Resource Allocation System Using Quality-Based Adjustment Factors
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Solution Overview
Problem
Content providers lack control over resource utilization in content distribution platforms, as these platforms do not consider the providers' intentions for maximizing audience coverage or quality of applicants, leading to inefficient resource management.
Innovation Solution
Implementing a system that allows content providers to specify resource splits for conservative or aggressive utilization strategies, using machine-learned models to score target entities and assign them to resource portions, with adjustment factors determining the final score for content delivery campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content distribution platforms utilize resources without considering content providers' intentions, then resource distribution is simplified and faster, but content providers cannot control resource allocation for their specific goals (maximizing audience coverage vs. maximizing quality)
Solution Approach 1:
The patent segments resource allocation into multiple resource pools, each associated with different utilization strategies (conservative, aggressive, etc.). Content providers can specify how their resources are divided among these pools, allowing differentiated control over resource utilization while maintaining system manageability through structured segmentation.
Solution Approach 2:
The system dynamically assigns content requests to resource pools based on the content provider's specified splits and the current state of resources. The resource utilization strategy is not fixed but adapts in real-time based on provider preferences and system conditions, enabling flexible control without rigid complexity.
2Measurement precision
If content providers specify detailed resource splits for different utilization strategies, then resource allocation precision is improved, but system complexity and configuration difficulty increase
Solution Approach 1:
The patent introduces parameter-based control where content providers specify resource allocation through simple percentage splits (e.g., 70/30) and select from predefined utilization strategy parameters (conservative, aggressive, etc.). This parameterized approach enables precise resource allocation control while maintaining ease of configuration through standardized options rather than complex detailed settings.
3Productivity
If content requests are processed using multiple resource splits with different utilization strategies, then campaign performance optimization is improved, but processing time and computational overhead increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring resource pools with different utilization strategies before content requests arrive. Content providers specify their resource splits in advance, and the system prepares the allocation framework beforehand. When requests come in, the system quickly assigns them to appropriate pools based on pre-established rules, reducing real-time processing overhead while maintaining performance optimization capabilities.
Data Source
AI summary
Techniques for user-defined quality control of resource usage are provided. In one technique, resource split data is received that indicates a split of a total resource amount that is associated with a content delivery campaign. In response, based on the split, a first resource amount and a second resource amount is determined, each a subset of the total resource amount. The first resource amount is associated with a first utilization strategy and a first mapping function and the second resource amount is associated with a second utilization strategy and a second mapping function. In response to receiving a request from a client device, an entity of the client device is determined and associated with the first mapping function. A quality score of the entity is determined and, based on the first mapping function and the quality score, an adjustment factor is determined. A content item selection event is conducted based on the adjustment factor.