Machine Learning Model for Content Distribution Plan Optimization
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Solution Overview
Problem
Existing methods for constructing electronic communications are inefficient, as they often result in communications being unintentionally left unopened, marked as spam, or discarded, due to the difficulty in tailoring content distribution plans effectively.
Innovation Solution
A machine learning model is trained to facilitate the selection of key-value pairs in a content distribution plan by leveraging vector representations of past communications, identifying similar plans, and predicting downstream events to optimize the completion of incomplete plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to construct electronic communications, then the process is simple and quick, but the communications are often ineffective (unopened, marked as spam, or discarded)
Solution Approach 1:
The system performs preliminary actions by analyzing downstream events from previously executed content distribution plans and training a machine learning model before constructing new communications. This advance preparation enables the system to predict effective content segments, transforming the approach from reactive to proactive and improving communication effectiveness without increasing apparent user complexity
Solution Approach 2:
A machine learning model serves as an intermediary between historical communication data and new content distribution plans. The model processes downstream event data and vector representations to identify effective segments, acting as a mediator that translates raw historical data into actionable insights for constructing effective communications without requiring users to manually analyze complex datasets
2Reliability
If content distribution plans are customized to improve effectiveness, then delivery and engagement rates increase, but the time and resources required to construct plans increase
Solution Approach 1:
The system enables self-service by automatically selecting content segments based on downstream event data from previously executed plans. The machine learning model autonomously identifies effective segments and generates recommendations without requiring manual analysis or iteration, allowing the system to serve itself by learning from its own historical performance data
Solution Approach 2:
The system implements feedback loops by continuously analyzing downstream events (openings, clicks, conversions) from executed communications and using this information to refine future segment selections. The machine learning model is retrained on accumulated data, creating a self-improving system that increases delivery and engagement rates over time while maintaining efficient construction processes
3Reliability
If manual selection of content segments is used, then the process is fast, but the selection quality is poor leading to spam marking or discarding
Solution Approach 1:
The patent replaces manual mechanical selection processes with an automated machine learning system. Instead of relying on human analysts to manually evaluate and select content segments, the system uses algorithms that process vector representations and downstream event data to automatically identify effective segments, substituting mechanical human analysis with computational automation
Solution Approach 2:
The system transforms unstructured content data into structured vector representations with specific parameters (dimensions, magnitudes, directions) that capture semantic meaning and performance characteristics. By changing the parameter space from raw text to mathematical vectors, the system enables automated comparison, similarity measurement, and effective segment identification that would be infeasible through manual analysis
Data Source
AI summary
The present disclosure generally relates to techniques for determining a segment of a content distribution plan. More specifically, the present disclosure discloses techniques for determining one or more key-value pairs of a content distribution plan by leveraging a trained machine learning model. A plurality of electronic communications may be generated based on completed key-value pairs with a content distribution plan. The plurality of electronic communications may then be distributed to a plurality of devices within a networked environment.