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: 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 presented herein use an audio based authentication system for pairing a user account with an audio-based periphery computing system. The audio-based authentication system allows a user to interface with the periphery device through a user computing device. The user can utilize a previously authenticated user account on the user computing device in order to facilitate the pairing of the audio-based periphery computing system with the user account.

Abstract: Embodiments of systems presented herein may identify users to include in a match plan. A parameter model may be generated to predict the retention time of a set of users. A queue of potential users, a set of teammates, and/or opponents may be selected from a queue of waiting users. User information for the set of teammates and/or opponents may be provided to the parameter model to generate a predicted retention time. The set of teammates and/or opponents may be approved if the predicted retention time meets a predetermined threshold. Advantageously, by creating a match plan based on retention rates, the engagement and/or retention level for a number of users may be improved compared to existing multiplayer matching systems.

Abstract: Embodiments presented herein include systems and methods for performing dynamic difficulty adjustment. Further, embodiments disclosed herein perform dynamic difficulty adjustment using processes that may not be detectable or are more difficult to detect by users compared to static and/or existing difficulty adjustment processes. In some embodiments, historical user information utilized by a machine learning system to generate a prediction model that predicts an expected duration of game play, such as for example, an expected churn rate, a retention rate, the length of time a user is expected to play the game, or an indication of the user's expected game play time relative to a historical set of users who have previously played the game. Before or during game play, the prediction model can be applied to information about the user to predict the user's expected duration of game play.

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: Embodiments of the systems and methods described herein can automatically measure the difficulty metrics associated with various aspects of a video game using an artificial intelligence system. The artificial intelligence system may include multiple game agents. Telemetry data associated with the gameplay of each game agent may be recorded while the game application is automatically executed by the game agents. The telemetry data may be communicated to a data analysis system which can calculate game difficulty metrics for various aspects of the game. The data analysis system can determine game difficulty associated with the various aspects based on the game difficulty metrics. The results from the data analysis system may be visualized and communicated to a game developer for updating the operations of the video game.

Abstract: Certain implementations of the disclosed technology may include methods and computing systems for memory-assisted compression of network packets using packet-level clustering. According to an example implementation, a method is provided. The method may include vectorizing a plurality of data packets stored in a memory, calculating respective distances between each of the respective vectorized data packets, clustering the plurality of data packets into a plurality of data packet clusters, obtaining a sample data packet to be compressed, identifying a training data packet cluster from among the plurality of data packet clusters, and compressing the sample data packet using a compression algorithm.

Abstract: Embodiments of the present disclosure provide a tutorial system that can aid a user in performing various game commands in response to different game states in a virtual game environment. As the user plays the game, various game states may be encountered. A tutorial engine may, based on a current game state, determine one or more game commands to be recommended to the user, based on historical information of the user and a game state model, wherein the game state model maintains associations between game states and different segments of users. The user is recommended relevant game commands during the normal course of gameplay, based on their own gameplay history and on game commands commonly performed by other users of the game application.

Abstract: Embodiments of an automated anomaly detection system are disclosed that can detect anomalous data from heterogeneous data sources. The anomaly detection system can provide an automated system that identifies data anomalies within data sets received from application host systems. The anomaly detection system may identify patterns using machine learning based on data set characteristics associated with the each data set. The anomaly detection system may generate a model that can be applied to existing data sets received from the application host systems in order to automatically identify anomalous data sets. The anomaly detection system may automatically identify the anomalous data sets and implement appropriate actions based on the determination.

Abstract: Various aspects of the subject technology relate to systems, methods, and machine-readable media for a proxy agent as an intermediary that hosts electronic exchange transactions between distributing computing devices to reduce the number of fraudulent transactions with respect to items of a multiplayer game. The proxy agent listens for events such as an item being listed for sale on an electronic marketplace associated with the multiplayer game, or a listed item being purchased from the electronic marketplace, before the proxy agent transitions to a state for initiating purchase transactions to obtain additional items from users and thereafter automatically listing the purchased items for sale on the electronic marketplace. The proxy agent serves as a conduit for sale and purchase transactions to restrict transactions from occurring outside of the electronic marketplace and to map pricing data for a given item to market data trends over the transactional life of the item.

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: Various aspects of the subject technology relate to systems, methods, and machine-readable media for interactive computer-operated agents for user engagement in an interactive environment. Computer-operated agents are introduced to help populate a session and are configured to maximize engagement rates among users associated with user-controlled agents. During these interactions, engagement metrics are collected that indicate different interaction rates at different times by the computer-operated agents. The number of popular computer-operated agents (with relatively high interaction rates) can be kept in circulation while some less popular computer-operated agents (with relatively smaller interaction rates) can be kept in circulation for diversity or are purged from circulation.

Abstract: Embodiments of the systems and methods described herein can automatically measure the difficulty metrics associated with various aspects of a video game using an artificial intelligence system. The artificial intelligence system may include multiple game agents. Telemetry data associated with the gameplay of each game agent may be recorded while the game application is automatically executed by the game agents. The telemetry data may be communicated to a data analysis system which can calculate game difficulty metrics for various aspects of the game. The data analysis system can determine game difficulty associated with the various aspects based on the game difficulty metrics. The results from the data analysis system may be visualized and communicated to a game developer for updating the operations of the video game.

Abstract: Embodiments presented herein include systems and methods for performing dynamic difficulty adjustment using processes that may not be detectable or are more difficult to detect by users compared to static and/or existing difficulty adjustment processes. Historical user information may be utilized by a machine learning system to generate a prediction model that predicts an expected duration of game play. Before or during game play, the prediction model can be applied to information about the user to predict the user's expected duration of game play. Based on the expected duration, in some embodiments, the system may then utilize a mapping data repository to determine how to dynamically adjust the difficulty of the game, such as, for example, changing the values of one or more gameplay parameters to make portions of the game less difficult.

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: Embodiments of the present disclosure provide a tutorial system that can aid a user in performing various game commands in response to different game states in a virtual game environment. As the user plays the game, various game states may be encountered. A tutorial engine may, based on a current game state, determine one or more game commands to be recommended to the user, based on historical information of the user and a game state model, wherein the game state model maintains associations between game states and different segments of users. The user is recommended relevant game commands during the normal course of gameplay, based on their own gameplay history and on game commands commonly performed by other users of the game application.

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 systems presented herein may identify users to include in a match plan. A parameter model may be generated to predict the retention time of a set of users. A queue of potential users, a set of teammates, and/or opponents may be selected from a queue of waiting users. User information for the set of teammates and/or opponents may be provided to the parameter model to generate a predicted retention time. The set of teammates and/or opponents may be approved if the predicted retention time meets a predetermined threshold. Advantageously, by creating a match plan based on retention rates, the engagement and/or retention level for a number of users may be improved compared to existing multiplayer matching systems.

Abstract: Various aspects of the subject technology relate to systems, methods, and machine-readable media for a proxy agent as an intermediary that hosts electronic exchange transactions between distributing computing devices to reduce the number of fraudulent transactions with respect to items of a multiplayer game. The proxy agent listens for events such as an item being listed for sale on an electronic marketplace associated with the multiplayer game, or a listed item being purchased from the electronic marketplace, before the proxy agent transitions to a state for initiating purchase transactions to obtain additional items from users and thereafter automatically listing the purchased items for sale on the electronic marketplace. The proxy agent serves as a conduit for sale and purchase transactions to restrict transactions from occurring outside of the electronic marketplace and to map pricing data for a given item to market data trends over the transactional life of the item.