Adaptive Polling Timing for Fresh Information Delivery
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Solution Overview
Problem
Existing systems fail to adaptively adjust the frequency of polling for dynamically updated information, leading to inefficient use of computing resources and network bandwidth, and potential staleness or inaccuracies in information delivery.
Innovation Solution
Implement a machine learning model to predict optimal polling frequencies based on factors such as time remaining, number of bidders, user interest, and item characteristics, using supervised and unsupervised learning techniques to determine when to update information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a high polling rate is used to ensure information is not stale, then information freshness is improved, but computational and network resources are wasted
Solution Approach 1:
The patent implements dynamic polling rates that adapt to changing conditions. The system monitors information volatility, user activity levels, and time-to-deadline metrics to continuously adjust the polling frequency. When information changes frequently or deadlines are approaching, the polling rate increases automatically. When conditions are stable, the rate decreases, optimizing the balance between information freshness and resource consumption.
Solution Approach 2:
The system changes the polling rate parameter based on multiple dynamic factors including information volatility measurements, user engagement levels, and temporal proximity to deadlines. By making the polling rate a variable parameter rather than a fixed value, the system can optimize resource usage while maintaining information reliability across different operational contexts.
2Loss of energy
If a low polling rate is used to save resources, then computational and network resources are conserved, but information becomes stale
Solution Approach 1:
The system dynamically adjusts the polling rate parameter based on real-time conditions. When information volatility is high or deadlines are approaching, the polling rate increases to prevent staleness. When conditions are stable, the rate decreases to conserve resources, thus avoiding constant polling regardless of actual need.
Solution Approach 2:
The patent implements a dynamic polling mechanism that responds to changing system conditions. The polling rate is continuously adapted based on measured information volatility, user activity patterns, and temporal factors. This dynamic approach ensures information remains fresh when necessary while conserving resources during stable periods.
3Loss of information
If frequent updates are sent to all users, then information availability is improved, but network bandwidth is wasted
Solution Approach 1:
The patent implements differentiated update strategies tailored to individual users and specific information types. Rather than uniformly polling and pushing updates to all users, the system analyzes user-specific factors such as engagement history, device characteristics, and information priority levels. This allows the system to optimize update frequency and content for each user-context combination, delivering necessary information while minimizing unnecessary network traffic.
4Device complexity
If static update frequency is used, then system complexity is reduced, but adaptability to changing conditions is lost
Solution Approach 1:
The patent transitions from static to dynamic update schemes by implementing adaptive polling rates that respond to real-time conditions. The system monitors multiple metrics including information volatility, user activity, and temporal factors to automatically adjust update frequencies. This dynamic approach maintains adaptability to changing conditions while managing complexity through systematic adaptation rules and machine learning models.
Solution Approach 2:
The system implements self-adjusting update mechanisms that automatically adapt to changing conditions without requiring manual configuration. The polling and push mechanisms monitor system state and autonomously modify their behavior based on observed patterns and measured metrics, enabling the system to serve itself in optimizing update frequencies across varying operational contexts.
Data Source
AI summary
Technologies are disclosed herein for distributing information. The disclosed technologies determine an application element configured to receive information that is updated at a variable rate, the information pertaining to an object. Feature data is received that is associated with the object and data associated with use of the application element. The feature data includes a time horizon for the object and supplemental information associated with the object. Based on the feature data and the data associated with use of the application element, a first rate is predicted for sending the information about the object to the application element. The information is sent to the application element at the first rate.