Sensor-based maximum-likelihood estimation of item assignments

Abstract

In a next-generation retail store, the presence of a shopper at a particular shelf location, at about the time that an item was removed from that shelf location, gives rise to a hypothesis that the item may have been picked for purchase by that shopper. An ensemble of different such hypotheses, about different items removed by different shoppers, is evaluated jointly to determine which hypotheses are most likely. Other factors can influence the determinations, such as a physical distance between picked products, together with a time interval between their picking. If cameras are used to detect removal of items from shelves, hand depictions captured by such cameras are further factors that may be considered, e.g., helping associate certain item removals with a common shopper. By generating purchase lists for each shopper with such methods, shoppers can be relieved of the need of having each item scanned at a checkout. Some embodiments are privacy-preserving—neither employing nor capturing personally-identifying information. Other embodiments don't require tracking movements of shoppers through the store. A great number of other features and arrangements are also detailed.