Latency compensation using machine-learned prediction of user input

Abstract

A trained machine learning model(s) is used to determine scores indicative of probabilities that certain types of user input will be provided to a player's game controller while playing a video game in order to compensate for latency between player action and player perception of video game content relating to the player action. In an example process, sensor data received from a client machine and/or game state data received from a video game is provided as input to a trained machine learning model(s), and a score as output therefrom, the score relating to a probability that a type of user input will be provided to a player's game controller. In this manner, game control data corresponding to the type of user input can be generated based on the score and provided to the video game as input before actual game control data is even received, thereby compensating for latency.