Complex Utterance Parsing for Sequential Command Execution
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Solution Overview
Problem
Current natural language understanding (NLU) systems struggle to process complex utterances that include multiple intents or commands, conditional statements, and repetitive actions, often executing only one command from a single utterance and failing to handle complex constructions such as sequential, conditional, or repetitive utterances.
Innovation Solution
A system is configured to process complex utterances by detecting their construction using rules and machine learning models, determining data types for conditional statements, and executing commands based on satisfied conditions, capable of handling multiple commands and conditional or repetitive actions within a single utterance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current NLU systems process utterances using traditional single-command execution, then system simplicity is maintained, but the ability to handle multiple intents and conditional statements is lost
Solution Approach 1:
The system segments complex utterances into multiple discrete commands by detecting construction types (sequential, conditional, repetitive) and parsing the utterance accordingly. Each command is then processed independently through the NLU pipeline, enabling the system to handle multiple intents while maintaining manageable processing complexity through divide-and-conquer.
Solution Approach 2:
The system dynamically adapts its processing approach based on the detected construction type of the utterance. Different parsing strategies are applied for sequential, conditional, and repetitive constructions, allowing the system to optimize its behavior for each specific case rather than using a fixed rigid processing model.
2Productivity
If the system executes only one command from a single utterance, then processing speed is maintained, but the completeness of command execution deteriorates
Solution Approach 1:
The system performs preliminary detection of the utterance construction type and identifies all commands within the utterance before executing any of them. This advance preparation allows the system to batch-process multiple commands efficiently, ensuring complete execution without significant time penalty.
3Adaptability or versatility
If the system handles complex constructions like sequential and repetitive utterances, then user interaction capability is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary construction detection layer that analyzes the utterance structure and identifies the construction type (sequential, conditional, repetitive) before passing it to the command execution engine. This intermediary layer simplifies the overall architecture by handling the complexity of interpretation separately from the complexity of execution.
Data Source
AI summary
A system capable of performing natural language understanding (NLU) on utterances including complex command structures such as sequential commands (e.g., multiple commands in a single utterance), conditional commands (e.g., commands that are only executed if a condition is satisfied), and/or repetitive commands (e.g., commands that are executed until a condition is satisfied). Audio data may be processed using automatic speech recognition (ASR) techniques to obtain text. The text may then be processed using machine learning models that are trained to parse text of incoming utterances. The models may identify complex utterance structures and may identify what command portions of an utterance go with what conditional statements. Machine learning models may also identify what data is needed to determine when the conditionals are true so the system may cause the commands to be executed (and stopped) at the appropriate times.