For example, a shopper might pick up and put down multiple varieties of yogurt, in different combinations, and as they are doing so, another customer might reach for the same item, or the freezer door could fog up, obscuring the cameras’ view. In complex situations like these, the new model can quickly and accurately determine the actual items taken by each shopper. This is thanks to its ability to simultaneously process inputs from various sources (including, in this scenario, weight sensors on the fridge shelves), continuously learn from these inputs, and decide which are most important in order to accurately sort out who took what. This minimizes receipt delays and increases the ease of deployment of the technology for retailers.