Hybrid AI-HI Proxy for Natural Language Routing
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Solution Overview
Problem
Conversational response systems, such as IVR and chat systems, often provide a less than satisfactory user experience due to limitations in automated speech recognition and natural language processing, leading to inconsistencies and frustration, especially when handling complex user interactions, and human agents face challenges with latency and scalability.
Innovation Solution
An interactive response system that combines human intelligence (HI) and artificial intelligence (AI) subsystems to enhance natural language understanding, allowing the system to route user inputs to either AI, HI, or both, and uses a proxy to decide on resource allocation based on algorithms, confidence scores, and application criteria, enabling improved accuracy and reliability through hybrid processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated speech recognition and natural language processing are used, then cost is reduced and scalability is improved, but user experience quality and recognition accuracy deteriorate
Solution Approach 1:
The patent combines automated AI subsystems with human intelligence (HI) subsystems into a hybrid architecture. The proxy routes utterances to either AI, HI, or both based on confidence scores and algorithms, merging the scalability of automation with the reliability of human recognition to resolve the contradiction between productivity and reliability.
Solution Approach 2:
The proxy acts as an intermediary component that sits between the user input and the recognition subsystems. It evaluates confidence scores, applies routing algorithms, and decides whether to use AI, HI, or both, thereby mediating between the need for automation and the need for accurate recognition.
2Reliability
If human agents are employed to recognize utterances, then recognition accuracy is improved, but latency increases and scalability is reduced
Solution Approach 1:
The system applies human intelligence selectively rather than universally. The proxy uses confidence scores to determine when HI is necessary, routing only uncertain or complex utterances to human agents while handling confident, routine utterances with AI alone, thus reducing overall latency while maintaining accuracy where needed.
Solution Approach 2:
The hybrid architecture merges fast AI processing with accurate human recognition. By combining both subsystems and routing strategically, the system achieves both low latency (through AI handling of routine cases) and high accuracy (through HI handling of uncertain cases).
3Reliability
If more human intelligent resources are allocated, then recognition accuracy is improved, but system cost and operational complexity increase
Solution Approach 1:
Human intelligent resources are allocated partially rather than fully. The proxy dynamically determines the appropriate mix of AI and HI based on utterance characteristics and confidence scores, using human resources only when necessary to improve accuracy, thereby reducing overall system complexity and cost.
Solution Approach 2:
The system dynamically adjusts the allocation of human versus AI resources based on real-time conditions. The proxy evaluates each utterance individually and routes it appropriately, creating a dynamic resource allocation strategy that optimizes accuracy while minimizing complexity and cost.
4Productivity
If automated systems handle all utterances, then scalability is maximized, but recognition accuracy and user satisfaction deteriorate under complex interactions
Solution Approach 1:
The system segments utterances into different categories based on their characteristics and confidence scores. The proxy divides the workload between AI and HI subsystems, routing simple, high-confidence utterances to AI for scalability and complex, low-confidence utterances to HI for accuracy, thus resolving the contradiction between scalability and precision.
Solution Approach 2:
The hybrid system merges the scalability advantages of full automation with the accuracy advantages of human recognition. By combining both approaches and routing strategically based on utterance complexity and confidence levels, the system achieves both high scalability and high recognition accuracy.
Data Source
AI summary
An interactive response system combines human intelligence (HI) subsystems with artificial intelligence (AI) subsystems to facilitate overall capability of multi-channel user interfaces. The system permits imperfect AI subsystems to nonetheless lessen the burden on HI subsystems. A combined AI and HI proxy is used to implement an interactive omnichannel system, and the proxy dynamically determines how many AI and HI subsystems are to perform recognition for any particular utterance, based on factors such as confidence thresholds of the AI recognition and availability of HI resources. Furthermore the system uses information from prior recognitions to automatically build, test, predict confidence, and maintain AI models and HI models for system recognition improvements.