Dynamic Category Range Distribution for Search Results
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Solution Overview
Problem
Existing search result presentation methods often overwhelm users with too many results or too few, making navigation inefficient, especially when results are not evenly distributed across categories like price ranges.
Innovation Solution
A system dynamically sets category ranges to ensure approximately equal numbers of items within each range, allowing users to select ranges for finer navigation and automatically adjusts ranges as needed, using algorithms like Scott's choice and Freedman-Diaconis' choice for histogram bin width calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If search results are presented using fixed category ranges (e.g., standard price brackets), then the presentation structure is simple and easy to implement, but the distribution of results within categories becomes uneven, making navigation inefficient
Solution Approach 1:
The patent applies dynamics by transitioning from fixed, static category ranges to dynamic, automatically adjusted ranges. The system calculates optimal range boundaries based on the actual distribution of search results using statistical methods (such as quantiles or histogram binning), allowing the categories to adapt to the data itself rather than using predetermined brackets. This dynamic adjustment ensures more uniform distribution of results across categories, improving user navigation efficiency.
Solution Approach 2:
The patent changes the parameters of category ranges by using statistical calculations to determine optimal boundaries. Instead of using fixed parameters like "$0-9.99, $10-19.99," the system calculates parameters based on the actual data distribution, such as using quantile-based approaches or Freedman-Diaconis rule to determine range boundaries that equalize the number of results across categories. This parameter transformation resolves the contradiction by making the ranges data-driven rather than arbitrary.
2Measurement precision
If the number of category ranges is increased to provide finer granularity, then user navigation becomes more precise, but the system complexity and processing requirements increase
Solution Approach 1:
The patent applies self-service by enabling the system to automatically calculate optimal category ranges without requiring manual intervention or complex user configuration. The system uses built-in algorithms (such as quantile calculations or statistical binning rules) to autonomously determine the number and boundaries of categories based on the search results distribution. This self-service approach provides precise categorization while avoiding the complexity of manual range setting or overly complex processing requirements.
Solution Approach 2:
The patent applies partial action by implementing a balanced number of categories that provides sufficient precision without excessive complexity. Rather than creating an unlimited number of ultra-fine categories, the system uses a moderate number of categories determined by statistical principles to achieve uniform distribution. This partial approach (not going to maximum precision) resolves the contradiction by finding the optimal balance between measurement precision and system complexity.
3Device complexity
If search results are grouped into fewer categories, then the presentation is simpler and easier to process, but the granularity of results decreases, reducing navigation effectiveness
Solution Approach 1:
The patent changes the parameters of category boundaries using statistical methods to achieve uniform distribution across a manageable number of categories. By calculating optimal boundaries based on data distribution (such as using quantiles to divide results into equal-sized groups), the system maintains presentation simplicity while ensuring navigation effectiveness. The parameter transformation ensures that each category contains approximately equal numbers of results, resolving the contradiction between simplicity and effectiveness.
Solution Approach 2:
The patent applies equipotentiality by creating categories that are roughly equal in size, ensuring that no single category is significantly larger or smaller than others. This equalization of category potential (in terms of result distribution) allows users to navigate effectively across all categories without encountering overwhelming numbers in some categories while others are too sparse. The equipotential approach maintains presentation simplicity while achieving navigation effectiveness through balanced category construction.
Data Source
AI summary
A service provider presents results from a search query with dynamic category ranges, with each category range having approximately the same number of items within the category range.