Dimension Layers for Analysis Data System
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Solution Overview
Problem
The overwhelming amount of data required for comprehensive evaluations can lead to 'rating fatigue' among evaluators, as they are often asked to rate numerous individual attributes, making it difficult to gather thoughtful feedback.
Innovation Solution
A system that logically arranges attribute data in a geometric structure, allowing multiple dimension layers to be overlaid, which aggregates neighboring attributes into dimensions, reducing the number of attributes evaluators need to rate and facilitating more efficient analysis through reporting configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If evaluators rate numerous individual attributes for comprehensive evaluations, then the completeness of evaluation data is improved, but evaluator fatigue increases and feedback quality deteriorates
Solution Approach 1:
The patent combines multiple individual attributes into higher-level dimensions through dimension layers. Instead of evaluating each attribute separately, the system aggregates attributes into dimensions (e.g., combining cleanliness, comfort, and service attributes into an overall hotel quality dimension), reducing the number of evaluation tasks while preserving comprehensive assessment capability.
Solution Approach 2:
The patent introduces a dimensional structure overlaying the attribute space, transforming the evaluation from a flat list of attributes to a multi-dimensional framework. Evaluators assess subjects in dimensional space rather than attribute-by-attribute, adding a layer of abstraction that reduces cognitive load while maintaining evaluation depth.
2Ease of operation
If multiple dimension layers are overlaid to aggregate attributes into dimensions, then the number of attributes to rate is reduced, but the system complexity increases
Solution Approach 1:
The patent segments the evaluation system into distinct dimension layers, each handling specific attribute groups. This modular segmentation allows the complex aggregation logic to be divided into manageable layers, where each layer processes a subset of attributes independently, making the overall system more tractable despite its complexity.
Solution Approach 2:
The patent performs preliminary aggregation of attributes into dimensions before the actual evaluation process. By pre-defining dimension layers and their associated attribute groupings, the system prepares the evaluation framework in advance, reducing the computational and cognitive complexity during the evaluation execution phase.
3Productivity
If attributes are logically arranged in a geometric structure with neighboring attributes, then the aggregation analysis efficiency is improved, but the data structure complexity increases
Solution Approach 1:
The patent organizes attributes in a geometric structure (e.g., 2D grid or 3D space) where attributes with semantic relationships are positioned as neighbors. This spatial arrangement enables efficient aggregation by allowing the system to quickly identify and group neighboring attributes into dimensions based on their geometric proximity, improving analysis efficiency despite the increased data structure complexity.
Data Source
AI summary
A system and method are presented that analyze evaluation data concerning a subject using attributes that are logically arranged in a geometric structure such as a rectangular array. A plurality of dimension layers is laid on top of the logical arrangement of data. Each dimension layers assigns values to a plurality of dimensions based on the value of neighboring attribute groups. Each dimension layer can be associated with one or more reporting configurations that contain descriptors for the defined dimensions as well as formatting instructions for report-like output.