Salesforce, Inc.

Splunk Inc.

Slack Technologies, Inc.

Alexander Nicholas Johnson

Adam Jamison Oliner

Zhifeng Deng

Renaud Bourassa-Denis

Aaron J Maurer

Leigh Ann Johnson

Kristal Curtis

Nghi Huu Nguyen

Aaron Mauer

Discovery of communication platform features or exposure of such features to the user may include generating embeddings for a variety of types of communication platform content and communications. These embeddings may be used to characterize and compare various communication platform features and ultimately expose these features to a user when the user may not have otherwise encountered them. The embeddings may additionally or alternatively be used to determine a degree of alignment.

Embeddings-based discovery and exposure of communication platform features

A communication platform may comprise different systems for helping a user discover features of the platform. However, the systems may generate different results. An application programming interface (API) may receive such outputs and may be configured to select between the outputs based on detecting a state at a user's computing device and/or using a machine-learned model to weight the outputs and/or probabilities associated therewith using a target metric. The API may then rank the outputs and select from among them based at least in part on the target metric.

Embeddings-based recommendations of latent communication platform features

A computerized method of diagnosing a mislabeling of a source type of a received event. The method comprising operations of receiving an event by a source type analysis logic with a data index and query system, wherein the event includes a portion of raw machine data and is associated with a specific point in time, obtaining an original source type assigned to the event and one or more predicted source types. The one or more predicted source types are determined by analysis of a data representation of the event in view of training data and the training data includes a plurality of data representations corresponding to known source types. Additionally, the computerized method also includes an operation of, determining whether the event has been mislabeled and in response to determining the event has been mislabeled, diagnosing a source of the mislabeling.

Detecting, diagnosing, and directing solutions for source type mislabeling of machine data, including machine data that may contain PII, using machine learning

Discovery of communication platform features or exposure of such features to the user may include generating embeddings for a variety of types of communication platform content and communications. These embeddings may be used to characterize and compare various communication platform features and ultimately expose these features to a user when the user may not have otherwise encountered them. Embeddings may be generated to characterize a user's interactions with a communication platform and/or characterize a channel and the embeddings may be used to expose methods of communicating with the user and/or over the channel.

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San Francisco, CA, United States of America

Alexander Nicholas Johnson