Tony W Tam

7860912, Dec 28, 2010

Dec 8, 2006

11/608275

Paul J. Gyugyi - Sunnyvale CA, US
Tony C. Tam - Redwood City CA, US

Nvidia Corporation - Santa Clara CA

G06F 7/58

708254

An embodiment of the invention includes a first pseudo-random number generator that is configured to produce a first sequence of values at a first clock rate. Also, a second pseudo-random number generator is configured to produce a second sequence of values at a second clock rate. The second clock rate is based on the first sequence of values and the first clock rate. A logical module is connected to the first pseudo-random number generator and the second pseudo-random number generator. The logical module is configured to produce an output value based on at least a portion of a value from the first sequence of values and at least a portion of a value from the second sequence of values.

20220413497, Dec 29, 2022

Aug 26, 2022

17/896825

- Santa Clara CA, US
Michael COX - Menlo Park CA, US
Miguel SAINZ - Palo Alto CA, US
Martin HEMPEL - Mountain View CA, US
Ratin KUMAR - Cupertino CA, US
Timo ROMAN - Uusimaa, FI
Gordon GRIGOR - San Francisco CA, US
David NISTER - Bellevue WA, US
Justin EBERT - Boulder CO, US
Chin-Hsien SHIH - Saratoga CA, US
Tony TAM - Redwood City CA, US
Ruchi BHARGAVA - Redmond WA, US

G05D 1/00
G06Q 50/30
G05B 13/02
G05D 1/02
G06Q 10/02

A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

20190265703, Aug 29, 2019

Feb 26, 2019

16/286330

- Santa Clara CA, US
Michael COX - Menlo Park CA, US
Miguel SAINZ - Palo Alto CA, US
Martin HEMPEL - Mountain View CA, US
Ratin KUMAR - Cupertino CA, US
Timo ROMAN - Uusimaa, FI
Gordon GRIGOR - San Francisco CA, US
David NISTER - Bellevue WA, US
Justin EBERT - Lafayette CO, US
Chin SHIH - Saratoga CA, US
Tony TAM - Redwood City CA, US
Ruchi BHARGAVA - Redmond WA, US

G05D 1/00
G06Q 50/30
G05B 13/02
G05D 1/02

A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.