Abstract: Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Abstract: Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

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. 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 an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Abstract: Embodiments of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.

Abstract: Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

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 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 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 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 systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.

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: 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 of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the 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: 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.