Semi-Autonomous Advertising Systems Optimizing Parameters
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Solution Overview
Problem
Current online marketing and advertising processes are inefficient due to human error in setting advertisement parameters, leading to suboptimal ad performance and increased costs for businesses.
Innovation Solution
The use of machine learning and automation to optimize advertisement parameter settings by analyzing data from previous products, employing neural networks and gradient boosting regressors to predict optimal parameters for targeting and budgeting, and automatically adjusting settings based on performance monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If marketing professionals manually set advertisement parameters, then human judgment and experience can be applied, but human error results in suboptimal parameter settings and increased costs
Solution Approach 1:
The patent replaces the manual mechanical process of parameter setting by marketing professionals with an automated machine learning system. The system uses neural networks and gradient boosting regressors to automatically determine optimal advertisement parameters based on historical data, eliminating human error while managing complexity through algorithmic automation rather than manual processes
Solution Approach 2:
The system enables self-service by allowing the advertisement platform to automatically optimize its own parameter settings using machine learning algorithms. The system learns from historical advertisement performance data and autonomously determines optimal parameters without requiring external marketing professional intervention, thereby improving reliability while maintaining operational simplicity
2Productivity
If automated systems are used to set advertisement parameters, then human error is eliminated and optimization is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex parameter optimization task into distinct functional components: a neural network module for certain parameter predictions and a gradient boosting regressor module for other parameters. This segmentation allows each algorithm to specialize in specific aspects of parameter optimization, improving overall productivity while managing complexity through modular architecture that can be developed and maintained independently
Solution Approach 2:
The system implements a universal machine learning framework that handles multiple advertisement parameters through a single integrated platform. The neural network and gradient boosting regressor work together to optimize various parameters including budget allocation, target audience selection, and timing, providing multi-functional capability that improves productivity without requiring separate systems for each parameter type
3Measurement precision
If more data from previous products is analyzed, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing historical advertisement data in structured formats before actual prediction needs arise. The neural network and gradient boosting regressor are trained in advance on historical data from previous products, so when new advertisement parameters need to be optimized, the system can quickly retrieve pre-processed data and generate predictions without extensive real-time processing, thereby improving prediction accuracy while minimizing data processing time
Solution Approach 2:
The patent applies parameter changes by transforming historical data into optimized feature representations that enhance prediction accuracy. The machine learning algorithms automatically identify and prioritize the most relevant parameters from historical data, transforming raw data into meaningful features that improve measurement precision. This parameter transformation reduces the effective data processing burden by focusing computational resources on the most predictive features rather than processing all raw data equally
Data Source
AI summary
Embodiments of the present disclosure provide improved methods, systems, devices, media, techniques, and processes, often computer-based and/or processor-based, for advertising to consumers, such as consumers of music-based products. In a first part of the disclosed methods and systems, potential sales of a product can be predicted. In a second part of the disclosed methods and systems, one or more regression strategies can be used to analyze data from previous products in order to produce optimized parameter values. In a third part of the disclosed methods and systems, advertisement performance can be monitored and input parameters adjusted based on that performance.