Remote API Performance Monitoring via Unique Namespace Metrics
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Solution Overview
Problem
Current systems fail to accurately monitor and report API performance and user behavior associated with user interfaces, leading to difficulties in identifying and prioritizing critical issues that impact user experience due to ambiguous performance metrics and inadequate measurement capabilities.
Innovation Solution
A system and method for remotely monitoring API performance and user behavior by registering metrics with unique namespace information values, synchronizing these values between client devices and servers, and generating reports based on numeric data received from client devices, which includes determining latency, session analysis, and navigation flow visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems are used to track API performance and user behavior, then basic data collection is possible, but the measurement precision and clarity of performance metrics are insufficient leading to ambiguous results
Solution Approach 1:
The patent segments performance monitoring into distinct components: UI event tracking, API call monitoring, and correlation logic. Each component captures specific metrics independently, then combines them to provide precise, unambiguous performance data. This segmentation allows for targeted measurement of specific performance aspects without the confusion of aggregated, indistinct metrics.
Solution Approach 2:
The system implements feedback loops where performance metrics are continuously measured, analyzed, and used to adjust monitoring parameters. The correlation between UI events and API performance is continuously refined based on observed patterns, improving measurement precision over time and eliminating ambiguous results through iterative optimization.
2Reliability
If comprehensive monitoring of all API calls and user interactions is implemented, then complete performance visibility is achieved, but the system complexity and difficulty of identifying critical issues increases
Solution Approach 1:
The patent extracts only the most critical performance indicators from the vast amount of available data. By identifying and isolating key metrics that directly impact user experience, the system achieves comprehensive monitoring reliability without being overwhelmed by unnecessary data complexity. Critical issues are highlighted through focused extraction of relevant performance signals.
Solution Approach 2:
Different monitoring strategies are applied to different parts of the system based on their importance. High-criticality API calls and user interactions receive detailed, granular monitoring, while less critical operations use simplified tracking. This local quality approach ensures reliable detection of critical issues without uniformly complex monitoring across all system components.
3Loss of information
If detailed metrics are collected for every UI interaction and API call, then complete user behavior analysis is possible, but the data processing overhead and reporting complexity increases
Solution Approach 1:
The system performs preliminary filtering and aggregation of user behavior data at the point of collection. Metrics are pre-processed, correlated, and validated before being stored or transmitted, reducing the burden on downstream processing systems. This preliminary action ensures complete user behavior insights are captured while minimizing subsequent data processing overhead.
4Reliability
If multiple performance metrics are tracked simultaneously, then comprehensive performance visibility is achieved, but the difficulty of understanding and prioritizing issues increases due to metric ambiguity
Solution Approach 1:
The patent merges multiple performance metrics into unified, correlated performance profiles. By combining UI event data, API response times, and user behavior patterns into integrated measurements, the system achieves comprehensive performance visibility while reducing issue identification complexity. Related metrics are combined into single actionable indicators that clearly indicate problem severity and type.
Data Source
AI summary
Various aspects of a system and a method to remotely monitor API performance and user behavior associated with a user interface (UI) are disclosed herein. In accordance with an embodiment, the system includes a server that includes registration of a metric associated with performance of an application program interface (API) and/or the UI associated with a client device. The metric may be registered as a unique namespace information value. A numeric value is assigned to the registered metric to associate the assigned numeric value with the registered metric. The assigned numeric value associated with the registered metric is synchronized at the client device and the server. The performance of the API and/or the UI associated with the client device is determined based on receipt of the assigned numeric value from the client device.