Management system for automatic determination of anomaly behavior for user of a smart shopping cart

A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising:

• gathering, by a computer system, data associated with a physical activity of a user, the user being physically present at a location of a retailer and adding a set of items to a physical receptacle, the computer system in communication with the physical receptacle, the data including information about one or more weight changes associated with one or more items of the set of items added to the physical receptacle without a scanning performed for the one or more items by the user;
• accessing a machine-learning model trained to detect a fraudulent behavior of the user associated with the physical activity;
• applying the machine-learning model to the gathered data including the information about the one or more weight changes associated with the one or more items added to the physical receptacle without the scanning to:
• detect one or more anomalies during the physical activity of the user, the one or more anomalies being indicative of the fraudulent behavior, and
• generate a score for each of the one or more detected anomalies that is indicative of a likelihood that each of the one or more detected anomalies is associated with the fraudulent behavior;
• generating, using the score for each of the one or more detected anomalies, a total score for the one or more detected anomalies;
• determining, based on the total score being higher than a threshold score, that the one or more detected anomalies satisfy a threshold condition for the fraudulent behavior;
• in response to determining that the one or more detected anomalies satisfy the threshold condition for the fraudulent behavior, sending a communication to a management system of the retailer to cause at least one remedial action to be performed before the user physically leaves the location of the retailer;
• determining whether the one or more anomalies detected by the machine-learning model were correct;
• generating training data based on information whether the one or more detected anomalies were correct;
• and
• retraining the machine-learning model using the generated training data.

A computer system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising:
• gathering, by the computer system, data associated with a physical activity of a user, the user being physically present at a location of a retailer and adding a set of items to a physical receptacle, the computer system in communication with the physical receptacle, the data including information about one or more weight changes associated with one or more items of the set of items added to the physical receptacle without a scanning performed for the one or more items by the user;
• accessing a machine-learning model trained to detect a fraudulent behavior of the user associated with the physical activity;
• applying the machine-learning model to the gathered data including the information about the one or more weight changes associated with the one or more items added to the physical receptacle without the scanning to:
• detect one or more anomalies during the physical activity of the user, the one or more anomalies being indicative of the fraudulent behavior, and
• generate a score for each of the one or more detected anomalies that is indicative of a likelihood that each of the one or more detected anomalies is associated with the fraudulent behavior;
• generating, using the score for each of the one or more detected anomalies, a total score for the one or more detected anomalies;
• determining, based on the total score being higher than a threshold score, that the one or more detected anomalies satisfy a threshold condition for the fraudulent behavior;
• in response to determining that the one or more detected anomalies satisfy the threshold condition for the fraudulent behavior, sending a communication to a management system of the retailer to cause at least one remedial action to be performed before the user physically leaves the location of the retailer;
• determining whether the one or more anomalies detected by the machine-learning model were correct;
• generating training data based on information whether the one or more detected anomalies were correct;
• and
• retraining the machine-learning model using the generated training data.