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.

Abstract: A trained machine learning model(s) is used to determine scores (e.g., trust scores) for user accounts registered with a video game service, and the scores are used to match players together in multiplayer video game settings. In an example process, a computing system may access data associated with registered user accounts, provide the data as input to the trained machine learning model(s), and the trained machine learning model(s) generates the scores as output, which relate to probabilities of players behaving, or not behaving, in accordance with a particular behavior while playing a video game in multiplayer mode. Thereafter, subsets of logged-in user accounts executing a video game can be assigned to different matches based at least in part on the scores determined for those logged-in user accounts, and the video game is executed in the assigned match for each logged-in user account.

Abstract: A trained machine learning model(s) is used to determine scores (e.g., trust scores) for user accounts registered with a video game service, and the scores are used to match players together in multiplayer video game settings. For example, sensor data received from client machines can be provided as input to the trained machine learning model(s), and the trained machine learning model(s) generates scores as output, which relate to probabilities that the game control data received from those client machines was generated by handheld devices, as opposed to having been synthesized and/or modified using software. In this manner, subsets of logged-in user accounts executing a video game can be assigned to different matches (e.g., by isolating non-human players from human players) based at least in part on the scores determined for those logged-in user accounts, and the video game is executed in the assigned match for each logged-in user account.

Abstract: A trained machine learning model(s) is used to determine scores (e.g., trust scores) for user accounts registered with a video game service, and the scores are used to match players together in multiplayer video game settings. In an example process, a computing system may access data associated with registered user accounts, provide the data as input to the trained machine learning model(s), and the trained machine learning model(s) generates the scores as output, which relate to probabilities of players behaving, or not behaving, in accordance with a particular behavior while playing a video game in multiplayer mode. Thereafter, subsets of logged-in user accounts executing a video game can be assigned to different matches based at least in part on the scores determined for those logged-in user accounts, and the video game is executed in the assigned match for each logged-in user account.

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.

Abstract: A trained machine learning model(s) is used to determine scores (e.g., trust scores) for user accounts registered with a video game service, and the scores are used to match players together in multiplayer video game settings. For example, sensor data received from client machines can be provided as input to the trained machine learning model(s), and the trained machine learning model(s) generates scores as output, which relate to probabilities that the game control data received from those client machines was generated by handheld devices, as opposed to having been synthesized and/or modified using software. In this manner, subsets of logged-in user accounts executing a video game can be assigned to different matches (e.g., by isolating non-human players from human players) based at least in part on the scores determined for those logged-in user accounts, and the video game is executed in the assigned match for each logged-in user account.

Abstract: A trained machine learning model(s) is used to determine scores (e.g., trust scores) for user accounts registered with a video game service, and the scores are used to match players together in multiplayer video game settings. In an example process, a computing system may access data associated with registered user accounts, provide the data as input to the trained machine learning model(s), and the trained machine learning model(s) generates the scores as output, which relate to probabilities of players behaving, or not behaving, in accordance with a particular behavior while playing a video game in multiplayer mode. Thereafter, subsets of logged-in user accounts executing a video game can be assigned to different matches based at least in part on the scores determined for those logged-in user accounts, and the video game is executed in the assigned match for each logged-in user account.

Abstract: A trained machine learning model(s) is used to determine scores (e.g., trust scores) for user accounts registered with a video game service, and the scores are used to match players together in multiplayer video game settings. In an example process, a computing system may access data associated with registered user accounts, provide the data as input to the trained machine learning model(s), and the trained machine learning model(s) generates the scores as output, which relate to probabilities of players behaving, or not behaving, in accordance with a particular behavior while playing a video game in multiplayer mode. Thereafter, subsets of logged-in user accounts executing a video game can be assigned to different matches based at least in part on the scores determined for those logged-in user accounts, and the video game is executed in the assigned match for each logged-in user account.