Temporal Cluster Targeting for Real-Time Content Release
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Solution Overview
Problem
Existing content targeting methods rely on static demographic and historical data, failing to optimize the timing and audience selection for digital media delivery, leading to suboptimal engagement and interaction.
Innovation Solution
A computer-implemented method using machine learning algorithms to analyze real-time user cluster activities, determine optimal content release times based on live user interactions, and transmit digital content when current usage metrics exceed predetermined thresholds, leveraging neural networks like CNNs and RNNs for enhanced targeting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static demographic and historical data is used for content targeting, then the system is simple to implement, but the timing and audience selection accuracy are suboptimal
Solution Approach 1:
The patent transitions from static targeting to dynamic real-time targeting by continuously monitoring live user interactions and adjusting content delivery decisions based on current user state. The system dynamically clusters users based on real-time behavior patterns and adjusts targeting thresholds according to live engagement metrics, enabling optimal content timing while managing complexity through automated adaptive algorithms.
2Productivity
If real-time user interaction monitoring is implemented, then content delivery timing is optimized, but data processing requirements and system complexity increase
Solution Approach 1:
The patent segments users into dynamic clusters based on real-time interaction patterns, allowing the system to process and analyze user data in manageable groups rather than handling all users individually. This segmentation enables targeted real-time monitoring of specific user segments while reducing overall data processing complexity through focused analysis of relevant user subsets.
Solution Approach 2:
The system implements feedback loops where live user interaction data continuously informs targeting decisions, and performance metrics feed back into threshold adjustments. This feedback mechanism enables automated optimization of content delivery timing based on actual engagement results, improving productivity while managing complexity through self-adjusting algorithms.
3Reliability
If dynamic real-time targeting is used, then content engagement is maximized, but the system requires sophisticated machine learning algorithms increasing complexity
Solution Approach 1:
The patent employs parameter changes by dynamically adjusting targeting thresholds and clustering parameters based on real-time engagement metrics. The system modifies operational parameters such as activation thresholds, cluster formation criteria, and content delivery timing based on live data, enabling reliable adaptive targeting while managing algorithm complexity through parameter-based control rather than overly complex models.
Data Source
AI summary
Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtains digital content and determines a target group (cluster) for the digital content. The processor(s) determine historical usage metrics on a social media platform for users in the cluster and utilize the historical usage metrics to determine a threshold value for identifying an optimized release time for the digital content to the cluster. The processors determine that current usage metrics exceed the threshold value, by monitoring, in real-time, one or more users in the cluster to obtain usage data, calculating, based on the usage data, the current usage metrics, comparing the current usage metrics to the threshold value, and based on determining that the current usage metrics exceed the threshold value at a given time, transmitting the digital content to the clutter via the social media platform.