Exercise Leaderboard Data Compression for Scalable On-Demand Classes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing exercise systems lack scalable and efficient methods for delivering large volumes of on-demand leaderboard data while preserving user experience, particularly in systems with growing user bases, leading to increased storage and network overhead.
Innovation Solution
A method and system that compresses workout data for live and archived classes, reconstructs global leaderboards, and uses lossy compression algorithms like the Ramer-Douglas-Peucker algorithm to reduce data volume, enabling efficient storage and retrieval of leaderboard information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system stores complete workout data for all users to maintain accurate leaderboard information, then the leaderboard accuracy and user experience are improved, but the storage space and network overhead increase significantly
Solution Approach 1:
The patent extracts and stores only the essential leaderboard-relevant data points (performance metrics, rankings, timestamps) while discarding redundant workout details. This selective extraction maintains leaderboard accuracy by preserving key competitive information while dramatically reducing storage requirements compared to storing complete workout datasets for all users.
Solution Approach 2:
Instead of storing complete data and then filtering for leaderboard information, the system inverts the approach by directly capturing and storing only the leaderboard-essential metrics from the outset. This inversion eliminates unnecessary data storage while ensuring leaderboard accuracy is maintained through dedicated metric capture.
2Productivity
If the system processes and delivers leaderboard data for thousands of concurrent users in real-time, then the user experience and system scalability are improved, but the network overhead and processing complexity increase
Solution Approach 1:
The patent segments leaderboard data into discrete, independently processable units (individual user performance metrics, ranking positions, time-stamped events). This segmentation enables parallel processing of multiple user data streams simultaneously, allowing the system to handle thousands of concurrent users efficiently while reducing processing complexity through modular data handling.
Solution Approach 2:
The system transforms raw workout data into standardized leaderboard parameters (rankings, performance scores, relative positions) that are optimized for efficient storage, transmission, and comparison. This parameter transformation reduces data complexity and enables scalable processing across large user bases by converting heterogeneous workout data into uniform leaderboard metrics.
3Ease of operation
If the system provides both on-demand and real-time leaderboard access to preserve live exercise experience, then the user experience is improved, but the network overhead and data delivery complexity increase
Solution Approach 1:
The patent pre-processes and pre-formats leaderboard data during the exercise session itself, organizing performance metrics and rankings in advance. This preliminary data preparation enables both real-time leaderboard updates and on-demand historical access without requiring intensive processing at retrieval time, thereby reducing network overhead while maintaining seamless user experience across different access modes.
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
On-demand data is provided for real-time exercise experience including identifying available classes, receiving exercise class selection, retrieving associated leaderboard data, decompressing leaderboard data, and delivering exercise class content including leaderboard data to an exercise apparatus. Systems and methods further include receiving a class end condition, gathering data from the selected exercise class, compressing the gathered data, and appending the compressed data to stored leaderboard data. Compressing includes sampling a points from each user's workout through a recursive process including identifying a first point, last point and at least one mid-point of the workout, and for each successive pair of sampled points, identifying a mid-point that is furthest away from a line segment between the pair of sampled points, and adding the mid-point to the plurality of points if a distance between the mid-point and the line segment is greater than a predetermined threshold.