AI Dialog Policy Learning with Feedback for Data-Efficient Optimization
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Solution Overview
Problem
Existing AI systems lack a comprehensive framework for optimizing customer communication, particularly in data efficiency, multi-turn dynamics for dialog policy learning, and integrating domain ontology into dialog systems, limiting effective messaging, product recommendations, and customer targeting for sales and marketing.
Innovation Solution
A method and system using AI models that iteratively refine processes through predefined criteria, integrating large language models (LLMs) for data normalization and embedding, reinforcement learning for dynamic optimization, and machine learning models to calculate performance metrics, ensuring alignment with business goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised learning methods are used for dialog policy learning, then training data requirements are high, but data efficiency is poor and training complexity increases
Solution Approach 1:
The patent implements a reinforcement learning framework where the dialog policy model receives feedback through reward signals based on customer interaction outcomes. The model continuously updates its policy based on this feedback, enabling efficient learning from limited data while improving accuracy over time through iterative optimization rather than requiring large static training datasets.
Solution Approach 2:
The system pre-defines a state space, action space, and reward function structure before training begins. This preliminary setup includes defining dialog states, possible responses, and reward criteria based on business goals, allowing the model to learn efficiently within a structured framework rather than requiring extensive unlabeled data exploration.
2Productivity
If comprehensive customer communication optimization is implemented, then business outcomes improve, but system complexity increases
Solution Approach 1:
The patent divides the complex optimization problem into distinct components: state representation, action selection, reward calculation, and policy optimization. Each component is handled by specialized modules within the reinforcement learning framework, allowing the system to manage complexity through functional segmentation while achieving comprehensive communication optimization.
Solution Approach 2:
The dialog policy model serves multiple functions simultaneously: it generates customer responses, optimizes engagement strategies, maximizes business outcomes, and adapts to different interaction scenarios. This multi-functionality is achieved through a unified reinforcement learning framework that handles diverse optimization goals through a single policy learning process.
3Adaptability or versatility
If real-time adaptation to customer interactions is implemented, then customer lifetime value increases, but computational requirements and processing time increase
Solution Approach 1:
The system pre-defines the state space, action space, and reward function structure before training begins. This preliminary setup includes defining dialog states, possible responses, and reward criteria based on business goals, allowing the model to learn efficiently within a structured framework rather than requiring extensive unlabeled data exploration.
Solution Approach 2:
The reinforcement learning implementation enables continuous learning and adaptation through online training mechanisms. The model continuously updates its policy based on incoming customer interactions while maintaining operational readiness, ensuring uninterrupted optimization without requiring batch processing or system downtime for retraining.
Data Source
AI summary
A method and system for optimizing a goal using artificial intelligence (AI) models. The system employs large language models (LLMs) for data normalization and message generation, alongside machine learning (ML) models for continuous training and optimization. It integrates structured and unstructured data into embeddings to evaluate performance metrics and iteratively refine outputs aligned with predefined criteria. The approach dynamically adapts to changes, enabling real-time decision-making and improved efficiency in applications such as customer engagement, sales, and marketing.