Cisco Technology, Inc.

Dipock Das

Dayanand Pochugari

Aungon Nag Radon

Adam Jamison Oliner

Nikesh Padakanti

Roussi Roussev

In various embodiments, a natural language (NL) application receives a first incomplete natural language (NL) request, and generates one or more request completion recommendations based on at least the first incomplete NL request and a first recommendation model, where the first recommendation model is generated via a machine learning algorithm applied to a first data dependency model and a first request completion model. The NL application receives a selection of a first request completion recommendation included in the one or more request completion recommendations, generates a complete request based on the first incomplete NL request and the first request completion recommendation, and causes the complete request to be applied to the data storage system.

Providing completion recommendation variations for a partial natural language request

In various embodiments, a natural language (NL) application enables users to more effectively access various data storage systems based on NL requests. The NL application includes functionality for selecting an optimal interpretation algorithm, generating a dashboard, and/or generating an alert based on an NL request. Advantageously, the operations performed by the NL application reduce the amount of time and user effort associated with accessing data storage systems and increase the likelihood of properly addressing NL requests.

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Sunnyvale, CA, United States of America

Dipock Das