Salesforce Com, Inc.

Salesforce, Inc.

Jean-Marc Soumet

Karl Ryszard Skucha

Shashank Harinath

Kit Pang Szeto

Roojuta Lalani

Thomas Archie Cook, Jr

Manju Vijayakumar

Michael Machado

Matvey Tovbin

Bryan McCann

Qingqing Liu

Simon Chan

Zineb Laraki

Ka Hou Chan

Johnson Liu

Mike Rosenbaum

Emmanual Felipe Oliveira

John Ball

Kevin Zhang

Caiming Xiong

Yan Yang

Nitish Shirish Keskar

Govardana Sachithanandam Ramachandran

Tian Xie

Xinyi Yang

Shiva Kumar Pentyala

Ankit Chadha

Leah McGuire

Juan Rodriguez

Shilpa Bhagavath

Vlad Patryshev

James Douglas Harrison

Vishal Motwani

Zhihao Zou

Minal Khodani

Adarsha Badarinath

Kevin Schoen

Marco Vivero

Chitra Gulabrani

Srikanth Kollu

Joshua Sauter

Rohiniben Thakar

Amit Hetawal

Qiong Liu

Rafael Amsili

Linwei Zhu

Yufan Xue

Abhishek Sharma

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

Approaches to using unstructured input to update heterogeneous data stores include receiving unstructured text input, receiving a template for interpreting the unstructured text input, identifying, using an entity classifier, entities in the unstructured text input, identifying one or more potential parent entities from the identified entities based on the template, receiving a selection of a parent entity from the one or more potential parent entities, identifying one or more potential child entities from the identified entities based on the template and the selected parent entity, receiving a selection of a child entity from the one or more potential child entities, identifying an action item in the unstructured text input based on the identified entities and the template, determining, using an intent classifier, an intent of the action item, and updating a data store based on the determined intent, the identified entities, and the selected child entity.

Using unstructured input to update heterogeneous data stores

A method for natural language processing includes receiving, by one or more processors, an unstructured text input. An entity classifier is used to identify entities in the unstructured text input. The identifying the entities includes generating, using a plurality of sub-classifiers of a hierarchical neural network classifier of the entity classifier, a plurality of lower-level entity identifications associated with the unstructured text input. The identifying the entities further includes generating, using a combiner of the hierarchical neural network classifier, a plurality of higher-level entity identifications associated with the unstructured text input based on the plurality of lower-level entity identifications. Identified entities are provided based on the plurality of higher-level entity identifications.

Systems and methods for named entity recognition

Techniques and architectures for data modeling and management. Data modeling services are provided to agents within multiple different operating environments of a computing environment having at least one database stored on one or more physical memory devices communicatively coupled with one or more hardware processors the one or physical memory devices. Building and versioning of data modeling projects is coordinated and data utilized for the data modeling projects with the one or more hardware processors.

Techniques and architectures for managing disparate heterogeneous cloud-based resources

A system may generate a score for a predictive model based on receiving a streaming data flow of events associated with a predictive model for a tenant. The system may receive the streaming data flow and calculate one or more feature values in real time based on the reception. The system may store each of the calculated features to a multi-tenant database server. The system may calculate a score for the predictive model based on the storage and may transmit an indication of the score (e.g., a prediction) based on the calculation. The system may transmit the score to, for example, a computing device.

Feature generation and storage in a multi-tenant environment

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San Jose, CA, United States of America

Jean-Marc Soumet