Amazon Technologies, Inc.

Jeremy Hardiman

Adam Lee Smith

Joseph Francis Stankowicz

Christian R Lange

Luis René Sempé Sosa

Christopher Scot Roby

Machine learning is used to generate and update predictive models configured to predict the positions, orientations, or actions of various gameplay elements capable of being controlled by players of a network-based multiplayer game. A gaming server can send a trained predictive model to each player device actively involved in a gaming session. Those devices can use the predictive model to predict the positions and orientations of various gameplay elements, where the actual data will not be received for a period of time due to latency of transmission over at least one network connection. When the actual data is received, the game state and gameplay action can be corrected or initiated as appropriate, enabling the overall gameplay experience to be smoother and more enjoyable. The gameplay and prediction data can be analyzed to further train or improve the models.

Predictive modeling for multiplayer gaming

Multi-dimensional models are supported for game creation via two-dimensional (2D) visual scripting systems. Visual scripting can utilize 2D nodes on a grid, where the nodes can interact through the use of input and output ports, and can be wired together to create gameplay behavior. A 2D rendering of a three-dimensional (3D) object (e.g., a mesh) can be added to the node grid for a gaming application. A scripting interface allows rules and ports to be created for specific aspects of the mesh, such as may relate to features, skeletons, or bounding boxes. Multiple scripting nodes can be associated with the same mesh such that the mesh is not duplicated in the visual scripting view port. The functionality for the 3D object can then be incorporated into the game based at least in part upon the associated nodes without manual coding or compilation.

Visual scripting for multi-dimensional elements

A reusable video game camera rig framework is described herein. The framework may include instruction sets for controlling a virtual camera rig, which is used to set a position and orientation of a virtual camera within a virtual area of a video game. The instruction sets may be reusable in a variety of different camera contexts, such as a first person camera, a third person camera, a top-down camera, and many others. The instruction sets may also be reusable in a variety of different video game contexts, such as combat games, sports games, fantasy games, racing games, and many others. The instruction sets may be both modifiable and extendible to different camera and game contexts, for example allowing developers to easily generate new camera behaviors for their games and also to modify existing available camera behaviors to better match their particular camera and game contexts.

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Kenmore, WA, United States of America

Jeremy Hardiman