Latency management with deep learning based prediction in gaming applications
AssigneeInterDigital Madison Patent Holdings, SAS
InventorsFranck Galpin, Philippe Bordes, Fabrice Le Leannec, Karam Naser, Louis Chevallier
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Abstract
A method for reducing a latency in a gaming application comprising: obtaining (305B) a first frame, said first frame being representative of a first action performed by a user in the gaming application; obtaining (500) information representative of a second action performed by the user in the gaming application, said second action following the first action; and, predicting (500) a second frame corresponding to the second action from data comprising at least the first frame and the information representative of a second action using a neural network.
Claims20
A method for reducing a latency in an interactive application comprising:
- obtaining a first frame, the first frame being representative of a first action performed by a user in the interactive application;
- obtaining information representative of a second action performed by the user in the interactive application, the second action following the first action;
- and,
- predicting a second frame corresponding to the second action from data comprising at least the first frame and the information representative of the second action using a neural network, the neural network being trained in real-time using data representative of frames, user actions and status of the interactive application collected during a current execution of the interactive application.
A device for reducing a latency in an interactive application comprising electronic circuitry adapted for:
- obtaining a first frame, the first frame being representative of a first action performed by a user in the interactive application;
- obtaining information representative of a second action performed by the user in the interactive application, the second action following the first action;
- and,
- predicting a second frame corresponding to the second action from data comprising at least the first frame and the information representative of the second action using a neural network, the neural network being trained in real-time using data representative of frames, user actions and status of the interactive application collected during a current execution of the interactive application.
Dependencies are inferred from the claim text.