Smart cart issue prediction with remedial workflow

A non-transitory computer-readable storage medium storing instructions that, when executed by a computer processor, cause the computer processor to perform operations comprising:

• obtaining sensor data from one or more sensors of a smart cart, wherein obtaining the sensor data comprises:
• obtaining inertial measurement unit (IMU) data from an IMU sensor coupled to the smart cart;
• applying a cart issue prediction model trained as a machine-learning model to input the sensor data comprising the IMU data and to identify one or more predicted issues with operation of the smart cart, wherein the cart issue prediction model is trained according to a process comprising:
• obtaining historical sensor data for a fleet of smart carts operated in an in-store environment, wherein the historical sensor data comprises IMU data captured by IMU sensors coupled to the fleet of smart carts, and wherein the historical sensor data for each smart cart in the fleet is associated with a label indicating any anomalies in the operation of the smart cart, and
• training the machine-learning model with the historical sensor data and the labels for the fleet of smart carts to identify anomalies in operation of a given smart cart based on the sensor data of the given smart cart;
• and
• responsive to identifying the one or more predicted issues, generating a command signal to power down the smart cart;
• and
• transmitting the command signal to the smart cart.

A system comprising:

• a computer processor;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed by the computer processor, cause the computer processor to perform operations comprising:
• obtaining sensor data from one or more sensors of a smart cart, wherein obtaining the sensor data comprises:
• obtaining inertial measurement unit (IMU) data from an IMU sensor coupled to the smart cart;
• applying a cart issue prediction model trained as a machine-learning model to input the sensor data comprising the IMU data and to identify one or more predicted issues with operation of the smart cart, wherein the cart issue prediction model is trained according to a process comprising:
• obtaining historical sensor data for a fleet of smart carts operated in an in-store environment, wherein the historical sensor data comprises IMU data captured by IMU sensors coupled to the fleet of smart carts, and wherein the historical sensor data for each smart cart in the fleet is associated with a label indicating any anomalies in the operation of the smart cart, and
• training the machine-learning model with the historical sensor data and the labels for the fleet of smart carts to identify anomalies in operation of a given smart cart based on the sensor data of the given smart cart;
• and
• responsive to identifying the one or more predicted issues, generating a command signal to power down the smart cart;
• and
• transmitting the command signal to the smart cart.