Assessment Results Viewer for Computing Device Performance Analysis
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Solution Overview
Problem
Users, manufacturers, and resellers of computing devices face challenges in analyzing performance effectively, as traditional methods provide standardized numerical data without context, failing to detect software anomalies and allowing for meaningful comparisons between devices.
Innovation Solution
An assessment results viewer that analyzes raw data, provides actionable insights, and enables users to compare performance across multiple devices, both numerically and graphically, while integrating with existing tools for deeper diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized performance measurement applications are used, then performance data can be collected consistently, but the results become meaningless to users who lack experience with the standardized components
Solution Approach 1:
The patent introduces an intermediary layer between the standardized measurement application and the user. This intermediary translates the raw standardized performance data into contextualized, human-readable insights by comparing results against real-world usage scenarios and providing actionable recommendations, thereby bridging the gap between technical measurement precision and user comprehension
2Reliability
If hardware-focused performance suites are used, then hardware performance can be measured, but software anomalies such as corrupted data files remain undetected
Solution Approach 1:
The patent extends the performance measurement system to be universal rather than hardware-specific. By incorporating software health assessments that evaluate system components, data integrity, and operational behavior alongside traditional hardware benchmarks, the system can detect both hardware performance characteristics and software anomalies within a single unified framework
3Ease of operation
If raw numerical performance data is provided, then data can be compared at a high level, but detailed raw data for in-depth analysis is not accessible
Solution Approach 1:
The patent segments the performance data presentation into multiple levels of detail. The user interface displays summarized performance metrics at a high level for quick comparison, while maintaining access to granular raw data through drill-down functionality. This segmentation allows users to navigate between overview and detail without cluttering the initial view, resolving the contradiction between ease of high-level comparison and access to detailed information
4Adaptability or versatility
If multiple computing devices are compared, then performance differentiation is possible, but automated alignment and aggregation of assessments across devices is complex
Solution Approach 1:
The patent implements self-service through automated data alignment and aggregation processes. The system automatically normalizes assessment data from multiple devices by identifying common metrics and standards, aligning measurements across different hardware configurations, and aggregating results without requiring manual intervention. This automation handles the complexity of multi-device comparison while presenting simplified results to users
Data Source
AI summary
An assessment results viewer displays the results of assessments that quantify the performance of an aspect of a computing device. The viewer presents both an overview of the collected data and mechanisms for displaying ever-increasing details, including raw data itself. The viewer further provides actionable information to the user that can offer the user guidance, or otherwise suggest potential courses of action. The viewer automatically aggregates multiple iterations of the same assessment to generate derivative overview data, and automatically aligns data collected by the same assessment across multiple job files. Such aggregation and alignment is performed by reference to metadata, including identifying information. Data can be presented in tabular form, and users can pivot along different axis to focus on groupings of data.