Constraining NLP via Metadata Domains for Digital Assistants
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Solution Overview
Problem
Digital assistants face challenges in accurately interpreting user intent due to the computational intensity of natural language processing, often leading to incorrect intent deduction and increased response latency, especially in contexts where multiple semantic intents are possible.
Innovation Solution
The system leverages interaction context to constrain natural language processing, determining whether the speech input is directed to specific text input fields, thereby narrowing the domains for processing and improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural language processing is performed on the text string without domain constraints, then the digital assistant can potentially identify any semantic intent, but the processing becomes computationally intensive and response latency increases
Solution Approach 1:
The patent segments the broad natural language processing task into domain-specific sub-tasks. By identifying the relevant domain from metadata (e.g., email, text message, instant message) and constraining NLP to that domain, the system divides the large solution space into smaller, more manageable segments. This segmentation reduces computational complexity and processing time while maintaining accurate intent identification within each domain context.
Solution Approach 2:
The patent applies local quality by tailoring the NLP processing to the specific domain context. Instead of applying a uniform, general-purpose NLP approach to all inputs, the system adapts the processing characteristics to match the specific domain (email vs. text message vs. instant message), optimizing accuracy for each local context while reducing unnecessary computational overhead.
2Measurement precision
If natural language processing is performed comprehensively across all possible domains, then the accuracy of semantic intent deduction improves, but the computational resources and processing time required increase
Solution Approach 1:
The patent extracts only the necessary domain information from metadata that is relevant to the current input, rather than processing all possible domain characteristics. By extracting and utilizing only the specific domain context needed for accurate intent deduction (e.g., determining this is an email context and only applying email-specific NLP rules), the system achieves high accuracy while minimizing computational energy consumption.
Solution Approach 2:
The patent applies partial action by performing NLP processing only to the extent necessary for the specific domain and context, rather than exhaustively analyzing all possible semantic intents. The system performs just enough processing to accurately identify the intent within the constrained domain, avoiding excessive computation that would waste energy without improving accuracy.
3Device complexity
If the digital assistant processes speech inputs without considering interaction context, then the system complexity remains low, but the accuracy of intent interpretation deteriorates when multiple semantic intents are possible
Solution Approach 1:
The patent performs preliminary action by extracting domain information from metadata before the main NLP processing occurs. By pre-identifying the domain context (email, text message, instant message) from available metadata, the system prepares the appropriate processing framework in advance, which simplifies the subsequent NLP task and improves accuracy without significantly increasing overall system complexity.
Data Source
AI summary
Systems and processes for structured dictation using intelligent automated assistants are provided. In one example process, a speech input representing a user request can be received. In addition, metadata associated with the speech input can be received. A text string corresponding to the speech input can be determined. The process can determine whether to perform natural language processing on the text string and whether the metadata identifies one or more domains corresponding to the user request. In response to the determination that natural language processing is to be performed on the text string and that the metadata identifies one or more domains corresponding to the user request, natural language processing of the text string can be constrained to the one or more domains. A result can be obtained based on the one or more domains and the result can be outputted from the electronic device.