公开(公告)号：US20240257168A1

公开(公告)日：2024-08-01

申请号：US18102558

申请日：2023-01-27

Applicant: Salesforce, Inc.

Inventor： Yuxi Zhang ,  Kexin Xie ,  Max Fleming

IPC: G06Q30/0204 ,  G06Q30/0202

CPC classification number: G06Q30/0205 ,  G06Q30/0202

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A modeling service may generate a set of candidate segments using a set of cluster models and based on a seed segment and entity data. Based on respective features associated with the segments, the service may generate candidate segment fingerprints and a seed segment fingerprint, where a segment fingerprint may indicate a distribution of entities within a segment based on similarities between features associated with entities within the segment. That is, a segment fingerprint may depict how similar entities are in a candidate segment based on different features. The service may calculate similarity scores between the seed segment and the candidate segments using the segment fingerprints, and rank entities in terms of their similarity. The highest ranking entities may be identified from the candidate segments and included in a lookalike segment corresponding to the seed segment.

公开(公告)号：US20240296104A1

公开(公告)日：2024-09-05

申请号：US18116644

申请日：2023-03-02

Applicant: Salesforce, Inc.

Inventor： Max Fleming ,  Yuxi Zhang ,  Kexin Xie

IPC: G06F11/30 ,  G06F11/34

CPC classification number: G06F11/3006 ,  G06F11/3438

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. An application server or another device may receive a set of input data associated with an activity between an actor and an electronic communication message (e.g., a marketing email). From the input data, the application server may identify a set of features associated with the activity (an open rate, a click rate, etc.) and a set of source network addresses of respective, known automated scanners. The application server may input the features and source network addresses into a positive-and-unlabeled (PU) learning model, which may output a classification result that indicates a probability that the activity is associated with an automated scanner.