Inference Engine Generates Structured Game Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Game developers are reluctant to provide structured context information to game console operating systems, and legacy games often lack the necessary infrastructure to do so, hindering the provision of enhanced game services that require structured data.
Innovation Solution
A multi-modal neural network system generates structured context information from unstructured game data, including audio, video, peripheral inputs, and user-generated content, using an inference engine that predicts and formats this data into a uniform data system (UDS) format, enabling additional functionalities without requiring extensive code modifications or patching of games.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If game developers provide structured context information to the game console operating system, then enhanced game services can be provided, but game developers are reluctant to expose information and only provide bare minimum information
Solution Approach 1:
The patent introduces an inference engine as an intermediary component that sits between the unstructured game data and the structured context information required by the operating system. This mediator automatically generates structured context information from unstructured game data without requiring developers to manually expose game information, thus resolving the contradiction between providing enhanced services and maintaining developer control over information exposure
Solution Approach 2:
The system enables self-service by allowing the operating system's inference engine to autonomously generate structured context information from available unstructured game data. The engine automatically performs analysis and generates context information without requiring developer intervention or additional code modifications, thus providing enhanced services while maintaining the status quo of minimal information exposure from developers
2Adaptability or versatility
If legacy games are required to provide structured context information, then enhanced game services can be provided, but legacy games were not required to provide structured information and lack necessary infrastructure
Solution Approach 1:
The inference engine serves as a mediator that bridges legacy games and the modern operating system's context information requirements. It automatically generates structured context information from the unstructured data that legacy games naturally produce, eliminating the need for legacy games to be modified or patched while still enabling enhanced services
Solution Approach 2:
The inference engine provides universal functionality by working with multiple types of games (both modern and legacy) and various unstructured data formats. It universally generates structured context information from diverse unstructured sources including image frame data, audio data, and peripheral inputs, making the system compatible with all games without requiring game-specific modifications
3Adaptability or versatility
If game engines are modified to provide structured information, then enhanced game services can be provided, but some game engines are not able to provide the structured information required
Solution Approach 1:
Instead of modifying game engines to produce structured information, the patent inverts the approach by having the inference engine consume unstructured information from the game engines and generate structured context information. This reversal eliminates the need for complex engine modifications while achieving the same goal of providing enhanced services
Solution Approach 2:
The inference engine acts as an intermediary layer between game engines and the operating system, translating unstructured game data into structured context information. This mediator handles the complexity of data transformation without requiring modifications to the game engines themselves, thus resolving the contradiction between service enhancement and engine complexity
4Measurement precision
If the inference engine processes all unstructured data continuously, then accurate structured context information can be generated, but processing time and computational resources increase
Solution Approach 1:
The system implements periodic action by updating the game state representation at specific intervals or trigger events rather than continuously processing all data. The inference engine generates structured context information at designated update points, balancing accuracy with processing efficiency by avoiding unnecessary continuous processing while maintaining timely context information
Solution Approach 2:
The inference engine applies partial action by selectively processing only the most relevant unstructured data types and features needed for accurate context generation. It focuses computational resources on key data sources that provide the most value for structured context information, rather than exhaustively processing all available unstructured data, thus reducing processing time while maintaining accuracy
Data Source
AI summary
Application context may be interpolated between application state updates from structured and unstructured application state data. Irrelevant unimodal modules may be deactivated based on the structured application state data while relevant unimodal modules remain active. Unimodal features are generated from the unstructured application using the relevant modules. A neural module selection network module may be trained with a machine learning algorithm. Each unimodal modules may generate unimodal feature vectors from unstructured application data. A context state update module may determine which unimodal modules are irrelevant from structured application state data and deactivate the irrelevant modules but not the relevant ones. A multimodal neural network may take the active unimodal feature vectors and predict structured context data and send it to a uniform data system.