Duplicate Station Detection via Hash-Based Segmentation
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Solution Overview
Problem
The presence of duplicate internet radio stations in aggregated networks degrades user experience due to the computational expense of comparing each station with every other station, leading to exponential time complexity in determining duplicates, especially in large networks like Aha Radio Network with over 400,000 stations.
Innovation Solution
Implementing a system and method for detecting duplicate radio stations using a hash-based approach that reduces the number of iterations from n to a constant, employing audio URL, call sign, and attribute maps to group stations as duplicates, and selectively displaying or suppressing them based on contractual agreements and quality rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each aggregated station is compared to every other station in the network to find duplicates, then duplicate detection accuracy is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent segments the duplicate detection process into two distinct phases: (1) generating candidate duplicates by comparing only essential attributes (station name, call sign, frequency, location) which runs in linear time O(n), and (2) verifying candidates by comparing full station details. This segmentation resolves the contradiction by eliminating the need for exhaustive O(n²) comparisons while maintaining high detection accuracy through the two-stage approach.
Solution Approach 2:
The patent performs preliminary filtering by comparing essential attributes first to generate a candidate set of potential duplicates before conducting full verification. This preliminary action reduces the problem from comparing all n stations against all other stations to only verifying a small subset of candidates, transforming the computational complexity from exponential to linear while preserving detection accuracy.
2Ease of operation
If all stations are processed to identify duplicates, then user experience is improved, but processing time increases significantly
Solution Approach 1:
The patent segments station processing into essential attribute comparison (for all stations) and detailed verification (only for candidates). This allows the system to process all stations efficiently by focusing computational resources only on potential duplicates, thereby improving user experience through comprehensive duplicate removal while minimizing processing time through selective detailed analysis.
Solution Approach 2:
The patent applies partial action by performing full station verification only on candidate duplicates identified through essential attribute matching, rather than verifying all stations. This partial verification approach is sufficient to achieve the goal of eliminating user-visible duplicates while dramatically reducing processing time compared to exhaustive verification of all stations.
3Ease of operation
If duplicate stations are removed from the network, then user experience is improved, but the number of stations decreases
Solution Approach 1:
The patent identifies and consolidates duplicate station entries by determining they represent the same underlying radio station. Rather than losing content, the system copies the station information to a single canonical entry, eliminating redundant listings while preserving the station's presence in the network. This resolves the contradiction by maintaining station quantity through information consolidation rather than deletion.
Solution Approach 2:
The patent discards duplicate station entries (redundant data) while recovering and preserving the unique station information. By identifying duplicates through attribute matching and retaining only one instance of each unique station, the system removes harmful redundancy while preserving the essential station content, thereby improving user experience without permanently losing station quantity.
Data Source
AI summary
Embodiments are disclosed for identifying duplicate stations in a radio station network. An example method includes identifying stations within a radio station network, determining which of the stations are duplicate stations, and selecting, via automated operated content management devices or user input directed to manually operated content management devices, which of the duplicate stations to show and which of the duplicate stations to suppress from being presented to a user. The example method further includes selectively displaying a selected duplicate station of a group of duplicate stations depending on a radio network's contractual agreements with various partners.