Dialog Order Acquisition Using ML Concept and Relationship Extraction
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Solution Overview
Problem
Existing dialog-driven applications struggle with managing complex, informal customer interactions for order acquisition and fulfillment, particularly in scenarios requiring multiple modes of communication and back-and-forth conversations, leading to inefficiencies and resource consumption.
Innovation Solution
Implementing machine learning techniques for hierarchical concept recognition, co-reference analysis, and relationship extraction, combined with a distributed order processing system that includes both provider network and on-premise resources, to accurately interpret and fulfill customer orders in a conversational manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional dialog-driven applications are used to manage customer interactions, then the system can handle basic order acquisition, but the system consumes excessive computational resources and cannot efficiently manage complex informal interactions
Solution Approach 1:
The patent segments the dialog processing system into multiple specialized machine learning models, each handling specific aspects of order acquisition (e.g., intent recognition, entity extraction, slot filling). This segmentation allows parallel processing of different interaction aspects, improving efficiency while distributing computational load across multiple models rather than using a single resource-intensive system
Solution Approach 2:
The system performs preliminary classification of customer intents and pre-processing of dialog inputs using machine learning models before full order processing. This preliminary action filters and structures incoming interactions, reducing the computational complexity of subsequent processing steps and enabling more efficient resource utilization
2Adaptability or versatility
If the system allows customers to express themselves conversationally with multiple modes of communication, then customer satisfaction improves, but the complexity of managing diverse interactions increases
Solution Approach 1:
The patent implements a universal machine learning-based dialog management system that handles multiple communication modes (voice, text, gestures) and interaction types through a single integrated framework. The system uses multi-modal input processing and unified intent recognition models that can interpret various communication forms, providing versatility without requiring separate systems for each interaction type
Solution Approach 2:
The system introduces machine learning models as intermediaries between diverse customer inputs and the order processing system. These models act as mediators that translate various communication modes into standardized internal representations, simplifying the management of complex interactions by converting them into a uniform processing format
3Measurement precision
If back-and-forth interactions are used to determine customer requirements, then order accuracy improves, but the interaction time increases
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning system continuously analyzes dialog state and customer responses, adjusting its questioning strategy in real-time. The system provides feedback about what information has been gathered and what is still needed, enabling more efficient back-and-forth interactions that converge faster on the complete order requirements while maintaining high accuracy
Solution Approach 2:
The system performs preliminary intent recognition and entity extraction from customer inputs before engaging in extended back-and-forth interactions. This preliminary action pre-processes available information and identifies what additional clarification is needed, reducing the number of interaction turns required while maintaining accurate order interpretation
Data Source
AI summary
A plurality of concepts is identified from utterances of a user of a service using a set of machine learning models. The plurality of concepts includes a primary concept representing an offering included in a catalog of the service, and a non-primary concept which provides additional information about the offering. A logical relationship is inferred among a pair of concepts using the set of machine learning models. A service order for the service is populated based at least in part on the relationship.