Target Brands, Inc.

Clayton Black

Eric Runquist

Michael Lissick

Andrew James Wipf

Joakim Sternberg

Debbie Bell

Ben Podoll

Aaron Decker

Ben Schuster

Chelsea Kane

Elizabeth Feist

Matthew Fevold

Susan Yang

Katlyn Temple

Randy Carlson

Craig Servin

In some implementations, a method performed by data processing apparatuses includes receiving map data that identifies a layout of physical objects within a physical area, identifying contiguous groups of the physical objects as composite objects, selecting bounding algorithms to use for generating graphical shapes for the composite objects, generating graphical shapes by applying the selected bounding algorithms to the composite objects, and testing the graphical shapes against one or more criteria. For candidate graphical shapes that fail a test, a new bounding algorithm can be selected and the generating and the testing can be repeated using the new bounding algorithm. A simplified graphical map can be output that represents the physical objects within the physical area using the graphical shapes.

Generating simplified map shapes

In some implementations, a method performed by data processing apparatuses includes receiving stored map data corresponding to a digital map, identifying a discrepancy between the digital map and a physical space which the digital map represents, updating the digital map to correct the identified discrepancy, storing the updated digital map, and optionally providing the updated digital may for presentation on a device display.

Map accuracy

In one implementation, a method for determining accurate store traffic levels includes receiving, at a computer system, historical store data for a physical store that was generated by internal data sources located within the physical store; generating, by the computer system, a historical store model that correlates values from store data with guest traffic levels within the physical store; receiving, by the computer system, current store data from one or more of the internal data sources; determining, by the computer system, current guest traffic level in the physical store based on the current store data and the historical store model; and outputting, by the computer system, the current guest traffic level in the physical store.

Store traffic indicator

In some implementations, a method performed by data processing apparatuses includes receiving an indication of interest in an item being presented to a user by an online shopping interface. Data that represents the indication of user interest in the item is stored with data that represents indications of user interest in other items. In response to determining that the user is located within a physical store, the stored data that represents indications of user interest in the items is accessed, items that are available from the physical store are ranked, and one or more of the ranked items are provided for presentation to the user.

Providing personalized item recommendations during in-store shopping experience

In some implementations, a method performed by data processing apparatuses includes receiving, from a requestor device, an item location request for an item. Map information is received for a mapped space in which the item is located. Item location coordinates are received corresponding to locations within the mapped space at which the item has been previously selected or scanned. The item location coordinates are mapped with respect to the mapped space, and a plurality of clusters are determined for the item location coordinates. For each cluster, a representative location of the cluster is determined. One of the clusters is selected, based at least in part on its representative location, and an estimated location of the item is provided to the requestor device, based at least in part on the representative location of the selected cluster.

Determining item locations using crowdsourced data

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Minneapolis, MN, United States of America

Clayton Black