Serial Number
90507968
Owner
Neuralmagic Inc.Attorney
Todd Braverman, Esq.First Use Date
Feb 4, 2021
Filing Date
Feb 3, 2021
DEEPSPARSE Trademark
Serial Number: 90507968 • Registration: 7495696
Trademark Classes
Class 9 - Computers & Electronics
Scientific, nautical, surveying, photographic, cinematographic, optical apparatus and instruments
Class 41 - Education & Entertainment
Education; providing of training; entertainment; sporting and cultural activities
Class 42 - Computer & Scientific
Scientific and technological services; industrial analysis and research services
Owner Contact Info
Legal Representation
Correspondence Address
Todd Braverman, Esq. PEARL COHEN ZEDEK LATZER BARATZ LLP
TIMES SQUARE TOWER, 7 TIMES SQUARE
NEW YORK, NY 10036
United States
Trademark Details
Filing Date
February 3, 2021
Registration Date
September 3, 2024
First Use Anywhere
February 4, 2021
First Use in Commerce
February 4, 2021
Published for Opposition
July 5, 2022
Goods & Services
Information technology (IT) training in the fields of artificial intelligence, neural networks, deep learning networks, machine learning, non-hardware graphical processing units, algorithms for machine learning and neural networks
Recorded computer software, downloadable computer software, and downloadable computer application software for quantizing and optimizing the computational speed of CPUs and GPUs by increasing computational capacity and accelerating the speed of computation and data transfer within deep learning neural networks and artificial intelligence computer systems
Technological planning and consulting services in the fields of computer hardware and software systems for quantizing and optimizing the computational speed of CPUs and GPUs by increasing computational capacity and accelerating the speed of computation and data transfer within deep learning neural networks and artificial intelligence computer systems; technology consultation services, namely, design, development, selection and implementation of computer hardware and software systems and custom computer algorithms used to quantize computer machine learning (ML) models, computer machine learning algorithms and architecture, incorporate open-source libraries, and optimize nonhardware computer processor units (CPU) and graphical processing units (GPU) algorithms and architecture to model, modify, and increase computational capacity and accelerate the speed of computation and data transfer of non-hardware computer processor units (CPU) and graphical processing units (GPU) within deep learning neural networks and artificial intelligence computer systems
