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.

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 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 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 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 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: A system comprises a communications engine configured to receive context information of a user of a digital service, the context information identifying a possible interest of the user in a digital asset of the digital service; a rules engine configured to obtain offer rules associated with the context information; an economic data engine configured to obtain economic data associated with the offer rules; and an offer engine configured to first determine based on the offer rules whether to present a first offer for the digital asset; if the offer engine determines to present the first offer, generate the first offer for the digital asset; if the first offer is refused, second determine based on the offer rules whether to present a successive offer for the digital asset; and if the offer engine determines to present the successive offer, generate the successive offer based on the economic data, the successive offer being designed to be more attractive to the user than the first offer.

Abstract: A digital-content-analysis system includes a network services component adapted to receive a remote service request, a stream monitor adapted to form digital content into at least one data stream, and a stream analyzer interoperably coupled to the network services component and the stream monitor and adapted to receive and analyze the at least one data stream. The stream analyzer includes a lexical analyzer adapted to separate the at least one data stream into a plurality of tokens, a classifier adapted to classify the at least one data stream into at least one category by processing at least a subset of the plurality of tokens and computing a classification rating for at least one category, and a set of parameters. At least a subset of the parameters contains information developed from previous operation of the stream analyzer.

Abstract: A producer node may be included in a hierarchical, tree-shaped processing architecture, the architecture including at least one distributor node configured to distribute queries within the architecture, including distribution to the producer node and at least one other producer node within a predefined subset of producer nodes. The distributor node may be further configured to receive results from the producer node and results from the at least one other producer node and to output compiled results therefrom.