Self-Learning Policy Engine for Spoken Language Understanding
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Solution Overview
Problem
Current spoken language understanding systems face challenges in efficiently processing user inputs due to limitations in data sharing and action selection across components, leading to potential errors and friction in human-computer interactions.
Innovation Solution
Implementing a self-learning policy engine with state data trackers and action selection components that enable data sharing and self-learning across SLU processing stages, allowing for adaptive action selection and error mitigation through reward-based learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spoken language understanding systems process user inputs through separate components without data sharing, then system complexity is reduced, but processing accuracy and reliability deteriorate due to potential errors and friction in human-computer interactions
Solution Approach 1:
The patent merges multiple SLU processing components into a unified system with shared state data. The policy engine integrates intent classification, entity recognition, and dialogue state tracking into a single coordinated framework that shares intermediate representations and confidence scores across all components, eliminating errors caused by isolated processing while maintaining manageable system complexity through unified architecture.
Solution Approach 2:
The policy engine serves multiple functions simultaneously: it performs intent classification, entity recognition, dialogue state tracking, and action selection within a single component. This multi-functional approach improves reliability by ensuring consistent data interpretation across all SLU tasks while reducing the number of separate components needed, thereby balancing accuracy improvement with complexity control.
2Adaptability or versatility
If spoken language understanding systems implement comprehensive data sharing and self-learning mechanisms across all processing stages, then processing accuracy and adaptability improve, but device complexity and computational requirements worsen
Solution Approach 1:
The policy engine performs preliminary action by pre-processing and normalizing state data from multiple sources before it reaches downstream components. It prepares unified representations of user intent, entities, and dialogue context in advance, enabling adaptive action selection without requiring complex real-time processing at each subsequent stage, thus improving adaptability while controlling architectural complexity.
Solution Approach 2:
The policy engine acts as an intermediary between raw SLU processing outputs and final action selection. It mediates data flow by standardizing formats, resolving conflicts between different processing stages, and providing a unified interface for adaptive decision-making. This intermediary role enables versatility without directly exposing the full complexity of underlying processing mechanisms to higher-level components.
3Productivity
If spoken language understanding systems use traditional processing methods without self-learning, then device complexity is lower, but productivity and efficiency worsen due to potential errors requiring manual correction and reprocessing
Solution Approach 1:
The policy engine implements feedback mechanisms by continuously monitoring processing outcomes and using confidence scores to determine when human intervention is needed. It feeds back refined state representations to previous processing stages when errors are detected, enabling automatic correction without manual intervention in most cases, thereby improving productivity while keeping the feedback loop manageable through confidence-based triggering.
Solution Approach 2:
The system performs self-service through automated error detection and correction capabilities within the policy engine. It independently resolves ambiguities, corrects recognition errors, and validates processing outputs without requiring external manual correction, significantly improving processing efficiency. The self-service functionality is kept simple through rule-based validation and confidence threshold mechanisms rather than complex autonomous learning systems.
Data Source
AI summary
A system is provided for a self-learning policy engine that can be used by various spoken language understanding (SLU) processing components. The system also provides for sharing contextual information from processing performed by an upstream SLU component to a downstream SLU component to facilitate decision making by the downstream SLU component. The system also provides for a SLU component to select from a variety of actions to take. A SLU component may implement an instance of the self-learning policy that is specifically configured for the particular SLU component.