Context Broker for Race-Condition-Free Recommendation Generation
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Solution Overview
Problem
Conventional systems for generating in-application recommendations lack accuracy, efficiency, and flexibility due to race-conditions and computational inefficiencies, failing to consistently aggregate user context values and accurately orchestrate reasoning processes across multiple applications.
Innovation Solution
The implementation of a context management system that utilizes dynamically triggered sensor graphs with dependency and constraint architectures to build a unified context store, ensuring accurate and coherent user context snapshots across applications, and generating personalized recommendations by orchestrating sensor activations based on timing and update constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems generate recommendations based on detected user actions, then recommendations can be provided within applications, but accuracy and consistency of context aggregation deteriorate due to race-conditions and computational inefficiencies
Solution Approach 1:
The patent introduces a context broker as an intermediary component that mediates between context sources and recommendation engines. The context broker collects, normalizes, and manages context data from multiple sources, ensuring consistent aggregation without race conditions. This intermediary layer resolves the contradiction by providing a centralized coordination point that maintains both speed and accuracy in context handling.
Solution Approach 2:
The patent extracts the context aggregation logic from the recommendation generation process itself, separating context collection and management into a distinct, dedicated component (context broker). This extraction allows the recommendation engine to operate independently with pre-processed context data, eliminating race conditions while maintaining high-speed recommendation generation.
2Adaptability or versatility
If systems aggregate user context values across multiple applications, then personalized recommendations can be generated, but system complexity and computational overhead increase
Solution Approach 1:
The context broker is designed as a universal component that handles multiple context sources, normalization rules, and recommendation engines through a single standardized interface. This multi-functional design enables personalized recommendations across diverse applications without proportionally increasing system complexity, as the same core infrastructure serves multiple purposes.
Solution Approach 2:
The patent segments the complex system into distinct modular components: context sources, context broker, and recommendation engines. Each component has a specific responsibility and communicates through well-defined interfaces. This segmentation reduces overall system complexity by making each part manageable and independently developable, while still enabling sophisticated cross-application personalization.
3Speed
If systems continuously update context values across applications, then recommendations remain timely and relevant, but computational resources are consumed inefficiently
Solution Approach 1:
The context broker implements periodic updates rather than continuous real-time synchronization. Context values are updated at scheduled intervals or triggered by significant events, rather than constantly propagating across all applications. This periodic action maintains recommendation timeliness while dramatically reducing computational resource consumption compared to continuous updates.
Solution Approach 2:
The system performs partial updates by only propagating context changes that meet certain thresholds or criteria, rather than updating all context values universally. This selective approach ensures recommendations remain relevant and timely by updating only when necessary, avoiding wasteful computational expenditure on redundant updates.
Data Source
AI summary
The present disclosure describes systems, non-transitory computer-readable media, and methods that intelligently sense digital user context across client devices applications utilizing a dynamic sensor graph framework and then utilize a persistent context store to generate flexible digital recommendations across digital applications. In one or more embodiments, the disclosed systems utilize triggers to select and activate one or more sensor graphs. These sensor graphs can include software sensors arranged according to an architecture of dependencies and subject to various constraints. The underlying architecture of dependencies and constraints in each sensor graph allows the disclosed systems to avoid race-conditions in persisting actionable user-context based signals, verify the validity of sensor output through the sensor graph, generate user-context based recommendations across multiple related applications, and accommodate a specific latency/refresh rate of context values.