Salesforce Com, Inc.

Salesforce, Inc.

Noah William Burbank

Alexis Roos

Bradford William Powley

Gabriel Starr Krupa

Shardul Vikram

Alex Michael Noe

Joseph Gerald Keller

William Christopher Fama Roller

Wenhao Liu

Percy Dara Mehta

Praveen Innamuri

Rohit Deshpande

Robert Christopher Ames

Narek Asadorian

Scott Walter Bishel

Madeleine Mary Gill

Madhav Vaidyanathan

Wing Hing Ku

Xu Yang

Janelle Wen Hui Teng

Sammy Adnan Nammari

Abraham Dio Suharli

Ascander Dost

Nelson Esteban Acevedo

Shuvajit Das

Oliver Qian Tang

Sandeep Raju Prabhakar

Rohith Ramprasad

Rowan Cassius

Bhaskar Garg

Methods, systems, and devices for distributed hyperparameter tuning and load balancing are described. A device (e.g., an application server) may generate a first set of combinations of hyperparameter values associated with training a mathematical model. The mathematical model may include a machine learning model, an optimization model, or any combination. The device may identify a subset of combinations from the first set of combinations that are associated with a computational runtime that exceeds a first threshold and may distribute the subset of combinations across a set of machines. The device may then test each of the first set of combinations in a parallel processing operation to generate a first set of validation error values and may test a second set of combinations of hyperparameter values using an objective function that is based on the first set of validation error values.

Distributed hyperparameter tuning and load balancing for mathematical models

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, apparatus, and system for modifying user datasets to support statistical resemblance is described. To support modifying user datasets to support statistical resemblance, an application may generate a first profile from a first corpus that includes first user data, generate a set of modified profiles from a second profile from a second corpus including second user data, wherein the first profile and the set of modified profiles includes respective sets of first and second attributes corresponding to one or both of text or metadata associated with the respective first and second user data, determine a mathematical distance between the first profile and each modified profile of the set of modified profiles based at least in part on a comparison between the first set of attributes and the second set of attributes, and finally, select a modified profile having a smallest determined mathematical distance.

Modifications of user datasets to support statistical resemblance

Methods, systems, and devices supporting global message threads are described. A thread server may receive a communication message associated with a first set of user identifiers (e.g., the author and recipients of the message) and may extract a thread identifier from the message. The thread identifier may associate the message with one or more additional messages (e.g., from the same thread of messages), where the one or more additional messages may be associated with different user identifiers. The thread server may upsert the received communication message to a stored set of messages associated with a root indicating the thread identifier. If the thread server receives a query indicating the thread identifier from a user, the thread server may retrieve all of the messages associated with the thread identifier from persistent memory based on the query, including messages that are not associated with the user identifier for the querying user.

Construction of global internet message threads

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 database server may identify mentioned names in a body of a message and extract the names using name identification heuristics and algorithms. The service retrieves or utilizes a distributed connection graph to identify contacts associated with the parties to the conversation that may match or be similar to the mentioned name. Contacts may be scored based on similarities between the extracted name the names associated with nodes of the graph, as well as other factors. The highest scoring contact may be surfaced or displayed to one or more of the parties to the communication message.

Contact information extraction and identification

A database server may analyze interaction data including communication to generate a graph representation of various users and connections between the users. The database server may utilize the graph representation of connections to identify sufficiently connected target user identifiers in one or more external organizations. A connection metric may be assigned to each user identifier of one or more groups of user identifiers generated using the graph representation, and the target user identifiers may be identified based on the connection metrics.

Connected contact identification

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Palo Alto, CA, United States of America

Noah William Burbank