Salesforce, Inc.

Salesforce Com, Inc.

Johnson Liu

Jean-Marc Soumet

Kevin Zhang

Caiming Xiong

Bryan McCann

Eli Levine

Nitish Shirish Keskar

Shashank Harinath

Tian Xie

Kit Pang Szeto

Roojuta Lalani

Ming-Yang Chen

Ankit Chadha

Xinyi Yang

Karl Ryszard Skucha

Thomas Archie Cook, Jr

Shiva Kumar Pentyala

Manju Vijayakumar

Ka Chun Au

Michael Machado

Shilpa Bhagavath

Juan Rodriguez

Christopher James Wu

Qiong Liu

Rafael Amsili

James Douglas Harrison

Amit Hetawal

Bilong Chen

Vishal Motwani

Zhihao Zou

Minal Khodani

Adarsha Badarinath

Kevin Schoen

Chitra Gulabrani

Srikanth Kollu

Rohiniben Thakar

Methods and systems for hierarchical natural language understanding are described. A representation of an utterance is inputted to a first machine learning model to obtain information on the first utterance. According to the information on the utterance a determination that the representation of the utterance is to be inputted to a second machine learning model that performs a dedicated natural language task is performed. In response to determining that the representation of the utterance is to be inputted to a second machine learning model, the utterance is inputted to the second machine learning model to obtain an output of the dedicated natural language task.

Hierarchical natural language understanding systems

A multi-tenant system performs custom configuration of a tenant-specific chatbot to process and act upon natural language requests. The multi-tenant system configures the tenant-specific chatbots without requiring tenant-specific training. The multi-tenant system providing a user interface for configuring a tenant-specific set of permitted actions. The multi-tenant system determines a set of example phrases for each of the selected permitted actions. The multi-tenant system receives a natural language request from a user and identifies the action that the user wants to perform. The multi-tenant system uses a neural network to compare the natural language request with example phrases to identify an example phrase that matches the natural language request. The multi-tenant system performs the action corresponding to the matching example phrase.

Machine learning based tenant-specific chatbots for performing actions in a multi-tenant system

Methods, systems, and devices for data access and processing are described. To set up secure environments for data processing (e.g., including machine learning), an access control system may first receive approval from an authorized user (e.g., an approver) granting access to data objects in a multi-tenant data store. The system may determine tenant-specific paths for retrieving the data objects from the data store, and may initialize a number of virtual computing engines for accessing the data. Each computing engine may be tenant-specific based on the path(s) used by that computing engine, and each may include an access role defining the data objects or data object types accessible by that computing engine. By accessing the requested data objects according to the tenant-specific path prefixes and access roles, the virtual computing engines may securely maintain separate environments for different tenants and may only allow user access to approved tenant data.

Securely accessing and processing data in a multi-tenant data store

Growing community of inventors

Santa Clara, CA, United States of America

Johnson Liu