Application Engagement Composite Index for Multidimensional Telemetry
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Solution Overview
Problem
Modern computer applications face challenges in tracking feature engagement across multiple dimensions and applications, leading to resource wastage and biased metrics due to varying feature importance and multidimensional usage measurements.
Innovation Solution
A software tool generates an unbounded engagement index that weights preferred features more and unpreferred features less, incorporating multiple dimensions such as action, file, session, and user metrics, allowing for granular analysis and visualization of feature usage across applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If telemetry data is collected and analyzed for all features and users, then comprehensive engagement insights are obtained, but computing resources become unsustainable
Solution Approach 1:
The patent extracts only the most important features and dimensions for engagement measurement, rather than analyzing all telemetry data. This selective extraction reduces computing resources while maintaining measurement precision for key metrics.
Solution Approach 2:
The patent implements partial action by calculating engagement metrics for a subset of critical features and dimensions rather than all possible combinations. This approach provides sufficient engagement insights without the excessive computational cost of complete analysis.
2Loss of information
If all features are tracked equally, then complete feature usage data is collected, but resource wastage occurs due to varying feature importance
Solution Approach 1:
The patent applies local quality by assigning different levels of tracking intensity to different features based on their importance. Critical features receive detailed multi-dimensional tracking while less important features receive minimal or no tracking, eliminating resource wastage while preserving essential information.
Solution Approach 2:
The patent changes the parameter of tracking depth based on feature importance. By dynamically adjusting the level of detail collected for each feature, the system avoids resource wastage on unimportant features while maintaining complete data for critical ones.
3Measurement precision
If multiple dimensions of usage are measured, then granular engagement analysis is achieved, but complexity of the metric system increases
Solution Approach 1:
The patent segments the engagement metric system into hierarchical levels (overall engagement score, feature-level metrics, and dimensional breakdowns). This segmentation allows granular analysis where needed while simplifying the overall system through modular organization of complex measurements.
Data Source
AI summary
A method may include accessing, from a data store, telemetry data for an application, the telemetry data identifying actions executed by the application for a set of users of the application; retrieving an engagement index for the application using the telemetry data based on an engagement index profile, the engagement index profile identifying a selection of actions and a set of calculation dimensions; and presenting a user interface, the user interface including: a first portion identifying the selection of actions; a second portion identifying the set of calculation dimensions; a third portion identifying an original value of an action of the selection of actions with respect to a dimension in the set of calculation dimensions; and a fourth portion identifying a weighted composite value of the action with respect to set of calculation dimensions.