Messaging-Platform AR Event Tracking for Feature-Specific Metrics
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Solution Overview
Problem
Current systems provide only general metrics for augmented reality (AR) experiences, failing to track interactions on a feature-by-feature basis, which hampers AR experience developers' ability to improve specific features and reduces user interest.
Innovation Solution
An AR event tracking system that allows developers to select and track specific AR events and interactions, generating feature-specific metrics by transmitting interaction data to a remote server for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If general metrics are provided for AR experiences, then system complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments AR event tracking into hierarchical levels: global event types (e.g., 'object_interaction'), feature-specific events (e.g., 'filter_applied'), and detailed interaction events (e.g., 'sticker_tapped'). This segmentation allows the system to collect comprehensive metrics without overwhelming complexity by organizing data collection at multiple granularities that can be selectively enabled.
Solution Approach 2:
The system dynamically adjusts the level of metric collection based on developer configuration and user behavior. Developers can selectively enable tracking for specific event types and features, and the system adapts the data collection intensity based on experience duration and engagement levels, optimizing between precision and complexity in real-time.
2Measurement precision
If feature-specific tracking is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The tracking system is segmented into modular event type categories (interaction events, completion events, error events) that can be independently configured and enabled. This modular structure allows developers to activate only the tracking components needed for their specific AR experience features, reducing overall system complexity while maintaining measurement precision for tracked features.
Solution Approach 2:
Different levels of tracking precision are applied to different features based on their importance and development needs. Critical features receive detailed feature-specific event tracking, while less important features use simplified event type tracking, optimizing the balance between measurement precision and system complexity across the entire AR experience.
3Loss of information
If comprehensive interaction data is collected, then information completeness is improved, but loss of time increases
Solution Approach 1:
The system extracts only the essential interaction data elements needed for meaningful analysis, filtering out redundant information. Event tracking focuses on capturing key interaction moments (event type, feature involved, user action) rather than recording every minor system state change, reducing data volume while maintaining information completeness for development insights.
Solution Approach 2:
The system implements partial data collection by enabling tracking for specific event types and features rather than comprehensively logging all possible interactions. This selective approach captures sufficient information for most development needs while significantly reducing the time required for data processing and analysis.
4Productivity
If detailed event tracking is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The event tracking system serves multiple functions through a unified framework: it collects data for analytics, provides feedback for development iteration, and enables feature performance comparison. This multi-functional design consolidates what could be separate complex systems into a single versatile tracking infrastructure, improving productivity without proportionally increasing complexity.
Solution Approach 2:
The tracking system is organized into segmented event type categories that map to different development needs (interaction analysis, completion tracking, error monitoring). This segmentation allows developers to quickly enable or disable specific tracking categories based on their current development priorities, improving productivity while keeping the overall system complexity manageable through modular configuration.
Data Source
AI summary
Methods and systems are disclosed for performing generating AR experiences on a messaging platform. The methods and systems receive, from a client device, a request to access an augmented reality (AR) experience and access a list of event types associated with the AR experience used to generate one or more metrics. The methods and systems determine that an interaction associated with the AR experience corresponds to a first event type of the list of event types and generates interaction data for the first event type representing the interaction. In response to receiving a request to terminate the AR experience, the systems and methods transmit the interaction data to a remote server.