Incremental Semantic State Updates for Real-Time Voice Fulfillment
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Solution Overview
Problem
Conventional natural language understanding systems update semantic states only at the end of an utterance, failing to provide real-time updates and context-dependent suggestions during speech processing.
Innovation Solution
A system that processes fragments of speech in real-time using a speech engine, fragment integrator, and domain handler to continuously update the semantic state, allowing mid-utterance context-aware suggestions and corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional natural language understanding systems process complete sentences only at the end of utterance, then system complexity is reduced, but real-time responsiveness and context-aware suggestions are lost
Solution Approach 1:
The patent segments the utterance processing into fragment-level units rather than treating complete sentences as atomic entities. The speech stream is divided into transcribable fragments that can be processed incrementally as they are recognized, enabling real-time updates without requiring complete sentence boundaries. This segmentation allows the system to maintain lower complexity by processing smaller, manageable units sequentially.
Solution Approach 2:
The system performs preliminary processing of speech fragments as they are transcribed, building up semantic understanding incrementally before the utterance is complete. By performing NLU operations on partial fragments in advance rather than waiting for complete sentences, the system achieves real-time responsiveness while managing complexity through staged processing.
2Measurement precision
If semantic state is updated only at the end of each sentence, then processing simplicity is maintained, but user feedback and suggestion accuracy are reduced
Solution Approach 1:
The patent implements continuous feedback loops where each transcribed fragment triggers an update to the semantic state, which is then reflected in real-time suggestions and responses. This feedback mechanism allows the system to continuously refine its understanding and provide accurate context-aware suggestions during the utterance, not just after completion, reducing feedback delay while maintaining precision through iterative state updates.
Solution Approach 2:
The system maintains continuous semantic state updates throughout the utterance duration rather than performing discrete updates only at sentence boundaries. This continuity ensures that context understanding accuracy is preserved at all times while eliminating dead time where no processing occurs, allowing the system to provide timely feedback without sacrificing measurement precision.
3Productivity
If fragment-level processing is implemented, then real-time suggestions are enabled, but computational load and processing complexity increase
Solution Approach 1:
The patent implements dynamic processing where the level of fragment integration and NLU processing adapts based on the current semantic state and confidence levels. Rather than uniformly processing every fragment at full complexity, the system dynamically adjusts processing depth, performing simpler operations when sufficient context is already established and more intensive operations only when needed, thereby increasing productivity without proportionally increasing processing complexity.
Data Source
AI summary
A system and method of real-time feedback confirmation to solicit a virtual assistant response from an evolving semantic state of at least a portion of an utterance. A user accesses a virtual assistant on an electronic device having the system and/or method configured to capture a command, a question, and/or a fulfillment request from audio such as, the speech emitted from the speaking user. The speech may be intercepted by a speech engine configured to transcribe the speech into text that is matched with the fragment pattern's regular expression to generate a fragment and/or the speech may be processed with a machine learning model to identify fragments. The fragments are identified by a domain handler configured to update a data structure of the current semantic state of the utterance in real-time on an interface of an electronic device.