Nvidia Corporation

Prakshep Mehta

Jonah Matthew Alben

Eric Brian Lum

Yury Y Uralsky

Thomas A Petersen

Ankan Banerjee

Gregory Massal

Oleg Kuznetsov

Ambrish Dantrey

Revanth Reddy Nalla

Jithin Thomas

Neilesh Chorakhalikar

In various examples, game session audio data—e.g., representing speech of users participating in the game—may be monitored and/or analyzed to determine whether inappropriate language is being used. Where inappropriate language is identified, the portions of the audio corresponding to the inappropriate language may be edited or modified such that other users do not hear the inappropriate language. As a result, toxic behavior or language within instances of gameplay may be censored—thereby enhancing the user experience and making online gaming environments safer for more vulnerable populations. In some embodiments, the inappropriate language may be reported—e.g., automatically—to the game developer or game application host in order to suspend, ban, or otherwise manage users of the system that have a proclivity for toxic behavior.

Automatic classification and reporting of inappropriate language in online applications

In various examples, game session audio data&#x2014;e.g., representing speech of users participating in the game&#x2014;may be monitored and/or analyzed to determine whether inappropriate language is being used. Where inappropriate language is identified, the portions of the audio corresponding to the inappropriate language may be edited or modified such that other users do not hear the inappropriate language. As a result, toxic behavior or language within instances of gameplay may be censored&#x2014;thereby enhancing the user experience and making online gaming environments safer for more vulnerable populations. In some embodiments, the inappropriate language may be reported&#x2014;e.g., automatically&#x2014;to the game developer or game application host in order to suspend, ban, or otherwise manage users of the system that have a proclivity for toxic behavior.

A raster unit is configured to generate different sample patterns for adjacent pixels within a given frame. In addition, the raster unit may adjust the sample patterns between frames. The raster unit includes an index unit that selects a sample pattern table for use with a current frame. For a given pixel, the index unit extracts a sample pattern from the selected sample pattern table. The extracted sample pattern is used to generate coverage information for the pixel. The coverage information for all pixels is then used to generate an image. The resultant image may then be filtered to reduce or remove artifacts induced by the changing of sample locations.

Enhanced anti-aliasing by varying sample patterns spatially and/or temporally

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Pune, India

Prakshep Mehta