Abstract: Using voice recognition, a user can interact with a companion application to control a video game from a mobile device. Advantageously, the user can interact with the companion application when the video game is unavailable because, for example, of the user's location. Moreover, machine learning may be used to facilitate generating voice responses to user utterances that are predicted to improve or maintain a user's level of engagement with the companion application, or its corresponding video game.

Abstract: Embodiments of the systems and methods disclosed herein provide a sponsor matching system in which players and sponsors can be matched. Upon a match based at least in part on stored sponsorship criteria and/or player preferences, a first sponsor can select a set of players to receive permission to select an advertisement associated with the first sponsor. Once a first player of the selected players selects an advertisement and an advertisement placement location associated with the first sponsor, the sponsor matching system can generate game rendering instructions for a first player system associated with the first player.

Abstract: Embodiments of the systems and methods disclosed herein provide a sponsor matching system in which players and sponsors can be matched. Upon a match based at least in part on stored sponsorship criteria and/or player preferences, a first sponsor can select a set of players to receive permission to select an advertisement associated with the first sponsor. Once a first player of the selected players selects an advertisement and an advertisement placement location associated with the first sponsor, the sponsor matching system can generate game rendering instructions for a first player system associated with the first player.

Abstract: Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.

Abstract: Systems and methods for generating a customized virtual character are disclosed. A system may obtain video data or other media depicting a real person, and then may provide the obtained media to one or more machine learning models configured to learn visual appearance and behavior information regarding the particular person depicted in the video or other media. The system may then generate a custom visual appearance model and a custom behavior model corresponding to the real person, which may subsequently be used to render, within a virtual environment of a video game, a virtual character that resembles the real person in appearance and in-game behavior.

Abstract: Embodiments of the systems and methods described herein provide a virtual object aging system. The virtual object aging system can utilize artificial intelligence to modify virtual objects within a video game to age and/or deteriorate for a certain time period. The virtual object aging system can be used to determine erosion, melting ice, and/or other environmental effects on virtual objects within the game. The virtual object aging system can apply aging, rust, weathering, and/or other effects that cause persistent change to object meshes and textures.

Abstract: Systems described herein may automatically and dynamically adjust the amount and type of computing resources usable to execute, process, or perform various tasks associated with a video game. Using one or more machine learning algorithms, a prediction model can be generated that uses the historical and/or current user interaction data obtained by monitoring the users playing the video game. Based on the historical and/or current user interaction data, future user interactions likely to be performed in the future can be predicted. Using the predictions of the users' future interactions, the amount and type of computing resources maintained in the systems can be adjusted such that a proper balance between reducing the consumption of computing resources and reducing the latency experienced by the users of the video game is achieved and maintained.

Abstract: Embodiments of the systems and methods described herein provide game terrain generation system that can generate height field data from a sketch of graphical inputs from a user via a graphical user interface. The game terrain generation system can use a model, such as a trained neural network, to apply macro and micro topological features on top of the height field data to generate game terrain data. The game terrain generation system can identify boundaries between different styles of terrain and generate transitions between the styles to create a more realistic terrain boundary.

Abstract: Embodiments of the systems and methods disclosed herein provide a request distribution system in which a request for resources may be executed by a plurality of workers. Upon receiving a request for resources from a user computing system, the request distribution system may select a subset of workers from the plurality of workers to execute the request within a time limit. Once the workers generate a plurality of outputs, each output associated with a quality level, the request distribution system may transmit the output associated with the highest quality level to the user computing system.

Abstract: Sequence predictors may be used to predict one or more entries in a musical sequence. The predicted entries in the musical sequence enable a virtual musician to continue playing a musical score based on the predicted entries when the occurrence of latency causes a first computing system hosting a first virtual musician to not receive entries or timing information for entries being performed in the musical sequence by a second computing system hosting a second virtual musician. The sequence predictors may be generated using a machine learning model generation system that uses historical performances of musical scores to generate the sequence predictor. Alternatively, or in addition, earlier portions of a musical score may be used to train the model generation system to obtain a prediction model that can predict later portions of the musical score.

Abstract: Systems and methods are disclosed for converting a player-controlled character or virtual entity in a video game to at least temporarily be under emulated control when certain criteria is met, such as when the player's device has lost its network connection to a game server. The character or virtual entity may continue to behave in the game in a manner that emulates or mimics play of the actual player until the end of the game session or until the underlying connection problem or other issue is resolved, such that other players participating in the game session have the same or similar gameplay experience as they would have had if the disconnected player had continued to play.

Abstract: Sequence predictors may be used to predict one or more entries in a musical sequence. The predicted entries in the musical sequence enable a virtual musician to continue playing a musical score based on the predicted entries when the occurrence of latency causes a first computing system hosting a first virtual musician to not receive entries or timing information for entries being performed in the musical sequence by a second computing system hosting a second virtual musician. The sequence predictors may be generated using a machine learning model generation system that uses historical performances of musical scores to generate the sequence predictor. Alternatively, or in addition, earlier portions of a musical score may be used to train the model generation system to obtain a prediction model that can predict later portions of the musical score.

Abstract: Methods for providing contextual video recommendations within a video game are provided. In one aspect, a method includes executing an application that uses a rendering engine. The method also includes determining that a video recommendation threshold has been met. The method also includes providing a current contextual state of the application to a server such that the server selects a video from a plurality of videos based on the provided current contextual state and an index, wherein the index includes output from a vision model applied on the plurality of videos, and wherein the vision model is trained on footage generated by the rendering engine. The method also includes receiving a reference to the selected video from the server. The method also includes providing for display, via the reference, the selected video within a user interface of the application. Systems and machine-readable media are also provided.

Abstract: Embodiments of the systems and methods described herein provide a virtual object aging system. The virtual object aging system can utilize artificial intelligence to modify virtual objects within a video game to age and/or deteriorate for a certain time period. The virtual object aging system can be used to determine erosion, melting ice, and/or other environmental effects on virtual objects within the game. The virtual object aging system can apply aging, rust, weathering, and/or other effects that cause persistent change to object meshes and textures.

Abstract: Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.

Abstract: Systems and methods are provided for real-time audio generation for electronic games based on personalized music preferences. An example method includes requesting listening history information from one or more music streaming platforms, the listening history information indicating, at least, music playlists to which a user created or is subscribed. A style preference associated with the user is determined based on the listening history information. A musical cue associated with an electronic game is accessed, with the musical being associated with music to be output by the electronic game based on a game state of the electronic game. Personalized music is generated utilizing one or more machine learning models based on the musical cue and the style preference, wherein the system is configured to provide the personalized music for inclusion in the electronic game.

Abstract: Systems described herein may automatically and dynamically adjust the amount and type of computing resources usable to execute, process, or perform various tasks associated with a video game. Using one or more machine learning algorithms, a prediction model can be generated that uses the historical and/or current user interaction data obtained by monitoring the users playing the video game. Based on the historical and/or current user interaction data, future user interactions likely to be performed in the future can be predicted. Using the predictions of the users' future interactions, the amount and type of computing resources maintained in the systems can be adjusted such that a proper balance between reducing the consumption of computing resources and reducing the latency experienced by the users of the video game is achieved and maintained.

Abstract: Systems and methods are provided for personalized real-time audio generation based on user physiological response. An example method includes obtaining hearing information associated with a user's hearing capabilities, the hearing information indicating one or more constraints on the user's hearing, and the hearing information being determined based on one or more hearing tests performed by the user; requesting listening history information from one or more music streaming platforms, the listening history information indicating, at least, music playlists to which a user created or is subscribed; determining, based on the listening history information, a style preference associated with the user; generating, utilizing one or more machine learning models, personalized music based on the hearing information and the style preference, wherein the personalized music comports with the constraints, and wherein the system is configured to provide the personalized music for output via a user device of the user.

Abstract: Embodiments of the systems and methods described herein provide a three dimensional reconstruction system that can receive an image from a camera, and then utilize machine learning algorithms to identify objects in the image. The three dimensional reconstruction system can identify a geolocation of a user, identify features of the surrounding area, such as structures or geographic features, and reconstruct the scene including the identified features. The three dimensional reconstruction system can generate three dimensional object data for the features and/or objects, modify the three dimensional objects, arrange the objects in a scene, and render a two dimensional view of the scene.

Abstract: Embodiments of the systems and methods disclosed herein provide a sponsor matching system in which players and sponsors can be matched. Upon a match based at least in part on stored sponsorship criteria and/or player preferences, a first sponsor can select a set of players to receive permission to select an advertisement associated with the first sponsor. Once a first player of the selected players selects an advertisement and an advertisement placement location associated with the first sponsor, the sponsor matching system can generate game rendering instructions for a first player system associated with the first player.