Predictive Software Agent for Cross-Channel Communication Optimization
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Solution Overview
Problem
Existing predictive data analysis solutions face inefficiencies and reliability issues in generating effective electronic communications, leading to low-value and unwanted agent actions that result in communication fatigue and reduced accuracy.
Innovation Solution
A computer-implemented method using a predictive software agent machine learning model that transforms historical event sequence data into sequence embeddings and generates optimal agent actions based on action reward values, trained over intervals with retrieved interaction data and stored in historical episodes, to reduce ineffective actions and improve communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive data analysis solutions are used to generate electronic communications, then communication coverage is maintained, but predictive accuracy deteriorates and communication fatigue increases due to low-value and unwanted agent actions
Solution Approach 1:
The system implements feedback mechanisms where the reinforcement learning model continuously learns from the outcomes of agent actions. The model receives rewards or penalties based on whether communications are valuable or unwanted, adjusting its predictions to improve accuracy over time and reduce communication fatigue by learning from past performance data.
Solution Approach 2:
The system changes the parameters of the predictive model by using reinforcement learning to dynamically adjust prediction strategies. Instead of static rules, the model adapts its parameters based on learned patterns from historical data and real-time feedback, transforming the communication generation process from rule-based to learning-based optimization.
2Measurement precision
If comprehensive historical event sequence data is processed to improve prediction quality, then predictive accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system segments the historical event sequence data into manageable state representations and transitions. By breaking down complex sequences into discrete states and transitions, the reinforcement learning model can process information more efficiently while maintaining prediction quality, avoiding the need to analyze entire historical datasets simultaneously.
Solution Approach 2:
The system uses copying by creating simplified representations (state embeddings) of complex historical data. Instead of processing raw historical event sequences directly, the model creates compressed state representations that capture essential patterns, reducing computational requirements while preserving predictive accuracy.
3Productivity
If multiple agent actions are generated and executed across different channels, then communication effectiveness may improve, but the quantity of unwanted actions increases leading to reduced reliability
Solution Approach 1:
The system applies partial action by selectively executing only the most valuable agent actions rather than generating all possible communications. The reinforcement learning model ranks and selects a subset of high-value actions to execute, avoiding the harmful effect of overwhelming clients with excessive communications while maintaining productivity through targeted interventions.
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing agent actions using a predictive software agent framework configured to retrieve historical event sequence data, transform, using a state encoder machine learning model, the historical event sequence data into one or more sequence embeddings comprising fixed-length vectors, generate, using a predictive software agent machine learning model, a prediction output comprising one or more optimal agent actions comprising at least a best agent action based on the one or more sequence embeddings.