Target-Pool Feedback for Precise Communication Transmission
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Solution Overview
Problem
The transmission of large volumes of communications, such as emails or text messages, places a significant burden on computing and network resources when excessive communications are sent to achieve a specific objective, leading to inefficient resource utilization.
Innovation Solution
A communication allocation system that uses iterative processing to dynamically adjust the number of communications based on updates to the target pool, performing multiple iterations to predict the optimal quantity of communications needed to achieve a specific objective, such as converting sales or driving website traffic, by utilizing databases to preserve the state of computations and efficiently utilize resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large volumes of communications are transmitted to achieve a specific objective, then the objective is more likely to be met, but computing and network resource utilization becomes inefficient
Solution Approach 1:
The system performs preliminary iterative processing to predict the optimal communication allocation before actual transmission occurs. By simulating different communication quantities and tracking target pool composition changes, the system determines the precise number of communications needed to achieve the objective, avoiding both under-transmission and excessive resource consumption.
Solution Approach 2:
The iterative processing incorporates feedback mechanisms where each iteration updates the target pool composition based on previous communication transmissions and their effects. This feedback loop allows the system to refine its predictions and adjust communication allocations dynamically, ensuring optimal resource utilization while meeting objectives.
2Loss of energy
If iterative processing is used to predict optimal communication quantities, then resource utilization is optimized, but computational complexity increases
Solution Approach 1:
The iterative processing is segmented into discrete, manageable iterations that process the target pool in portions. Each iteration handles a specific subset of the communication allocation problem, making the overall complex computation more manageable and efficient. The system divides the target pool into segments and processes them systematically through multiple iterations.
Solution Approach 2:
The system dynamically changes parameters such as communication quantity, target pool composition, and allocation ratios during iterative processing. By adjusting these parameters across iterations, the system converges on optimal values without requiring complex computational algorithms, simplifying the overall processing complexity while maintaining optimization benefits.
Data Source
AI summary
In some implementations, a device may identify a plurality of individuals that form a target pool for transacting with a plurality of entities. The device may determine responsiveness associations between the plurality of individuals and the plurality of entities. The device may perform, based on the responsiveness associations and a composition of the target pool, multiple iterations of computations of respective quantities of individuals predicted to transact with one or more of the plurality of entities and respective communication allocations predicted to realize transactions for the respective quantities of individuals. Each iteration, of the multiple iterations of computations, may be initiated by an update to the composition of the target pool in response to an entity accepting a communication allocation. The device may cause, based on the entity accepting the communication allocation, transmission of a plurality of communications in accordance with the communication allocation.