Dynamic Budget Pacing Using Spending Feedback Loops
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Solution Overview
Problem
Existing systems lack the ability to dynamically adjust content distribution budgets in real-time based on spending feedback, leading to inefficiencies and inconsistencies in content delivery.
Innovation Solution
A dynamic budget control system that adjusts spending budgets using feedback loops and machine learning models to optimize content distribution across multiple client devices, allowing for real-time adjustments based on spending rates and traffic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static budget is used for content distribution, then budget management is simple, but spending efficiency and adaptability to traffic patterns deteriorate
Solution Approach 1:
The patent implements dynamic budget adjustment by continuously monitoring spending rates and traffic patterns, then automatically modifying budget allocations in real-time. The system transitions from a static budget to a dynamic one that adapts to changing conditions, resolving the contradiction between management simplicity and distribution efficiency.
Solution Approach 2:
The system establishes a feedback loop that collects data on actual spending rates and traffic patterns, processes this information through machine learning models, and uses the results to adjust budget allocations. This continuous feedback mechanism enables the system to maintain both manageable complexity and high efficiency by making data-driven budget decisions.
2Productivity
If real-time budget adjustments are implemented, then content distribution efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that automatically process spending data and traffic patterns to determine optimal budget allocations. These models act as mediators between raw data and budget decisions, reducing the need for complex manual intervention while maintaining high distribution efficiency.
Solution Approach 2:
The system implements self-service capabilities where the budget control mechanism automatically adjusts allocations based on real-time data without requiring external intervention. The machine learning models autonomously analyze patterns and make optimization decisions, reducing system complexity by eliminating the need for complex human-in-the-loop processes.
3Productivity
If dynamic budget control is used, then resource allocation improves, but measurement and detection difficulty increases
Solution Approach 1:
The patent replaces manual measurement and detection processes with automated machine learning models that continuously monitor and analyze spending rates and traffic patterns. This substitution of mechanical/manual processes with automated computational systems improves resource allocation while managing the complexity of measurement through standardized algorithms.
Data Source
AI summary
One or more computing devices, systems, and/or methods are provided. In an example, a first budget is determined for a first instance of a first time period. A pacing system, that is configured to pace spending over a time period based upon a budget, is controlled to spend on transmission of content to a first plurality of client devices using the first budget during the first instance of the first time period. Feedback indicating a rate of spending associated with the transmission of content to the first plurality of client devices is received. Using the feedback, the first budget is updated to determine a second budget for a second instance of the first time period. The pacing system is controlled to spend on transmission of content to a second plurality of client devices using the second budget during the second instance of the first time period.