公开(公告)号：US20250086309A1

公开(公告)日：2025-03-13

申请号：US18410722

申请日：2024-01-11

Applicant: Salesforce, Inc.

Inventor： Shashank Harinath ,  Eugene Wayne Becker ,  Subha Melapalayam ,  Eric Brochu ,  Claire Cheng ,  Mario Rodriguez ,  Prithvi Krisnan Padmanabhan ,  Kathy Baxter ,  Kin Fai Kan

IPC: G06F21/62 ,  G06F40/20

Abstract: A cloud platform may include a model interface that receives from a client and at an interface for accessing a large language model, a prompt for a response from the large language model, and the client is associated with a set of configuration parameters via a cloud platform that supports the interface. The cloud platform may modify, in accordance with the set of configuration parameters, the prompt that results in a modified prompt and transmit, to the large language model, the modified prompt. The cloud platform may receive the response generated by the large language model and provide the response to a model that determines one or more probabilities that the response contains content from one or more content categories. The cloud platform may transmit the response or the one or more probabilities to the client.

公开(公告)号：US20230259824A1

公开(公告)日：2023-08-17

申请号：US18140400

申请日：2023-04-27

Applicant: Salesforce, Inc.

Inventor： Mayukh Bhaowal ,  Leah McGuire ,  Kin Fai Kan ,  Christopher Rupley ,  Xiaodan Sun ,  Michael Weil ,  Subha Nabar

IPC: G06N20/00 ,  G06N5/045 ,  G06F18/21 ,  G06F18/214

CPC classification number: G06N20/00 ,  G06N5/045 ,  G06F18/2185 ,  G06F18/2148

Abstract: A set of data for training a machine learning system can be modified to improve its performance. An item of information can be transmitted. A message can be transmitted that includes an explanation of a determination, by the machine learning system, to transmit the item of information from among a plurality of items of information. A first set of data can have been used to train the machine learning system. A signal can be received that includes an indication of a usefulness of the message, to a user of a user device, in making a decision to perform an action based on a knowledge associated with the item of information. The first set of data can be modified, in response to a receipt of the signal, to produce a second set of data. The machine learning system can be caused to be trained using the second set of data.

公开(公告)号：US11663517B2

公开(公告)日：2023-05-30

申请号：US15884878

申请日：2018-01-31

Applicant: Salesforce, Inc.

Inventor： Sara Beth Asher ,  John Emery Ball ,  Vitaly Gordon ,  Till Christian Bergmann ,  Kin Fai Kan ,  Chalenge Masekera ,  Shubha Nabar ,  Nihar Dandekar ,  James Reber Lewis

IPC: G06N20/00 ,  G06N5/02 ,  G06N5/04

CPC classification number: G06N20/00 ,  G06N5/02 ,  G06N5/04

Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.

公开(公告)号：US11983184B2

公开(公告)日：2024-05-14

申请号：US17496615

申请日：2021-10-07

Applicant: Salesforce, Inc.

Inventor： Kin Fai Kan ,  Chaney Lin ,  Mayukh Bhaowal ,  Shubha Nabar ,  Seiji J. Yamamoto

IPC: G06F16/2457 ,  G06F16/25 ,  G06F18/214 ,  G06N20/00

CPC classification number: G06F16/24578 ,  G06F16/258 ,  G06F18/214 ,  G06N20/00

Abstract: A method for generating a model for recommendations from an item data set for a target data set includes embedding a set of targets from the target data set in a shared coordinate space using a first embedding function, embedding a first set of items from the item data set in the shared coordinate space using a second embedding function, selecting at least one target from the set of targets, and identifying a second set of items from the first set of items that are proximate to the at least one target as candidates from the recommendations.