Software Usage Metrics Categorization and Drill-Down Visualization
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Solution Overview
Problem
The collection and analysis of software usage metrics across multiple client devices is unmanageable due to volume and diversity, making it difficult for software developers to identify bugs and areas for improvement without the aid of computer-generated visualizations.
Innovation Solution
A metrics collection system that standardizes and collects software usage metrics, assigns them to categories, calculates scores, and generates visualizations using a Uniform Metrics Identifier (UMI) to facilitate understanding and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If software usage metrics are collected from multiple client devices, then the volume and diversity of data increases, but the manageability and analysis efficiency deteriorates
Solution Approach 1:
The patent segments the large volume of software usage metrics data into distinct categories (e.g., engagement metrics, performance metrics, growth metrics). This categorization breaks down the overwhelming data set into manageable segments that can be analyzed separately, directly resolving the contradiction between data volume and manageability.
Solution Approach 2:
The patent introduces a metrics collection system as an intermediary between the client devices and the developers. This system automatically collects, standardizes, categorizes, and visualizes the metrics data, acting as a mediator that transforms raw data into actionable insights without requiring developers to manually manage the underlying data complexity.
2Reliability
If detailed software usage metrics are collected across diverse devices, then the completeness of bug identification improves, but the complexity of data collection and processing increases
Solution Approach 1:
The patent implements a universal metrics collection system that can gather diverse types of software usage metrics across multiple client devices through a standardized interface. The system performs multiple functions including collection, standardization, categorization, and visualization, reducing the need for separate specialized systems for each metric type or device.
Solution Approach 2:
The patent changes the parameters of data representation by transforming raw metrics data into standardized formats with consistent schemas, data types, and categorization structures. This parameter transformation maintains the completeness needed for reliable bug identification while simplifying the complexity of handling diverse device data through uniform parameter standards.
3Ease of operation
If software metrics are visualized with detailed categorization, then the ease of identifying issues improves, but the computational resources required increases
Solution Approach 1:
The patent segments metrics data into predefined categories (engagement, performance, growth) and visualizes them through dedicated interface elements. This segmentation allows developers to focus on specific aspects of software usage without being overwhelmed by all data simultaneously, improving ease of issue identification while managing computational resources through targeted visualization.
Solution Approach 2:
The patent implements partial visualization by providing overview metrics at the top level and allowing drill-down into detailed categorization only when needed. This approach balances computational resource usage by visualizing essential information immediately while offering detailed categorization on-demand, rather than rendering all possible visualizations simultaneously.
Data Source
AI summary
Example embodiments involve a metrics collection system for collecting software usage metrics from one or more client devices at deployments. A computer, such as a server configured to execute the metrics collection system, collects software usage metrics (e.g., as a metrics submission from a client device) of the software product at the deployment, identifies a metrics type of the software usage metrics collected, assigns the software usage metrics to a metrics category, and calculates and updates a metrics score of the metrics category, based on the software usage metrics collected.