Nvidia Corporation

Docusign, Inc.

Boon Sun Song

David Wyatt

Mark Pereira

Taiwo Raphael Alabi

Shrinivas Kiran Kaza

Kim Cuong Phung

Eric M Zenz

Michael Anthony Palazzolo

Roshan Satish

Patrick Beukema

An auto-tagging engine receives a training set of data comprising documents including a set of tagged fields with each tagged field corresponding to a portion of the document. The auto-tagging engine trains a machine learned model using the training set of data. The trained machine learned model, when applied to a target document in a document management environment, identifies portions of the target document each corresponding to fields of the target document. For each field of the target document, the auto-tagging engine identifies text of the target document associated with the identified portions of the target document corresponding to fields. Natural language processing is performed on the identified text in order to identify field types for the fields. The target document is automatically modified to include a tag identifying the portion of the target document corresponding to each field and identifying a field type of the field.

Automated document tagging in a digital management platform

One embodiment of the present invention sets forth a method for managing a power state of an audio device resident in a graphics processing unit. The method includes the steps of directing audio data originated from a client application via an audio path in an audio driver stack to the audio device, determining whether an active stream of audio data along the audio path is present in response to a notification of an attempt to shut down the graphics processing unit, and requesting a plug and play manager to disable the audio device, if no active stream of audio data is present along the audio path.

Method and system for managing the power state of an audio device integrated in a graphics device

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San Jose, CA, United States of America

Boon Sun Song