Balancing an exercise intensity level and a fun factor of a game is non-trivial and challenging. Disclosed herein are aspects and embodiments of an optimization-based approach to address this challenge. The approach can be applied to synthesize a game level with a desired exercise intensity level. By formulating the design problem as an optimization, a level designer can easily balance factors concerning the exercise intensity level of the game as well as other design factors and constraints.

A method for assisting game play. The method includes monitoring game play of the user playing a gaming application, wherein the user has a defined task to accomplish, wherein the task is associated with a task type. The method includes determining a task type proficiency rule for the task type based on results of a plurality of players taking on a plurality of tasks having the task type. The method includes determining a player proficiency score for accomplishing the task based on the task type proficiency rule. The method includes determining a user predictive rate of success in accomplishing the task based on the player proficiency score, the task type proficiency rule, and the task. The method includes determining a recommendation for the user based on the user predictive rate of success.

A system and method optimizes game quality by matching players for an online game to one of several virtual games. This matching process may involve filtering the players who wish to play according to various constraint minimizing criteria, packing the players into one or more virtual games to optimize game quality factors of the virtual games, and then instantiating the virtual games to actual online games played by the players. The game packing process may be iterative and may involve adding a new player into a virtual game. Game quality factor (GQF) values prior to and after the placement of the new player in the virtual game may be compared. The comparison of the GQF values may be used, at least in part to determine whether the new player is to remain in the virtual game. Various criteria may be considered in instantiating a virtual game.

A mobile and adaptable fitness system allows a fitness device to be used to control an application running on a host device. The system includes one or more controllers, a motion sensing device, and a host device. The hand controllers may be mounted on the handlebars of the fitness device or held in a user's hands and include buttons that the user can press. The motion sensing device may be mounted on the user or on a moving part of the fitness device and contains sensors that capture motion performed by the user. The host device receives input data from the hand controllers and the motion sensing device and generates an activity vector representing the intensity at which the user performs a fitness activity. Based on the activity vector, the host device controls an object in an application, such as a character, based on the input data.

A system that incorporates teachings of the present disclosure may include, for example, an accessory having a plurality of tactile-sensitive buttons, a plurality of light sources, wherein each light source emits a controllable spectrum of light through a corresponding one of the plurality of tactile-sensitive buttons, and a controller coupled to the plurality of tactile-sensitive buttons, and the plurality of light sources. The controller can be operable to detect tactile contact of each of the plurality of tactile-sensitive buttons, receive status information associated with a video game, and adjust the spectrum of light emitted by at least a portion of the plurality of light sources according to the status information to indicate one or more aspects of the video game. Additional embodiments are disclosed.

Systems and methods are disclosed for training a machine learning model to control an in-game character or other entity in a video game in a manner that aims to imitate how a particular player would control the character or entity. A generic behavior model that is trained without respect to the particular player may be obtained and then customized based on observed gameplay of the particular player. The customization training process may include freezing at least a subset of layers or levels in the generic model, then generating one or more additional layers or levels that are trained using gameplay data for the particular player.

Technology is described for surfacing in-game durational information to a player by way of a durational information platform. In a method embodiment, an operation processes a game data of a player for determining a game course the player is to take between a current state and a subsequent state within the game. The method also includes an operation for identifying a plurality of sequential segments within the game course for completion by the player and an operation for processing game telemetry of the player for determining effectiveness metrics of the player. The method further includes operations for calculating an estimated time for completion of the game course and for generating a recommendation for communication to a device of the player including the estimated time for completion.

In various embodiments of the present disclosure, playstyle patterns of players are learned and used to generate virtual representations (bots) of users. Systems and methods are disclosed that use game session data (e.g., metadata) from a plurality of game sessions of a game to learn playstyle patterns of users, based on user inputs of the user in view of variables presented within the game sessions. The game session data is applied to one or more machine learning models to learn playstyle patterns of the user for the game, and associated with a user profile of the user. Profile data representative of the user profile is then used to control or instantiate bots of the users, or of categories of users, according to the learned playstyle patterns.

The present disclosure relates to devices and methods for personalizing channel parameters for streaming content to a client device by dynamically adjusting channel parameters in response to learned user preferences. The devices and methods may receive context information from a client device and may send a rank and reward call to a reinforcement learning system for a recommendation for a value of the channel parameters. The rank and reward call may include the context information, a user vector, an item vector and a reward function error. The reinforcement learning system may use the information provided in the rank and reward call to the provide a recommendation for the value of the channel parameters. The devices and methods may use the recommendation to set the value of the channel parameters to stream the content to the client device.

An in-terminal display device causes at least one first icon to appear and displays the at least one first icon in at least one of a plurality of areas. An in-terminal input device receives an operation of switching areas and an operation of selecting a first icon. An in-terminal controller or an in-server controller controls a game corresponding to a selected first icon such that the game can be played when any one of first icons is selected in a switched area. The in-terminal controller of the in-server controller limits the number of first icons to be caused to appear to an upper limit value or less.