Disclosed are systems and methods for session management. The disclosed system allows for seamless merging and splitting of network sessions in a multiplayer network gaming environment. Seamless session management allows dynamic movement of players in a virtual world during gameplay without unnecessary loading and/or stalling. As the players in the virtual world move around, the management of active game sessions can be improved to effect a more realistic perceived population.

Disruptive behavior events are identified within an online gaming system. Players responsible for the identified disruptive behavior events are verified. A behavioral designation is applied to players in the online gaming system. The behavioral designation indicates whether or not a player is verified as responsible for one or more of the identified disruptive behavior events. Current real-time player behavior demographic data is generated for a specified game space using the behavioral designations applied to players associated with the specified game space. The generated current real-time player behavior demographic data for the specified game space is displayed to players associated with the specified game space. Using current real-time player behavior demographic data generated and displayed for different game spaces, a player is enabled to make a behavior-informed selection of a game space in which to enter for game play. Also, player behavioral profiles enable tracking of player-specific behavior preferences and events.

Methods and systems for managing processing resources of an online game include executing the online game for one or more users. Each user is provided access to virtual locations in a gaming world of the online game. A compute is assigned for processing gaming interactivity of the one or more users in the gaming world. A number of users accessing a region within a virtual location are determined. When the number of users exceeds a predefined limit for the compute assigned to the users in the region, the region is divided into a plurality of sub-regions and additional compute is assigned for the region so that the compute and the additional compute can process gaming interactivity for the users in each of the sub-regions defined in the gaming world.

Methods and systems for generating incident recommendations in processed data using machine learning models. Specifically, the methods and system extract telemetry data from native game data. By reducing game data to telemetry data only, the system may reduce the amount of data that must be stored and processed. The system reconstructs game data in order to generate an incident recommendation. This reconstructed data allows for manual user review and/or understanding the context of an incident.

An electronic device and a method for detecting abnormal device operation are provided. The method includes: obtaining multiple action events of a movable input device, and each action event including a relative coordinate and a time stamp of the movable input device; generating multiple absolute coordinates based on the relative coordinate of each action event; estimating multiple speed vectors based on the absolute coordinates and the time stamp of each action event; estimating multiple acceleration vectors based on the speed vectors and the time stamp of each action event; and estimating a probability of abnormal operation based on the speed vectors and the acceleration vectors.

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.

Disclosed is a non-transitory computer readable medium storing computer program, in which when the computer program is executed by one or more processors. When the computer program is executed by one or more processors of a computing device, the computer program performs an operation for user drawing-based security authentication, and the operations may include: determining to transmit a first control signal to cause a user terminal to display a first drawing input display, which includes one or more subjects, if a lock display release signal is received from the user terminal; receiving a first drawing input information inputted in accordance with the first drawing input display from the user terminal; and determining whether to release the lock display of the user terminal by inputting the received first drawing input information to a drawing evaluation model.

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.

A method for optimizing a computer-implemented game for a target metric is disclosed. Based on a detecting that an optimization point has been reached during a runtime of the computer game, user data, game state data, and personalized gaming experience (PGE) question data is communicated to a PGE server. The PGE question data is linked to the optimization point. An answer corresponding to the PGE question data is received from the PGE server. The answer is selected from a plurality of answers linked to the optimization point based on an application of a machine-learned model to the user data, game state data, and PGE question data. The received answer is implemented within the computer-implemented game.

This document relates to techniques for addressing disruptions that prevent applications from receiving user input, prevent users from providing input to an application, and/or prevents or impacts users from receiving application output. One example method involves detecting a disruption to an interactive application during interaction by a user with the interactive application, generating automated user inputs, and providing the automated user inputs to the interactive application during the disruption to the interactive application.