Salesforce Com, Inc.

Salesforce, Inc.

Shardul Vikram

Percy Dara Mehta

Alexis Roos

Noah William Burbank

Praveen Innamuri

Scott Walter Bishel

William Christopher Fama Roller

Alex Michael Noe

Joseph Gerald Keller

Wenhao Liu

Yogesh Patel

Mattia Padovani

Rohit Deshpande

Ankush Goyal

Robert Christopher Ames

Gabriel Starr Krupa

Wing Hing Ku

Madeleine Mary Gill

Madhav Vaidyanathan

Bhaskar Garg

Xu Yang

Janelle Wen Hui Teng

Abraham Dio Suharli

Sammy Adnan Nammari

Nelson Esteban Acevedo

Ascander Dost

Shuvajit Das

Sandeep Raju Prabhakar

Nathaniel Richard Price

Oliver Qian Tang

Sudarshan Vijay Sarolkar

Jon Tomas Gretarsson

Rohith Ramprasad

Methods, systems, and devices for analyzing communication messages (e.g., emails or activities) to determine relationship strength using a distributed graph are described. In some systems, a user may be associated with a specific tenant. A database server of the system may receive communication messages associated with the user and a target user. The server may perform a natural language processing (NLP) analysis on the communication messages to extract metadata, and may generate or update a distributed graph indicating connections between users based on the extracted metadata. Using the connections of the graph, the server may calculate a closeness score between the user and the target user. Additionally, the server may calculate closeness scores between the target and other users associated with the tenant, and may determine the users with the greatest closeness scores. The server may send a suggestion for the determined users to initiate communication with the target.

Calculating relationship strength using an activity-based distributed graph

A method and system that perform cluster node work assignments for tenants in a multi-tenant system. The method includes selecting a partition of a cluster tenant tracking structure having a hierarchical namespace and virtual nodes representing each tenant by a cluster node foreman controller, searching the selected partition for a tenant that is unlocked in the cluster tenant tracking structure, and initiating work for the tenant by the cluster node.

Mechanism for a work node scan process to facilitate cluster scaling

Methods, systems, and devices for analyzing communication messages (e.g., emails) and selecting corresponding actions are described. In some database systems, a user may receive multiple messages at a user device. To efficiently determine responses to these messages, the user device may send the messages to a backend server for analysis. The server may perform natural language processing (NLP) to classify the message with one or more binary classifications and may extract metadata from each message. Based on the classifications and the metadata, the server may determine one or more actions the user device may perform to respond to each message. The server may send instructions to the user device indicating the suggested actions, and the user device may display these actions as options to a user. Additionally, the user device may use the classifications and metadata to automatically generate one or more communication templates in response to the message.

Action response selection based on communication message analysis

A system may utilize a combination of real-time and polling connectors for data crawling. A real-time connector may use a collector to write data associated with a new event from a database to a channel and record external identifiers (IDs) associated the data. A polling connector may use a verifier to compare the recorded IDs with IDs associated with a batch of data, and may write any data of the batch of data to the channel that has not been written previously written by the collector. The system may include an onboarder for reading data previously stored in the database.

Synergizing real-time and polling connectors for data ingestion

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San Jose, CA, United States of America

Shardul Vikram