Relating environmental effects to user interactions using automated shopping carts
An automated checkout system applies environmental effects to physical regions within a store. The automated checkout system logs the environmental effect, a time the environmental effect was applied, and the physical region to which the environmental effect was applied. The automated checkout system detects an interaction event and logs a time associated with the interaction event. The automated checkout system identifies a location of the automated shopping cart and identifies a physical region within the store that contains the automated shopping cart's location. The automated checkout system identifies the environmental effect that was applied to the physical region at the time of the interaction event and generates a data point. For each environmental effect, the automated checkout system computes a success metric based on the generated data points. The automated checkout system applies environmental effects to physical regions based on the success metrics.
1 . A method comprising:
at a computer system comprising at least one processor and memory:
applying a set of environmental effects to physical regions within a store during a time period, wherein each environmental effect is one of a control environmental effect or an experimental environmental effect, and wherein applying an environmental effect of the set of environmental effects comprises:
transmitting instructions over a network to a device within a physical region, wherein the device is configured to applying the environmental effect to the physical region;
logging, in an effect log, an entry for each of the set of environmental effects, wherein the entry for each of the set of environmental effects comprises the environmental effect applied, a time associated with the application of the environmental effect, and the physical region to which the environmental effect was applied;
generating a set of data points based on a plurality of interaction events from a plurality of users and based on sensor data captured by automated shopping carts within the store, wherein generating a data point based on an interaction event from a user comprises:
detecting an interaction event performed by the user based on first sensor data captured by sensors of an automated shopping cart associated with the user, wherein the interaction event indicates that the user has interacted with an item, and wherein the first sensor data comprises image data captured by a camera coupled to the automated shopping cart;
logging, in an event log, an entry for the interaction event, wherein the entry comprises the interaction event and a time associated with the interaction event;
identifying a location of the automated shopping cart within the store at the time associated with the interaction event, wherein the location of the automated shopping cart is identified based on second sensor data captured by sensors of the automated shopping cart;
identifying a physical region within the store that contains the identified location of the automated shopping cart;
identifying an environmental effect that was applied to the physical region at the time associated with the interaction event based on the logged environmental effects in the effect log; and
generating a data point for the interaction event, wherein the data point represents the environmental effect in place in the physical region at the time of the interaction event;
generating a success metric for each environmental effect of the set of environmental effects based on the generated data points; and
applying an environmental effect of the set of environmental effects to a physical region based on the success metric associated with the environmental effect, wherein applying the environmental effect to the physical region comprises:
transmitting instructions over a network to a device within the physical region to cause the device to apply the environmental effect.
2 . The method of claim 1 , wherein applying an environmental effect to a physical region within a store comprises changing a lighting of the physical region.
3 . The method of claim 1 , wherein applying environmental effects to physical regions within a store comprises applying environmental effects at periodic time intervals.
4 . The method of claim 1 , wherein applying environmental effects to physical regions within a store comprises applying the same environmental effect to each physical region.
5 . The method of claim 1 , wherein applying environmental effects to physical regions within a store comprises simultaneously applying an experimental environmental effect to a first physical region and a control environmental effect to a second physical region.
6 . The method of claim 1 , wherein generating a success metric for each environmental effect of the set of environmental effects based on the data points comprises filtering the data points based on whether the user performed a subsequent interaction with the item.
7 . The method of claim 1 , wherein generating a success metric for each environmental effect based on the data points comprises computing a success metric for each environmental effect in a particular physical region of the physical regions.
8 . The method of claim 1 , wherein generating a success metric for each environmental effect of the set of environmental effects further comprises filtering the generated data points based on whether the item is on a wish list of the user.
9 . The method of claim 1 , wherein generating a success metric for each environmental effect based on the data points further comprises filtering the data points based on whether the identified location of the automated shopping cart is within a threshold distance of a boundary between two physical regions.
10 . The method of claim 1 , wherein generating a success metric for each environmental effect based on the data points further comprises filtering data points with associated times within a threshold time from the time associated with the application of the environmental effect.
11 . The method of claim 1 , wherein the interaction event indicates that the user added the item to the automated shopping cart.
12 . The method of claim 1 , further comprising training a machine-learning model based on the generated success metrics for each environment effect of the set of environmental effects.
13 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform steps comprising:
applying a set of environmental effects to physical regions within a store during a time period, wherein each environmental effect is one of a control environmental effect or an experimental environmental effect, and wherein applying an environmental effect of the set of environmental effects comprises:
transmitting instructions over a network to a device within a physical region, wherein the device is configured to applying the environmental effect to the physical region;
logging, in an effect log, an entry for each of the set of environmental effects, wherein the entry for each of the set of environmental effects comprises the environmental effect applied, a time associated with the application of the environmental effect, and the physical region to which the environmental effect was applied;
generating a set of data points based on a plurality of interaction events from a plurality of users and based on sensor data captured by automated shopping carts within the store, wherein generating a data point based on an interaction event from a user comprises:
detecting an interaction event performed by the user based on first sensor data captured by sensors of an automated shopping cart associated with the user, wherein the interaction event indicates that the user has interacted with an item, and wherein the first sensor data comprises image data captured by a camera coupled to the automated shopping cart;
logging, in an event log, an entry for the interaction event, wherein the entry comprises the interaction event and a time associated with the interaction event;
identifying a location of the automated shopping cart within the store at the time associated with the interaction event, wherein the location of the automated shopping cart is identified based on second sensor data captured by sensors of the automated shopping cart;
identifying a physical region within the store that contains the identified location of the automated shopping cart;
identifying an environmental effect that was applied to the physical region at the time associated with the interaction event based on the logged environmental effects in the effect log; and
generating a data point for the interaction event, wherein the data point represents the environmental effect in place in the physical region at the time of the interaction event;
generating a success metric for each environmental effect of the set of environmental effects based on the generated data points; and
applying an environmental effect of the set of environmental effects to a physical region based on the success metric associated with the environmental effect, wherein applying the environmental effect to the physical region comprises:
transmitting instructions over a network to a device within the physical region to cause the device to apply the environmental effect.
14 . The non-transitory computer-readable medium of claim 13 , wherein the instructions for applying environmental effects to physical regions within a store comprise instructions that cause the processor to apply environmental effects at periodic time intervals.
15 . The non-transitory computer-readable medium of claim 13 , wherein the instructions for applying environmental effects to physical regions within a store comprise instructions that cause the processor to apply the same environmental effect to each physical region.
16 . The non-transitory computer-readable medium of claim 13 , wherein the instructions for applying environmental effects to physical regions within a store comprise instructions that cause the processor to simultaneously apply an experimental environmental effect to a first physical region and a control environmental effect to a second physical region.
17 . The non-transitory computer-readable medium of claim 13 , wherein the instructions for generating a success metric for each environmental effect of the set of environmental effects based on the data points comprise instructions that cause the processor to filter the data points based on whether the user performed a subsequent interaction with the item.
18 . The non-transitory computer-readable medium of claim 13 , wherein the instructions for generating a success metric for each environmental effect based on the data points comprise instructions that cause the processor to generate a success metric for each environmental effect in a particular physical region of the physical regions.
19 . The non-transitory computer-readable medium of claim 13 , wherein the instructions for generating a success metric for each environmental effect of the set of environmental effects further comprise instructions that cause the processor to filter the generated data points based on whether the item is on a wish list of the user.
20 . A system comprising:
a processor; and
a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform steps comprising:
applying a set of environmental effects to physical regions within a store during a time period, wherein each environmental effect is one of a control environmental effect or an experimental environmental effect, and wherein applying an environmental effect of the set of environmental effects comprises:
transmitting instructions over a network to a device within a physical region, wherein the device is configured to applying the environmental effect to the physical region;
logging, in an effect log, an entry for each of the set of environmental effects, wherein the entry for each of the set of environmental effects comprises the environmental effect applied, a time associated with the application of the environmental effect, and the physical region to which the environmental effect was applied;
generating a set of data points based on a plurality of interaction events from a plurality of users and based on sensor data captured by automated shopping carts within the store, wherein generating a data point based on an interaction event from a user comprises:
detecting an interaction event performed by the user based on first sensor data captured by sensors of an automated shopping cart associated with the user, wherein the interaction event indicates that the user has interacted with an item, and wherein the first sensor data comprises image data captured by a camera coupled to the automated shopping cart;
logging, in an event log, an entry for the interaction event, wherein the entry comprises the interaction event and a time associated with the interaction event;
identifying a location of the automated shopping cart within the store at the time associated with the interaction event, wherein the location of the automated shopping cart is identified based on second sensor data captured by sensors of the automated shopping cart;
identifying a physical region within the store that contains the identified location of the automated shopping cart;
identifying an environmental effect that was applied to the physical region at the time associated with the interaction event based on the logged environmental effects in the effect log; and
generating a data point for the interaction event, wherein the data point represents the environmental effect in place in the physical region at the time of the interaction event;
generating a success metric for each environmental effect of the set of environmental effects based on the generated data points; and
applying an environmental effect of the set of environmental effects to a physical region based on the success metric associated with the environmental effect, wherein applying the environmental effect to the physical region comprises:
transmitting instructions over a network to a device within the physical region to cause the device to apply the environmental effect.