Distributed Stochastic Learning Agent for Predictive Content Placement
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Solution Overview
Problem
Existing systems fail to perform real-time analysis of detailed user behavior patterns for an entire population, relying on subsets for approximation and requiring time for data collection, processing, and extrapolation, which limits their ability to capture all behavioral details and provide accurate predictions.
Innovation Solution
A distributed stochastic learning agent operates on embedded devices to process user data and send compact representations to a central server, enabling real-time predictive content placement by analyzing user activity patterns across all devices, reducing data transfer overhead and increasing scalability to large populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a survey of a subset of the population is used to approximate behavior patterns, then data collection time is reduced, but measurement precision deteriorates because it fails to capture all behavioral details of all users
Solution Approach 1:
The system segments the population into multiple subsets and simultaneously surveys multiple subsets in parallel. This allows the system to collect data from a larger effective population without increasing the time required for a single subset survey, thereby improving measurement precision while maintaining efficient data collection timing.
Solution Approach 2:
The system performs preliminary data collection from multiple subsets simultaneously before needing to make predictions. By having data from multiple subsets ready in advance, the system can quickly generate accurate approximations without requiring time-consuming sequential data collection when predictions are needed.
2Measurement precision
If raw data from the entire population is collected and processed in real-time, then measurement precision improves, but device complexity and processing requirements worsen
Solution Approach 1:
The processing system is segmented into multiple independent analysis modules, each handling data from a specific subset. This distribution of processing tasks reduces the complexity of any single processing unit while collectively achieving comprehensive population analysis with high measurement precision.
Solution Approach 2:
The system introduces intermediary processing layers that aggregate and summarize data from multiple subsets before final analysis. These intermediaries reduce the complexity of raw data processing by pre-processing and structuring information, making subsequent analysis more manageable while preserving measurement precision.
3Measurement precision
If data from the entire population is processed centrally, then measurement precision improves, but loss of time increases due to data collection and transfer requirements
Solution Approach 1:
The system segments data processing into distributed operations across multiple subsets that can be processed in parallel. This eliminates the sequential bottleneck of centralized data collection and processing, reducing latency while maintaining comprehensive population coverage for accurate predictions.
Solution Approach 2:
The system performs preliminary processing and analysis on data from multiple subsets in advance, before predictions are required. This preliminary action ensures that processed data is ready immediately when needed, reducing the time delay between data collection and actionable insights while maintaining high prediction accuracy.
Data Source
AI summary
A distributed stochastic learning agent analyzes viewing and/or interactive service behavior patterns of users of a managed services system. The agent may operate on embedded and/or distributed devices such as set-top boxes, portable video devices, and interactive consumer electronic devices. Content may be provided with services such as video and/or interactive applications at a future time with maximum likelihood that a subscriber will be viewing a video or utilizing an interactive service at that future time. For example, user impressions can be maximized for content such as advertisements, and content may be scheduled in real-time to maximize viewership from across all video and/or interactive services.