Graphical Control Structure for Machine Learning Dialogue Systems
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Solution Overview
Problem
Modern virtual assistants implemented with a rules-based approach lack flexibility to handle queries or commands outside their predetermined scope, limiting their ability to provide meaningful responses to unrecognized user inputs, and inefficiencies persist in the control structures of machine learning-based conversational systems.
Innovation Solution
A graphical control structure for machine learning-based automated dialogue systems is configured with a root dialogue classification node and multiple distinct dialogue state classification nodes, connected via graphical edges, allowing for dynamic dialogue path traversal based on intent classification predictions, enabling the system to evolve and handle complex conversations without additional programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a rules-based approach is used to implement a virtual assistant, then the system can provide precise responses to predetermined queries, but the system lacks flexibility to handle queries outside the finite set of predefined rules
Solution Approach 1:
The patent transitions from static rules-based systems to dynamic machine learning models that can adapt their behavior based on input patterns. The control structure dynamically routes dialogue states to appropriate classification nodes, enabling the system to flexibly handle diverse queries while maintaining response accuracy through learned patterns rather than fixed rules.
Solution Approach 2:
The patent creates a universal control structure that can handle multiple types of dialogue intents through a single unified architecture. The root classification node and downstream state classification nodes work together to process various query types (informational, transactional, conversational) without requiring separate rule sets for each, thus achieving versatility while maintaining precision.
2Adaptability or versatility
If machine learning models are used to enhance conversational system capabilities, then the system can handle diverse user inputs, but inefficiencies persist in the control structures used for controlling the machine learning models
Solution Approach 1:
The patent segments the control structure into distinct functional components: a root classification node for initial intent recognition and multiple downstream state classification nodes for specific dialogue states. This segmentation allows each component to specialize in particular classification tasks, improving overall efficiency while maintaining high conversational capability through coordinated operation of the segmented structure.
Solution Approach 2:
The patent introduces a graphical control structure as an intermediary layer between user inputs and machine learning model processing. This intermediary efficiently routes dialogue states to appropriate classification nodes based on the current conversation context, reducing unnecessary model invocations and improving productivity while preserving the adaptability of the underlying machine learning models.
3Ease of manufacture
If a finite set of predetermined rules is used to govern virtual assistant responses, then the system implementation is simple and straightforward, but the system cannot address queries that exceed the scope of the finite rule set
Solution Approach 1:
The patent enables the system to serve itself by using machine learning models that automatically learn from dialogue data and improve their classification capabilities without requiring manual rule creation for each new query type. The graphical control structure automatically routes new dialogue states to appropriate classification nodes, allowing the system to expand its query scope organically while maintaining implementation simplicity through the unified graphical interface.
Data Source
AI summary
A system and method of configuring a graphical control structure for controlling a machine learning-based automated dialogue system includes configuring a root dialogue classification node that performs a dialogue intent classification task for utterance data input; configuring a plurality of distinct dialogue state classification nodes that are arranged downstream of the root dialogue classification node; configuring a graphical edge connection between the root dialogue classification node and the plurality of distinct state dialogue classification nodes that graphically connects each of the plurality of distinct state dialogue classification nodes to the root dialogue classification node, wherein (i) the root dialogue classification node, (ii) the plurality of distinct classification nodes, (iii) and the transition edge connections define a graphical dialogue system control structure that governs an active dialogue between a user and the machine learning-based automated dialogue system.