Nvidia Corporation

Bojan Skaljak

Arman Toorians

Anthony Michael Tamasi

Michael Kuohao Chu

Jay Huang

Timothy Paul Lottes

Ashutosh Gajanan Rege

Andrew Leighton Edelsten

Fedor Fomichev

Keith Brian Galocy

Jen-Hsun Huang

Tony Tamasi

Andrew Edelsten

Traditionally, a software application is developed, tested, and then published for use by end users. Any subsequent update made to the software application is generally in the form of a human programmed modification made to the code in the software application itself, and further only becomes usable once tested, published, and installed by end users having the previous version of the software application. This typical software application lifecycle causes delays in not only generating improvements to software applications, but also to those improvements being made accessible to end users. To help avoid these delays and improve performance of software applications, deep learning models may be made accessible to the software applications for use in providing inferenced data to the software applications, which the software applications may then use as desired. These deep learning models can furthermore be improved independently of the software applications using manual and/or automated processes.

Automatic dataset creation using software tags

A computer system comprising a processor and a memory for storing instructions, that when executed by the processor performs a copy protection method. The copy protection method comprises executing a software loop of a first software application in a first operating system. A first call is executed in the software loop to a code portion. A decrypted code portion of the first software application is executed in a second operating system in response to the first call. The code portion is decrypted in response to a successful validation of the first software application.

Content protection via online servers and code execution in a secure operating system

A method for reducing line display latency on a touchpad device is disclosed. The method comprises storing information regarding a plurality of prior touch events on a touch screen of the touchpad device into an event buffer. It further comprises determining an average speed and a predicted direction of motion of a user interaction with the touch screen using the plurality of prior touch events. Next, it comprises calculating a first prediction point using the average speed, the predicted direction, and a last known touch event on the touch screen. Subsequently, it comprises applying weighted filtering on the first prediction point using a measured line curvature to determine a second prediction point. Finally, it comprises rendering a prediction line between the last known touch event on the touch screen and the second prediction point.

Methods and apparatus for reducing perceived pen-to-ink latency on touchpad devices

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San Jose, CA, United States of America

Bojan Skaljak