公开(公告)号：US20230171210A1

公开(公告)日：2023-06-01

申请号：US17590836

申请日：2022-02-02

Applicant: VMWARE, INC.

Inventor： ROHIT PRADEEP SHETTY ,  Ravish CHAWLA ,  Adam CHOW

IPC: H04L51/066 ,  G06F40/284 ,  G06F40/103 ,  G06N20/00

CPC classification number: H04L51/066 ,  G06F40/284 ,  G06F40/103 ,  G06N20/00

Abstract: Disclosed herein are examples of systems and methods for formatting electronic messages using machine learning. An electronic message can be obtained, and a processed message can be generated based at least in part on the electronic message. At least one attribute for the processed message can be determined. A formatting specification can be generated based at least in part on the at least one attribute. A reformatted message can be generated based at least in part on the formatting specification.

公开(公告)号：US20240005430A1

公开(公告)日：2024-01-04

申请号：US17895142

申请日：2022-08-25

Applicant: VMWARE, INC.

Inventor： Rohit Pradeep SHETTY ,  Ramani PANCHAPAKESAN ,  Ravish CHAWLA

IPC: G06Q50/20 ,  G06N20/00

CPC classification number: G06Q50/2057 ,  G06N20/00

Abstract: Disclosed are various approaches for surfacing contextual training programs for users. In some examples, user context data is identified for a user account. The user context data is inputted into a training recommendation model. A training recommendation is generated. The training recommendation recommends a training program that is mapped to the user context data by the training recommendation model. The training recommendation or the training program is surfaced to a client device that is identified by the contextual training service.

公开(公告)号：US20240346021A1

公开(公告)日：2024-10-17

申请号：US18301739

申请日：2023-04-17

Applicant: VMware, Inc.

Inventor： Chaoting XUAN ,  Ravish CHAWLA ,  Erich STUNTEBECK

IPC: G06F16/2452 ,  G06F40/20

CPC classification number: G06F16/24522 ,  G06F40/20

Abstract: The present disclosure provides an approach for training a machine learning model. Embodiments include receiving text comprising a natural language request. Embodiments include providing one or more inputs to a source machine learning model based on the text, wherein the source machine learning model has been trained using source training data corresponding to a plurality of databases. Embodiments include receiving, from the source machine learning model in response to the one or more inputs, a database query in a syntax corresponding to a target database. Embodiments include generating training data for a target machine learning model based on the text and the database query received from the source machine learning model, wherein the target machine learning model has been trained using a smaller amount of training data than the source training data that was used to train the source machine learning model.