18885294. ANOMALY DETECTION AND USER ATTRIBUTION USING MACHINE-LEARNING LARGE LANGUAGE MODELS (Maplebear Inc.)
ANOMALY DETECTION AND USER ATTRIBUTION USING MACHINE-LEARNING LARGE LANGUAGE MODELS
Organization Name
Inventor(s)
Benjamin Knight of Oakland CA (US)
Kenneth Jason Sanchez of Orange CA (US)
Christopher Billman of Chicago IL (US)
Rebecca Riso of Croton-On-Hudson NY (US)
Matthew Negrin of Brooklyn NY (US)
Licheng Yin of Mississauga (CA)
ANOMALY DETECTION AND USER ATTRIBUTION USING MACHINE-LEARNING LARGE LANGUAGE MODELS
This abstract first appeared for US patent application 18885294 titled 'ANOMALY DETECTION AND USER ATTRIBUTION USING MACHINE-LEARNING LARGE LANGUAGE MODELS
Original Abstract Submitted
An online system detects an anomaly associated with an item selection made by a picker for fulfilling an order of a user of an online system. The system generates a prompt for execution by a machine-learned model trained as a large language model. The prompt comprises a chat log between the picker and the user. The system provides the prompt to the machine-learned model for execution. The system receives, as output from the machine-learned model and based on the chat log, a description indicating whether the anomaly is attributable to the user. The system determines, based on the output from the machine-learned model, that the item selection is not attributable to the user. Responsive to determining that the item selection is not attributable to the user, the system provides a notification to a client device of the user to confirm whether the item selection is approved by the user.