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Tracking a Shopper's Interactions with Inventory via a Wearable Device

IP.com Disclosure Number: IPCOM000245323D
Publication Date: 2016-Feb-29
Document File: 2 page(s) / 60K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed a system that leverages wrist-wearable smart devices (i.e., smart wristbands) to non-invasively gather and benefit from motion data tracked during a user's usual trip to a brick-and-mortar store.

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Tracking a Shopper's Interactions with Inventory via a Wearable Device

Trends in the progression of technology indicate that wearable devices will continue to bring innovation to people's everyday lives. These devices have already shown potential, providing creative and innovative use cases to integrate with and improve daily interactions not yet successfully penetrated by modern technology. One such area is the consumer's in-store shopping experience. Opportunity for improvement is available both on the customer-side and on the enterprise analytics side.

The novel contribution is a system that leverages wrist-wearable smart devices (i.e., smart wristbands) to non-invasively gather and benefit from motion data tracked during a user's usual trip to a brick-and-mortar store.

The system combines motion data recorded by a wrist-wearable smart device, and a unique proximity-identifier tag (e.g., radio frequency identification, RFID) located on all products in stock, thereby determining and classifying the consumer's interaction with one or more items from the store's inventory. Machine learning algorithms built into the invention provide an increasingly accurate method for profiling the consumer's

wrist-based movements while holding the item. Interactions of significance include, but are not limited to, taking an item off the shelf, turning the item over, placing the item back on the shelf, and placing the item in a cart.

Analytics can be performed thereafter on all recorded/profiled customer interactions, and the resulting (now meaningful) conclusions can benefit the consumer, the brick-and-mortar store, and the product manufacturers.

Example use cases include:


• Retailer/Manufacturer Insights - The combination of product information and geo-spatial consumer action analysis can help the retailer and manufacturer determine why a product is not selling. If consumers are inspecting the product's nutrition information (identified by the turning over of the product) and are not purchasing the product (identified by the returning of the product to the shelf), then it could be a health concern. If consum...