Salesforce, Inc.

Tian Xie

Caiming Xiong

Xinyi Yang

Wenhao Liu

Huan Wang

Jin Qu

Kazuma Hashimoto

Feihong Wu

Yingbo Zhou

Xugang Ye

Kevin Zhang

Chu Hong Hoi

Zachary Alexander

Bryan McCann

Nitish Shirish Keskar

Chien-Sheng Wu

Roojuta Lalani

Jean-Marc Soumet

Thomas Archie Cook, Jr

Manju Vijayakumar

Yixin Mao

Wenpeng Yin

Michael Machado

Johnson Liu

Divyansh Agarwal

Soujanya Lanka

Juan Rodriguez

Rohiniben Thakar

Srikanth Kollu

Amit Hetawal

Qiong Liu

Rafael Amsili

Karl Ryszard Skucha

James Douglas Harrison

Vishal Motwani

Zhihao Zou

Minal Khodani

Adarsha Badarinath

Kevin Schoen

Chitra Gulabrani

Embodiments described herein provide a multi-modal search-and-summarize tool for message platforms. Specifically, the multi-modal search-and-summarize tool may monitor conversational content of different formats, e.g., text, image, video, etc., and use multi-modal summarization models to generate a summary of the conversation channel. The summarization may be conducted via a search-and-summarize process in response to a specific user query, e.g., a user may enter 'what did John and Josh say about the presentation tomorrow?' The multi-modal summarization model would first search for relevant conversation messages between user John and user Josh, identify communication files of different format (e.g., text messages, emojis, multimedia attachments, etc.), and then input the communication files to respective text or image encoders to generate a summary of the communication content.

Systems and methods for multi-modal conversation summarization on a conversation platform

Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a record associated with a conversation involves obtaining a plurality of utterances associated with the conversation, identifying, from among the plurality of utterances, a representative utterance for semantic content of the conversation, assigning the conversation to a group of semantically similar conversations based on the representative utterance, and automatically updating the record associated with the conversation at a database system to include metadata identifying the group of semantically similar conversations.

Database systems with automated structural metadata assignment

A conversation engine performs conversations with users using chatbots customized for performing a set of tasks that can be performed using an online system. The conversation engine loads a chatbot configuration that specifies the behavior of a chatbot including the tasks that can be performed by the chatbot, the types of entities relevant to each task, and so on. The conversation may be voice based and use natural language. The conversation engine may load different chatbot configurations to implement different chatbots. The conversation engine receives a conversation engine configuration that specifies the behavior of the conversation engine across chatbots. The system may be a multi-tenant system that allows customization of the chatbots for each tenant.

Customizing chatbots based on user specification

Configurable conversation engine for executing customizable chatbots

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

An online system that allows users to interact with it using expressions in natural language form includes an intent inference module allowing it to infer an intent represented by a user expression. The intent inference module has a set of possible intents, along with a small set of example natural language expressions known to represent that intent. When a user interacts with the system using a natural language expression for which the intent is not already known, the intent inference module applies a natural language inference model to compute scores indicating whether the user expression textually entails the various example natural language expressions. Based on the scores, the intent inference module determines an intent that is most applicable for the expression. If an intent cannot be determined with sufficient confidence, the intent inference module may further attempt to determine whether the various example natural language expressions textually entail the user expression.

Predicting user intent for online system actions through natural language inference-based machine learning model

A system performs conversations with users using chatbots customized for performing a set of tasks. The system may be a multi-tenant system that allows customization of the chatbots for each tenant. The system receives a task configuration that maps tasks to entity types and an entity configuration that specifies methods for determining entities of a particular entity type. The system receives a user utterance and determines the intent of the user using an intent detection model, for example, a neural network. The intent represents a task that the user is requesting. The system determines one or more entities corresponding to the task. The system performs tasks based on the determined intent and the entities and performs conversations with users based on the tasks.

Entity resolution for chatbot conversations

A system authenticates users using voice-based conversations. The system allows the authentication process to be customized using an authentication plan. For example, the system may be a multi-tenant system that allows customization of the authentication process for each tenant. The authentication plan is represented as an expression of phrase types, each phrase type associated with a phrase verification method. The system authenticates a user by executing the expression of an authentication plan for that user in response to a request from the user. The system performs a conversation with the user according to the authentication plan. The system determines whether to allow or deny the user request based on the result of evaluation of the expression of the authentication plan.

Customizable voice-based user authentication in a multi-tenant system

A system performs conversations with users using chatbots customized for performing a set of tasks. The system may be a multi-tenant system that allows customization of the chatbots for each tenant. The system processes sentences that may include negation or coreferences. The system determines a confidence score for an input sentence using an intent detection model, for example, a neural network. The system modifies the sentence to generate a modified sentence, for example, by removing a negation or by replacing a pronoun with an entity. The system generates a confidence score for the modified sentence using the intent detection model. The system determines the intent of the sentence based on the confidence scores of the sentence and the modified sentence. The system performs tasks based on the determined intent and performs conversations with users based on the tasks.

Intent resolution for chatbot conversations with negation and coreferences

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Los Altos, CA, United States of America

Tian Xie