Configurable Response-Action Engine for Messaging Personalization
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Solution Overview
Problem
Current automated messaging systems lack the ability to tailor responses effectively, relying on minimal customization and requiring significant human intervention, which limits their efficiency and effectiveness in dynamic messaging campaigns.
Innovation Solution
A configurable response-action engine that uses natural language processing, industry-specific instructions, client data, and lead historical patterns to generate targeted responses, independent of the AI model, allowing for flexible and context-specific actions to transition leads to desired states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If automated messaging systems use minimal customization, then system complexity is reduced, but response effectiveness and personalization are worsened
Solution Approach 1:
The system segments the messaging response process into multiple independent components: template selection module, parameter extraction module, NLP processing module, and action generation module. Each component handles a specific aspect of response customization, allowing the system to achieve high personalization without overall complexity increase, as each segment can be developed and maintained independently
Solution Approach 2:
The patent introduces an intermediary NLP processing layer between the incoming message and the response generation system. This intermediary extracts key parameters, intent, and context from unstructured messages, transforming them into structured data that can be efficiently processed by the template system, thus bridging the gap between minimal system complexity and high response personalization
2Reliability
If automated messaging systems require significant human intervention, then response accuracy is improved, but productivity and efficiency are worsened
Solution Approach 1:
The system implements self-service through automated NLP processing that independently extracts parameters, determines intent, selects appropriate templates, and generates personalized responses without human intervention. The action model automatically determines the appropriate response action based on extracted insights, enabling the system to maintain high accuracy while operating autonomously at scale
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously learns from message exchanges and response outcomes. The action model is refined based on observed patterns in lead responses and campaign performance, allowing the system to improve accuracy over time while maintaining automated operation, thus increasing both reliability and productivity
3Adaptability or versatility
If configurable response-action engine uses multiple data sources including industry instructions and client data, then adaptability is improved, but device complexity is worsened
Solution Approach 1:
The patent creates a universal action model that handles multiple types of inputs (industry instructions, client data, lead history, message context) through a single integrated processing framework. This multi-functional model can adapt to different data sources and campaign types without requiring separate processing systems, achieving high adaptability while managing complexity through consolidation rather than proliferation of components
Data Source
AI summary
Systems and methods for a configurable response-action engine are provided. Actions are generated for a conversation when an insight is received from a natural language processing system. Industry, segment, client specific instructions, third party data, a state for the lead and lead historical patterns are also received. A decision making action model is tuned using this information. An objective for the conversation may be extracted from the state information for the lead. The tuned model is then applied to the insight and objective to output an action. A response message may be generated for the action. The action is directed to cause a state transition of the lead to a preferred state. In another embodiment, systems and methods are presented for feature extraction from one or more messages. In yet other embodiments, systems and methods for message cadence optimization are provided.