Deep Learning Pacing Model for Dynamic Content Deployment
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Solution Overview
Problem
Current content deployment systems lack the ability to respond to real-time or unexpected changes in deployment factors, such as limited resources, leading to inefficient content deployment without consideration for dynamic changes during the deployment period.
Innovation Solution
A system utilizing a trained pacing model with a k-nearest neighbor (KNN) and Neural Basis Expansion Analysis for Time Series (N-BEATS) portion to generate pacing parameters, which modifies a pacing pipeline to optimize content deployment parameters based on input parameters and feedback data, ensuring dynamic adjustment of deployment strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed deployment plan is generated without real-time adjustment capability, then the system complexity is reduced, but the adaptability to changing deployment factors deteriorates
Solution Approach 1:
The patent implements dynamic pacing parameters that adjust in real-time based on feedback from deployment performance and changing resource availability. The system transitions from static fixed deployment plans to dynamic adaptive pacing that responds to actual deployment conditions, resolving the contradiction between system complexity and adaptability.
Solution Approach 2:
The system incorporates feedback loops that monitor deployment performance, resource consumption, and pacing metric adherence in real-time. This feedback is used to dynamically adjust pacing parameters, enabling the system to adapt to changing deployment factors without requiring complete re-planning, thus balancing complexity and adaptability.
2Adaptability or versatility
If real-time feedback and dynamic adjustment are implemented, then the adaptability to changing conditions is improved, but the device complexity increases
Solution Approach 1:
The system pre-calculates multiple pacing scenarios and adjustment strategies in advance. When real-time feedback indicates a need for adjustment, the system selects from pre-prepared options rather than performing complex real-time optimization, reducing the computational complexity while maintaining adaptability.
Solution Approach 2:
The patent introduces intermediary pacing components that act as buffers between the deployment system and the control system. These intermediaries handle the complexity of real-time adjustments by managing pacing queues and buffer resources, isolating the complexity from the main deployment management system.
3Productivity
If deployment pacing is optimized for multiple objectives simultaneously, then the content deployment effectiveness is improved, but the difficulty of detecting and measuring performance deteriorates
Solution Approach 1:
The patent segments the multi-objective deployment optimization into separate measurable components, each tracked by dedicated pacing metrics. This segmentation allows the system to optimize for multiple objectives simultaneously while maintaining clear, separate measurement mechanisms for each objective, reducing the difficulty of detection and measurement.
Solution Approach 2:
The system dynamically adjusts pacing parameters based on the relative importance and performance of different deployment objectives. By changing parameters such as pacing rates, resource allocation weights, and priority levels, the system optimizes multi-objective effectiveness while maintaining measurable performance through standardized metric collections.
Data Source
AI summary
Systems and methods of deep learning-based multi-objective pacing content deployment are disclosed. A first set of input parameters is received and a first set of pacing parameters are generated by a trained pacing model that receives the first set of input parameters. The trained pacing model includes a k-nearest neighbor (KNN) portion and Neural Basis Expansion Analysis for Time Series (N-BEATS) portion. In response to generating the first set of pacing parameters, a pacing pipeline is modified to incorporate the set of pacing parameters. The pacing pipeline is configured to generate deployment parameters. Content is deployed to one or more content systems based on the deployment parameters and feedback data representative of the deployed content is received. A second set of pacing parameters is generated by the trained pacing model. The trained pacing model receives a second set of input parameters that are based at least in part on the feedback data.