Nvidia Corporation

Mellanox Technologies, Ltd.

Siddha Ganju

Elad Mentovich

Ruthie D Lyle

Ryan Kelsey Albright

Benjamin Goska

Steven Dalton

Andrew Russell

Tahir Cader

Eitan Zahavi

Michael Lawrence Sabotta

Yaakov Gridish

Michael Thompson

William Ryan Weese

Aaron Richard Carkin

William Andrew Mecham

Jordan Levy

Elena Oleynikova

Michael Houston

David Greenlaw

Tony Altinis

Fred Devoir

Elijah Chen

Michael Norman

Michael Balint

Rotem Barzilai

Itamar Frenkel

Naveen Kumar Rai

Ronay Ak

Marco Foco

In various examples, a 3D representation of an environment may be generated from sensor data, with objects being detected in the environment using the sensor data and stored as items that can be tracked and located within the 3D representation. The 3D representation of the environment and item information may be used to determine (e.g., identify or predict) a location or position of an item within the 3D representation and/or recommend a storage location for the item within the 3D representation. Using a determined location or position, one or more routes to the location through the 3D representation may be determined. Data corresponding to a determined route may be provided to a user and/or device. User preferences, permissions, roles, feedback, historical item data, and/or other data associated with a user may be used to further enhance various aspects of the disclosure.

3D environment reconstruction for persistent object tracking

Apparatuses, systems, and techniques to cause one or more directions of travel to be indicated to a user in order to improve wireless signal strength. In at least one embodiment, one or more directions of travel are indicated to a device in order to improve wireless signal strength, based on, for example, wireless signal strength values obtained by said device at one or more locations.

Wireless signal strength indications

Systems and methods are directed toward evaluating auditory inputs against a range of tolerance to provide feedback regarding pronunciation. An auditory input may be evaluated using a trained machine learning system and evaluated for similarity against a target word. Similarity may be scored and then evaluated to determine whether the similarity falls within a range of tolerance, wherein the range of tolerance may be adjusted or modified for particular uses. A score within the range of tolerance is indicative of a word that has been pronounced such that it would be perceptible.

Pronunciation features for language models

Various embodiments of the present disclosure provide for generating and managing a digital ledger access system and its associated objects. An example method is configured for securing objects in a digital ledger of objects by identifying an object from amongst a plurality of objects in the digital ledger of objects and generating a quantum token for attachment with the object. The method includes deriving one or more classical public keys associated with the quantum token and determining an attempt to access the object. The method provides access to the object in response to a validation of the classical public key based on the quantum token, and the method precludes access to the object in response to an invalidation of the classical public key based on the quantum token.

Systems, methods, and apparatuses for securing ownership of objects in a digital ledger

A method includes receiving, using a processing device, a first condition associated with an operation at a data center, where the operation at the data center pertains to a first location at the data center, the first location corresponding to a first parameter value. The method further includes providing the first condition as an input to a machine learning model. The method also includes performing one or more reinforcement learning techniques using the machine learning model to cause the machine learning model to output an indication of a final location associated with the operation, where the final location corresponds to a final parameter value that is closer to a target than the first parameter value corresponding to the first location at the data center.

Data center job scheduling using machine learning

Systems, computer program products, and methods are described herein for intelligent data compression, in accordance with an embodiment of the invention. The present invention may be configured to receive a plurality of files for storage in a database and perform a series of steps iteratively, for each file of the plurality of files, and until each file of the plurality of files is represented in the database. The series of steps may include identifying one or more data points in the respective file, where each identified data point was previously unidentified in the database and adding the identified one or more data points to the database. The series of steps may also include identifying one or more features of the respective file for storage in the database and storing the identified one or more features in the database as a surrogate for the respective file.

Systems and methods for intelligent data compression

A digital avatar system can process video streams and generate synthetic video with a digital avatar. The digital avatar provides the appearance of a participant from the video stream talking and one or more of performing various behaviors or actions consistent with the participant's behavior when they are live streamed. A digital avatar system can detect triggering events during a live stream and automatically switch to an avatar mode.

Systems and methods to optimize video streaming using digital avatars

Systems and methods herein address power for one or more processing units, using one of a plurality of power profiles during execution of a group of real-time instructions, the one of the plurality of power profiles determined based in part on a relationship determined between the one of the plurality of power profiles and a power profile of the group of real-time instructions, the relationship limited by a threshold, and the plurality of power profiles are associated with a plurality of groups of reference instructions.

Instruction prefetch based power control

A computing entity is described that obtains at least one inspection image of an at least partially fabricated product and causes the at least one inspection image to be processed by a product inspection engine. The product inspection engine includes a machine learning-trained model. The computing entity obtains an inspection result determined based on the processing of the at least one inspection image by the product inspection engine; identifies one or more training images stored in an image database based at least in part on the at least one inspection image; associates automatically generated labeling data with the one or more training images based at least in part on the inspection result determined by the processing of the at least one inspection image; and causes training of the product inspection engine using the one or more training images and the associated labeling data.

Active learning of product inspection engine

Systems and methods for datacenter are disclosed. In at least one embodiment, a system or method herein causes a process for a structure that includes a component tracking system, where such a system includes individual components within individual servers or individual racks, the process being based in part on at least location information and configuration information from one or more of an optical sensor or a radio sensor associated with a motile-support that is adapted for at least three dimensional (3D) movement in a space having the individual servers or the individual racks.

Motile tracking of datacenter components

Systems and methods of presenting interrupting content during human speech are disclosed. The proposed systems offer improved duplex communications in conversational AI platforms. In some embodiments, the system receives speech data and evaluates the data using linguistic models. If the linguistic models detect indications of linguistic irregularities such as mispronunciation, a smart feedback assistant can determine that the system should interrupt the speaker in near-real-time and provide feedback regarding their pronunciation. In addition, conversational irregularities may also be detected, causing the smart feedback assistant to interrupt with presentation of moderating guidance. In some cases, emotion models may also be utilized to detect emotional states based on the speaker's voice in order to offer near-immediate feedback. Users can also customize the manner and occasions in which they are interrupted.

Growing community of inventors

Santa Clara, CA, United States of America

Siddha Ganju