Foreground Background Request Differentiation for Accurate Usage Monitoring
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Solution Overview
Problem
Existing systems for monitoring Internet traffic on client devices, such as smartphones and laptops, struggle to accurately differentiate between foreground and background requests, leading to distorted monitoring results due to the mixing of these requests in time, which can result in false positives in crediting application usage.
Innovation Solution
A measurement system that includes a proxy server to monitor Internet traffic, using a request analyzer to apply rules based on timestamps and application information to distinguish between foreground and background requests, tagging foreground requests for crediting and discarding background requests to provide accurate application usage metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all requests are monitored and credited without differentiation, then monitoring coverage is maximized, but measurement precision deteriorates due to false positives from background requests
Solution Approach 1:
The patent segments requests into distinct categories (foreground and background) based on timing patterns and application state. By dividing the monolithic request stream into classified subsets, the system achieves precise crediting of only foreground requests while filtering out background noise, thereby resolving the contradiction between measurement precision and system complexity.
Solution Approach 2:
The system performs preliminary classification of requests before crediting occurs. By analyzing timestamps, application states, and request patterns in advance, the system pre-determines which requests qualify for crediting. This preliminary action prevents false positives from background requests while maintaining a relatively simple crediting mechanism.
2Measurement precision
If request filtering rules are simplified, then ease of operation improves, but measurement precision deteriorates due to inability to distinguish foreground from background requests
Solution Approach 1:
The patent implements dynamic filtering rules that adapt based on application state and request patterns. Rather than using static, overly complex rules, the system dynamically determines foreground/background status by comparing request timestamps against application lifecycle events. This dynamic approach achieves high precision while maintaining operational simplicity through intuitive timing-based logic.
Solution Approach 2:
The system uses self-evident timing relationships and application state information already available in the monitored environment to automatically distinguish foreground from background requests. By leveraging naturally occurring temporal patterns and existing application metadata, the system achieves precise identification without requiring complex external validation mechanisms.
3Reliability
If all requests are processed for crediting, then productivity of crediting system increases, but reliability deteriorates due to inclusion of background requests in usage metrics
Solution Approach 1:
The patent extracts and removes background requests from the crediting pipeline by identifying and filtering them based on timing and state analysis. This extraction of unwanted background traffic before crediting occurs ensures that only genuine foreground requests contribute to usage metrics, thereby maintaining high reliability without requiring complex post-processing validation that would reduce productivity.
Solution Approach 2:
The system introduces an intermediary classification layer between request reception and crediting. This intermediary analyzer examines timing patterns and application state to mediate which requests proceed to crediting. By placing this intelligent filter in the middle of the processing chain, the system maintains high throughput for legitimate requests while reliably blocking background requests, thus balancing productivity and reliability.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to monitor mobile Internet usage are disclosed. Example apparatus disclosed herein to monitor application usage associated with a device means for differentiating between foreground requests and background requests included in a burst of logged requests. Disclosed example apparatus further include means for calculating a presentation duration for a first application executed by the device based on at least a subset of the foreground requests identified in the burst of logged requests.