公开(公告)号：US20240221009A1

公开(公告)日：2024-07-04

申请号：US18148713

申请日：2022-12-30

Applicant: Fidelity Information Services, LLC

Inventor： Benjamin Wellmann ,  Luke Jurat ,  Edward S. Barker ,  Stephane Wyper

IPC: G06Q30/0202

CPC classification number: G06Q30/0202

Abstract: Systems and methods for developing temporal behavioral profiles are disclosed. A system for developing temporal behavioral profiles may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving first transaction information; determining that the first transaction information shares a threshold amount of attributes with second transaction information; based on the determination that the first transaction information shares a threshold amount of attributes with the second transaction information, determining that the first transaction information and the second transaction information are associated with a single purchaser; constructing a temporal behavioral profile for the single purchaser based on the first transaction information and the second transaction information, the temporal behavioral profile being configured for predicting at least one future transaction associated with the single purchaser; and predicting the at least one future transaction associated with the single purchaser using the temporal behavioral profile.

公开(公告)号：US20240220822A1

公开(公告)日：2024-07-04

申请号：US18148058

申请日：2022-12-29

Applicant: Fidelity Information Services, LLC

Inventor： Benjamin Wellmann ,  Luke Jurat ,  Edward S. Barker ,  Stephane Wyper

IPC: G06N5/022 ,  G06N5/04

CPC classification number: G06N5/022 ,  G06N5/04

Abstract: Systems and methods for predicting item group composition are disclosed. A system for predicting item group composition may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving entity identification information and a timestamp associated with a transaction without receiving information distinguishing items associated with the transaction; determining, based on the entity identification information, a localized machine learning model trained to predict categories of items based on transaction information applying to all of the items associated with the transaction; and applying the localized machine learning model to a model input to generate predicted categories of items associated with the transaction, the model input including the received entity identification information and a timestamp but not including information distinguishing items associated with the transaction.