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：US12051008B2

公开(公告)日：2024-07-30

申请号：US17883503

申请日：2022-08-08

Applicant: Salesforce, Inc.

Inventor： Donglin Hu ,  Yuxi Zhang ,  Kexin Xie ,  Chen Xu

IPC: G06N5/022 ,  G06Q30/0241

CPC classification number: G06N5/022 ,  G06Q30/0241

Abstract: A prediction system of an online system deploys one or more machine-learned architectures to generate predictions. In one embodiment, the machine-learned architecture is a stacked ensemble model. The stacked ensemble model includes a plurality of base models, where a base model is coupled to receive input data and generate a base prediction for the input data. The stacked ensemble model includes a meta model that combines the base predictions to generate a meta prediction for the input data. The prediction system also generates a reliability measure that takes advantage of the base predictions to evaluate the reliability of the meta prediction. In this manner, while the quality of individual predictions may differ from one another depending on the values of the input data, the prediction system can dynamically generate the reliability measure to account for this variation.

公开(公告)号：US20240144328A1

公开(公告)日：2024-05-02

申请号：US18405279

申请日：2024-01-05

Applicant: Salesforce, Inc.

Inventor： Yuxi Zhang ,  Kexin Xie ,  Shrestha Basu Mallick ,  Darrell Grissen

IPC: G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0251

CPC classification number: G06Q30/0281 ,  G06Q30/0201 ,  G06Q30/0271

Abstract: A system can recommend a next action for a user. A memory can store user data corresponding to the user and can include historic interaction points. A behavior pattern can be identified based on two or more interaction points stored in the user data. An intent of the user based on the behavior pattern can be identified. The intent can be based on a previous behavior pattern of another user. Several probabilities that the user will meet one or more objectives can be determined based on the intent. The probabilities can be scored using and used to assign a policy to the first user. A next action can be recommended based on the policy and executed with respect to the user. The outcome of the recommended next action can be stored to the user data.

公开(公告)号：US11907267B2

公开(公告)日：2024-02-20

申请号：US16119951

申请日：2018-08-31

Applicant: Salesforce, Inc.

Inventor： Yacov Salomon ,  Kexin Xie ,  Wanderley Liu ,  Nathan Irace Burke ,  David Yourdon

IPC: G06F16/28 ,  G06F9/451 ,  G06F3/0481 ,  G06F16/26 ,  G06F16/901

CPC classification number: G06F16/287 ,  G06F3/0481 ,  G06F9/451 ,  G06F16/26 ,  G06F16/9024

Abstract: Methods, systems, and devices for displaying a user interface for frequent pattern (FP) analysis are described. In some cases, data stored at a multi-tenant database server may be analyzed to understand various interactions and patterns between data attributes associated with multiple users, or determine one or more attributes associated with a characterization of an individual (e.g., a persona). The multi-tenant database server may effectively cluster and/or perform calculations on attributes of the data to understand user patterns and determine common personas. The results may then be displayed by a user interface at a user device (e.g., associated with the user).

公开(公告)号：US20250086402A1

公开(公告)日：2025-03-13

申请号：US18415308

申请日：2024-01-17

Applicant: Salesforce, Inc.

Inventor： Ran Xu ,  Zeyuan Chen ,  Yihao Feng ,  Krithika Ramakrishnan ,  Congying Xia ,  Juan Carlos Niebles Duque ,  Vetter Serdikova ,  Huan Wang ,  Yuxi Zhang ,  Kexin Xie ,  Donglin Hu ,  Bo Wang ,  Ajaay Ravi ,  Matthew David Trepina ,  Sam Bailey ,  Abhishek Das ,  Yuliya Feldman ,  Pawan Agarwal

IPC: G06F40/40 ,  G06F9/451 ,  G06F9/50 ,  G06T11/00

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A flow generation service may receive a natural language input that indicates instructions for automating a task according to a first process flow. Using a large language model (LLM), the flow generation service may decompose the natural language input into a set of elements (e.g., logical actions) and connectors, where the LLM may be trained on first metadata corresponding to a second process flow that is created manually by a user. In addition, using the LLM, the flow generation service may generate second metadata corresponding to each of the set of elements based on decomposing the natural language input. The flow generation service may sequence and merge the set of elements to generate the first process flow. In some examples, the flow generation service may send, for display to a user interface of a user device, the first process flow.

公开(公告)号：US20240046115A1

公开(公告)日：2024-02-08

申请号：US17883503

申请日：2022-08-08

Applicant: Salesforce, Inc.

Inventor： Donglin Hu ,  Yuxi Zhang ,  Kexin Xie ,  Chen Xu

IPC: G06N5/02 ,  G06Q30/02

CPC classification number: G06N5/022 ,  G06Q30/0241

Abstract: A prediction system of an online system deploys one or more machine-learned architectures to generate predictions. In one embodiment, the machine-learned architecture is a stacked ensemble model. The stacked ensemble model includes a plurality of base models, where a base model is coupled to receive input data and generate a base prediction for the input data. The stacked ensemble model includes a meta model that combines the base predictions to generate a meta prediction for the input data. The prediction system also generates a reliability measure that takes advantage of the base predictions to evaluate the reliability of the meta prediction. In this manner, while the quality of individual predictions may differ from one another depending on the values of the input data, the prediction system can dynamically generate the reliability measure to account for this variation.

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.