Dialog Manager Task Path Selection for Contact Centers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current dialog management systems for contact centers face challenges in combining conversational sophistication with business logic flexibility, as stochastic approaches struggle with incorporating business rules and deterministic approaches are limited in handling complex transactions due to scalability issues with finite state machines.
Innovation Solution
A dialog manager that selects the simplest distinct task path by filtering intent hypotheses based on confidence scores, using natural language understanding and historical conversation data to refine intent determination and manage confirmation of new intents and slot values, allowing for context-switching and disambiguation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a stochastic approach is used for dialog management, then conversational sophistication and context awareness are improved, but the ability to incorporate business logic and adapt to changing business rules deteriorates
Solution Approach 1:
The dialog management system is segmented into separate functional modules: a stochastic intent recognition module for natural language understanding and a deterministic task path selection module for business logic execution. This segmentation allows each module to specialize in its strength without compromising the other.
Solution Approach 2:
An intermediary layer is introduced between the stochastic intent recognition and deterministic task execution. This intermediary uses confidence scores and task path probabilities to bridge the gap between probabilistic intent detection and deterministic business rule application, enabling smooth transition between conversational flexibility and business logic enforcement.
2Ease of manufacture
If a deterministic approach with finite state machines is used, then business logic flexibility is improved, but the ability to handle complex transactions and scale deteriorates
Solution Approach 1:
The system transitions from traditional 2D finite state machine diagrams to a multi-dimensional task path hierarchy. Task paths are organized in a hierarchical structure with multiple levels of abstraction, allowing complex transactions to be decomposed into manageable sub-tasks while maintaining scalability through the additional dimensional organization.
Solution Approach 2:
The task path selection mechanism is designed to be universal, handling both simple transactions and complex multi-step processes through the same framework. The system can accommodate various business logic patterns (sequential, parallel, conditional, loop-based) within a single unified architecture, eliminating the need for separate FSM implementations for different transaction types.
3Measurement precision
If multiple task paths are presented to users for disambiguation, then intent accuracy is improved, but the conversation time and user friction increase
Solution Approach 1:
Instead of presenting all possible task paths to the user, the system presents only the top N most probable paths based on confidence scores. This partial action approach maintains high intent accuracy by focusing on the most likely interpretations while avoiding the time cost of presenting all possibilities. The system uses confidence thresholds to determine when user clarification is necessary versus when the most probable intent can be executed directly.
Data Source
AI summary
A method is presented for selecting, by a dialog manager, a current path location of an instance of a task from a list of a plurality of scored intent hypotheses with distinct intents and different task paths, comprising the steps of: performing a first filtering of the list of the plurality of scored intent hypotheses to comprise hypotheses meeting a pre-determined threshold; from the first filtered list, determining an intent and a task path for each of the hypotheses meeting the pre-determined threshold, wherein each of the hypotheses meeting the pre-determined threshold have the same intent; determining a simplest distinct task path to each hypothesis and presenting each of the simplest distinct paths in a second list; performing a second filtering of the second list by selecting a single distinct path; and confirming the intent of the hypothesis and configuring the selected single path to the current path location.