Authors:
Phillip Monkowski, William Karnavas, Asai Murugan, Fred Boadu
Abstract:
This Disclosure proposes a store using automated moving shelves that optimize the shopping experience for either an individual shopper, or for the aggregation of total shoppers. In the case of the aggregation of total shoppers, certain shelves in the store can be ranked by shoppers, either by buying items from the shelf, or by giving an "upvote" or "downvote" for the shelf, if it contained items they would want to purchase or not. At a certain time, once a certain number of scores are accumulated, the scores for each shelf can be sorted, and the shelves can be rearranged into a way where the high-traffic area shelves have the highest ranking, and areas with least traffic (far back corner) have the least used shelves. This should optimize the store for most shoppers over time, or as item trends change for shopper habits, so can the shelves.
In the opposite direction, when a shopper arrives, the store can read the shopper's data or determine what the shopper needs today and present them items they would want to purchase in an area with less traffic, for a more intimate experience or to help with social distancing.
Background:
As the usage of online shopping continues to grow due to convenience, amongst other factors, brick and mortar stores need to become more convenient, easier to use, or provide a different experience than an online one. Part of the issue that occurs with brick-and-mortar stores comes from the large number of products that are on shelves but not desired during a shopping trip. In fact, there may be a large quantity of items that are not desired by any shopper, but are in high traffic areas, due to relation to other products in the store or other factors.
Description:
Our idea is to have a store use automated moving shelves (similar to those used by large automated warehouses, for example) that optimize the shopping experience for either an individual shopper, or for the aggregation of total shoppers. In the case of the aggregation of total shoppers, certain shelves in the store can be ranked by shoppers, either by buying items from the shelf, or by giving an "upvote" or "downvote" for the particular shelf if it contained items they would want to purchase or not. At a certain time (such as overnight or after a week), the scores for each shelf can be sorted, and the shelves can be rearranged into a way where the high-traffic area shelves have the highest ranking, and areas with least traffic (far back corner, for example) have the least used shelves. This should optimize the store for most shoppers over time, and as item trends change for shopper habits, so can the shelves.
In the opposite direction, when a shopper arrives, the store can read the shopper's data or determine what the shopper needs today, and present them items they would want to purchase by moving those shelves to an area of the store with less traffic, for a more intimate experience or to help with social distancing.
TGCS Reference 2522
