Digital Experience Index Computation via Hierarchical Weighted Scoring
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Solution Overview
Problem
Current computer systems lack an effective method to comprehensively monitor and report the health and performance of digital experiences across large networks, making it difficult to identify areas for improvement and compare performance against industry benchmarks.
Innovation Solution
A digital experience index (DXI) is computed by organizing measurement data into a hierarchical tree structure, where scores are calculated based on weighted averages of child nodes, normalized by population size, and compared to industry benchmarks, with automated goal-setting and trend detection capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurement data is collected and organized into a hierarchical tree structure for comprehensive monitoring, then the completeness of digital experience monitoring is improved, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by organizing measurement data into a hierarchical tree structure with multiple levels (root nodes, intermediate nodes, leaf nodes). Each node represents a specific aspect of digital experience, allowing comprehensive monitoring to be broken down into manageable segments that can be independently measured and analyzed while maintaining overall completeness.
Solution Approach 2:
The patent introduces a hierarchical dimension to organize measurement data, transforming flat data collection into a multi-level tree structure. This dimensional organization allows the system to handle complexity by adding structure across multiple levels, where each level aggregates information from the level below while maintaining traceability to individual measurement points.
2Measurement precision
If scores are calculated based on weighted averages of child nodes with normalization, then the accuracy of performance assessment is improved, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and caching scores at each node in the hierarchical tree. When weights or measurement data change, only the affected portions of the tree need to be recalculated rather than computing all scores from scratch. This preliminary organization of data and pre-computation of intermediate results reduces the computational burden of complex weighted average calculations with normalization.
Solution Approach 2:
The patent implements dynamic score calculation where the hierarchical tree structure allows flexible updating of scores based on changing weights and measurement data. The system can dynamically adjust which nodes are recalculated based on what has changed, making the computational process adaptive rather than static, thereby reducing unnecessary computations while maintaining accuracy.
3Ease of operation
If automated goal-setting and benchmark comparison are implemented, then the actionability of performance insights is improved, but the data processing requirements increase
Solution Approach 1:
The patent applies self-service through automated goal-setting functionality that uses the collected measurement data and calculated scores to automatically establish performance targets and benchmarks. The system compares organizational performance against industry benchmarks and automatically generates insights about areas for improvement, reducing the need for manual analysis and making the performance management system self-sufficient in generating actionable recommendations.
Solution Approach 2:
The patent implements feedback mechanisms by continuously comparing current performance scores against automated goals and industry benchmarks, then using this feedback to identify improvement opportunities. The system provides closed-loop feedback where performance data flows back into the goal-setting and benchmarking processes, enabling continuous refinement of targets and actionable insights without requiring proportional increases in manual data processing.
Data Source
AI summary
Measurement data may be collected for a computing system, where the measurement data may correspond to leaf nodes in a tree that organizes the measurement data into a hierarchy of categories. A score associated with a first node in the tree may be calculated based on the measurement data and a set of weights, where each node in the tree may be associated with a weight in the set of weights, and where a score associated with a node in the tree may be based on scores associated with child nodes of the node. A first score corresponding to the computing system may be reported relative to other scores corresponding to other computing systems, where the other scores are computed based on other measurement data collected from other computing systems and the set of weights.