公开(公告)号：US20230334089A1

公开(公告)日：2023-10-19

申请号：US18213263

申请日：2023-06-22

Applicant: Stripe, Inc

Inventor： Pranav DANDEKAR ,  Ashish GOEL ,  Peter LOFGREN ,  Matthew FISHER

IPC: G06F16/583 ,  G06F16/51 ,  G06F16/58 ,  G06V40/16 ,  G06V40/13 ,  G06V10/77 ,  G06V10/762 ,  G06F18/214 ,  G06V10/764 ,  G06V20/30

CPC classification number: G06F16/583 ,  G06F16/51 ,  G06F16/58 ,  G06F16/5838 ,  G06V40/172 ,  G06V40/1306 ,  G06V40/165 ,  G06V10/77 ,  G06V10/7625 ,  G06F18/214 ,  G06V10/764 ,  G06V10/7715 ,  G06V20/30

Abstract: Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

公开(公告)号：US20240303554A1

公开(公告)日：2024-09-12

申请号：US18664202

申请日：2024-05-14

Applicant: Stripe, Inc.

Inventor： Ashish GOEL ,  Peter LOFGREN

IPC: G06N20/20 ,  G06F18/243 ,  G06N3/084 ,  G06N5/043

CPC classification number: G06N20/20 ,  G06F18/24323 ,  G06N3/084 ,  G06N5/043

Abstract: Embodiments herein use transfer learning paradigms to facilitate classification across entities without requiring the entities access to the other party's sensitive data. In one or more embodiments, one entity may train a model using its own data (which may include at least some non-shared data) and shares either the scores (or an intermediate representation of the scores). One or more other parties may use the scores as a feature in its own model. The scores may be considered to act as an embedding of the features but do not reveal the features. In other embodiments, parties may be used to train part of a model or participate in generating one or more nodes of a decision tree without revealing all its features. The trained models or decision trees may then be used for classifying unlabeled events or items.

公开(公告)号：US20200242492A1

公开(公告)日：2020-07-30

申请号：US16258116

申请日：2019-01-25

Applicant: Stripe, Inc.

Inventor： Ashish GOEL ,  Peter LOFGREN

IPC: G06N5/04 ,  G06N20/20 ,  G06K9/62 ,  G06N3/08

Abstract: Embodiments herein use transfer learning paradigms to facilitate classification across entities without requiring the entities access to the other party's sensitive data. In one or more embodiments, one entity may train a model using its own data (which may include at least some non-shared data) and shares either the scores (or an intermediate representation of the scores). One or more other parties may use the scores as a feature in its own model. The scores may be considered to act as an embedding of the features but do not reveal the features. In other embodiments, parties may be used to train part of a model or participate in generating one or more nodes of a decision tree without revealing all its features. The trained models or decision trees may then be used for classifying unlabeled events or items.