Staples, Inc.

Nitin Verma

Michael Bhaskaran

Sandeep Sikka

Randall L Cogill

Xiaowei Lu

Christopher Yong

The technology includes an example method for determining matching items. In some implementations, the method may determine a first set and second set of attributes and attribute types associated with a first and second item, respectively, based on description data associated with the items. The method may determine an attribute-type indicator for a pairing of a first attribute of the first set with a second attribute of the second set based on attribute types of the attributes, compute an attribute value for the pairing based on a similarity between the first and second attribute, and compute a match score for a combination of the first and second items based on the attribute value and attribute-type indicator. The method may also, in response to receiving a data query identifying the first item, provide a graphical user interface including a graphical element representing the second item based on the match score.

System and method for automatic product matching

In an example embodiment, a method may generate an item layout map for a shelving unit, where the shelving unit stores item types associated with a product category. The method may determine product attributes associated with the product category and divider configurations of the shelving unit, where a divider configuration divides the shelving unit into one or more candidate regions. The method may generate an ontology entry associated with a product attribute and the divider configuration based on the item layout map and the divider configuration of the shelving unit, the ontology entry indicating a regional entropy values of a product attribute for the candidate regions generated by the divider configuration. In some instances, the method may generate a guidance instruction related to the product attribute of the product category based on the ontology entry and provide the guidance instruction to a user.

Interactive machine learning assistant

A method determines a processing cluster including one or more stock keeping units (SKUs); divides the processing cluster into a first cluster and a second cluster based on SKU affinities between the one or more SKUs in the processing cluster; determines a first SKU of the first cluster to be replicated to the second cluster based on a demand correlation between the first SKU of the first cluster and a second SKU of the second cluster; replicates the first SKU of the first cluster to the second cluster; responsive to replicating the first SKU of the first cluster to the second cluster, determines whether the first cluster and the second cluster satisfy a defined constraint; and responsive to determining that the first cluster and the second cluster satisfy the defined constraint, assigns the first cluster to a first physical location and assigning the second cluster to a second physical location.

Automated guided vehicle control and organizing inventory items using stock keeping unit clusters

In an example embodiment, a method determines a container containing one or more items associated with one or more item types, the container located at a current position in a storage facility; determines one or more order likelihoods of the one or more item types contained in the container; determines a container utilization likelihood of the container based on the one or more order likelihoods of the one or more item types contained in the container; determines an optimal position for the container in the storage facility based on the container utilization likelihood of the container, the optimal position being different from the current position of the container; and instructing an automated guided vehicle (AGV) to transport the container from the current position of the container to the optimal position of the container in the storage facility.

Automated guided vehicle control and organizing inventory items using predictive models for slow item types

In an embodiment, a method may determine a first candidate item type and a second candidate item type for a container. The method may determine appearance feature(s) of the first candidate item type and appearance feature(s) of the second candidate item type. The method may determine, using a trained dissimilarity model, a distinguishability score between the first candidate item type and the second candidate item type based on the appearance feature(s) of the first candidate item type and the appearance feature(s) of the second candidate item type. The method may determine to store the first candidate item type and the second candidate item type together in the container based on the distinguishability score between the first candidate item type and the second candidate item type. In some instances, the method may place item(s) of the first candidate item type and item(s) of the second candidate item type in the container.

Growing community of inventors

Natick, MA, United States of America

Nitin Verma