Dynamic Tip Jar Threshold Control for Live Streaming
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Solution Overview
Problem
The tip jar function in live content distribution systems faces challenges in managing a high volume of simultaneous contributions, leading to viewer dissatisfaction and increased processing burdens, as well as issues with setting optimal contribution thresholds that balance usage and effectiveness.
Innovation Solution
A contribution control server calculates an index based on predicted item contribution activity, dynamically determining consideration values to optimize the number of contributions by adjusting the value based on the index, ensuring higher values during active periods to prevent overwhelming content with icons and lower values during inactive periods to encourage contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the tip jar function allows unrestricted contributions, then viewer participation is encouraged, but the content becomes overwhelmed with icons and processing burden increases
Solution Approach 1:
The patent applies dynamics by making the contribution threshold variable rather than fixed. The server dynamically adjusts the minimum contribution amount based on real-time conditions including current contribution volume, viewer count, and content popularity. This allows the system to automatically reduce processing burden during high-activity periods by raising thresholds, while maintaining accessibility during lower-activity periods.
Solution Approach 2:
The patent changes the parameter of contribution threshold from a static value to a dynamic one that adapts to system conditions. By modifying the minimum contribution amount based on calculated indices reflecting current content activity levels, the system optimizes the balance between encouraging contributions and preventing overload without manual intervention.
2Device complexity
If a high minimum contribution amount is set, then processing burden is reduced, but viewer participation decreases
Solution Approach 1:
The system dynamically adjusts the minimum contribution threshold based on real-time indices that reflect content popularity and current contribution rates. When contribution activity is low, the threshold is lowered to encourage participation. When activity is high, the threshold rises to reduce processing burden, creating a self-regulating mechanism that balances both objectives.
Solution Approach 2:
The server continuously monitors contribution patterns, viewer counts, and content metrics to calculate an index that feeds back into threshold adjustment decisions. This feedback loop allows the system to respond to changing conditions in real-time, lowering thresholds when participation drops and raising them when the system becomes overwhelmed, thereby optimizing both engagement and processing efficiency.
3Productivity
If the contribution threshold is set low, then viewer participation increases, but concentrated use of the tip jar becomes problematic
Solution Approach 1:
The minimum contribution threshold is dynamically adjusted based on the calculated index that reflects current system load and contribution concentration. When too many contributions arrive in a short period, the threshold automatically increases to dampen the concentration effect, while still allowing genuine engagement to continue.
4Device complexity
If a fixed high contribution threshold is set, then processing burden is reduced, but viewer satisfaction and trust in the tip jar function decrease
Solution Approach 1:
Rather than using a fixed high threshold that permanently risks viewer trust, the system employs a dynamic threshold that adapts to current conditions. This allows the system to maintain lower, more viewer-friendly thresholds during normal operation to build trust, while automatically raising them during periods of excessive contribution volume to protect system performance.
Data Source
AI summary
In an aspect of the present invention, a server comprises a calculating unit, a determination unit, and a contribution implementing unit. The calculating unit calculates an index corresponding to the predicted value of an item contributed to the content. The determination unit determines the consideration provided to a viewer of the content based on the index in order to implement contribution of the item to the content. When an implementation request for the contributed item is received, the contribution implementing unit superimposes on the content an image of the item that is the subject of the implementation request in exchange for a consideration. The determination unit determines the consideration as a first value when the index is equivalent to a first predicted value and determines the consideration as a second value greater than the first value when the index is equivalent to a second predicted value, which corresponds to contribution of items that are more active in the content than those of the first predicted value.