Speech Routing System Using ML Feedback
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Solution Overview
Problem
Existing speech processing systems face challenges in dynamically routing user requests to the most appropriate skills, especially for new skills that lack sufficient user feedback, leading to suboptimal user experiences and inefficient learning of routing patterns.
Innovation Solution
A speech processing routing system that employs machine learning models to dynamically determine the appropriate action and speech processing component by using user feedback, contextual data, and predictive signals, including an exploration policy to route a portion of requests to new skills and update ranking models based on predicted user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses traditional routing methods without machine learning, then the routing decisions are simple and fast, but the accuracy of routing speech processing requests to appropriate skills is low
Solution Approach 1:
The patent replaces traditional mechanical routing rules with machine learning models (neural networks) that automatically learn optimal routing decisions from data. The system uses feedback signals and contextual data to train models that predict the best skill routing, eliminating the need for complex hand-crafted routing logic while achieving high accuracy.
Solution Approach 2:
The system performs self-learning by automatically analyzing user feedback, skill performance data, and routing outcomes to improve its own routing decisions. The machine learning models continuously refine their parameters based on observed data, enabling the system to adapt to changing user needs and skill capabilities without external intervention.
2Adaptability or versatility
If the system routes all requests to existing skills only, then the routing process is fast, but new skills cannot be discovered or learned from user feedback
Solution Approach 1:
The system performs preliminary exploration by routing a portion of requests to newly deployed skills before they are fully optimized. This allows the system to collect initial feedback data and train models on new skill behavior patterns proactively, rather than waiting for organic adoption. The exploration policy actively seeks out new skills and gathers data to enable faster future routing decisions.
Solution Approach 2:
The system implements robust feedback mechanisms that capture user responses, skill performance metrics, and routing outcomes. This feedback is fed back into the machine learning models to continuously improve routing accuracy. The feedback loop enables the system to learn from both successful and failed routing decisions, accelerating the discovery and optimization of new skills.
3Loss of information
If the system requires explicit user feedback to learn routing patterns, then the learning process is accurate, but the system cannot learn from implicit user behaviors
Solution Approach 1:
The system automatically infers user preferences and skill performance from implicit behaviors such as request completion rates, user retention, and interaction patterns. Rather than requiring explicit feedback, the system self-learning algorithms analyze usage data to extract meaningful signals about user preferences, enabling continuous improvement without additional user input.
Solution Approach 2:
The system introduces intermediary analysis layers that translate implicit user behaviors into actionable learning signals. Machine learning models act as intermediaries between raw usage data and routing decisions, extracting meaningful patterns from complex behavioral data and converting them into improved routing strategies.
4Measurement precision
If the system uses complex machine learning models for routing, then the routing accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The patent segments the routing system into multiple components: a lightweight routing service that makes initial decisions based on simple rules, and separate machine learning models that handle complex routing scenarios. This segmentation allows the system to use computational resources efficiently by applying complex models only when necessary, rather than continuously.
Solution Approach 2:
The system applies machine learning models partially to specific routing decisions rather than universally to all requests. The exploration policy and feedback mechanisms ensure that the most impactful routing decisions receive the most computational attention, optimizing the balance between accuracy and resource consumption.
Data Source
AI summary
Devices and techniques are generally described for using user feedback to determine routing decisions in a speech processing system. In various examples, first data representing a first utterance may be received. Second data representing a first semantic interpretation of the first utterance may be determined. A first intent data processing application may be selected for processing the second data. Feedback data may be determined related to the first intent data processing application processing the second data. Third data representing a semantic interpretation of a second utterance may be received, wherein the first semantic interpretation is the same as the second semantic interpretation. A second intent data processing application may be determined for processing the third data based at least in part on the feedback data.