products matching the at least one of the one or more dominant colours may be initiated to obtain one or more results of the searching, the one or more results including at least one product matching the at least one of the one or more dominant colours.

Methods and systems for colour-based image analysis and search

A data partitioning system receives an input dataset for e-commerce products, each sample containing attributes and associated values for each product including at least an image; represents each sample as a node on a graph to provide a graph of nodes for the dataset; measures a relative similarity distance between each pair of nodes based on comparing at least image values for the attributes; determines for each pair of nodes whether they are related if the similarity distance between them is below a defined threshold, and if related, generate an edge between them on the graph; group the connected nodes into a first or a second group such that the grouped nodes have no edges connecting them to nodes in the other group and have a shortest relative similarity distance with each other. The groups are used as training dataset and testing data sets for a supervised machine learning classifier.

Systems and methods for automated product classification

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Fremont, CA, United States of America

Kshetrajna Raghavan