Wake-word Context Processor for Virtual Assistant Query Parsing
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Solution Overview
Problem
Virtual personal assistants often struggle to correctly interpret user queries due to ambiguity in natural language, leading to repetitive interactions and inefficiencies in information retrieval.
Innovation Solution
A method and system that utilize a wake-word processor to extract and classify information from a wake-word database, combined with user preference data, to generate a personalized response by parsing user queries and retrieving relevant information from a knowledge base, thereby reducing the need for repetitive user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple clarification steps are used to resolve query ambiguity, then response accuracy is improved, but interaction time and user burden increase
Solution Approach 1:
The system performs preliminary actions by extracting and processing wake-word information before the main query processing. The wake-word extractor retrieves relevant information from the wake-word database, and the wake-word context processor generates contextual information in advance, allowing the system to have preliminary understanding of user intent before processing the full query, thus reducing the need for multiple clarification steps
Solution Approach 2:
The wake-word context information acts as an intermediary between the user's query and the knowledge base. By generating wake-word context that captures user preferences, historical interactions, and relevant attributes, the system creates a bridging layer that helps interpret ambiguous queries more accurately without requiring repeated user clarifications
2Adaptability or versatility
If wake-word processing and context generation are added, then response personalization is improved, but system complexity increases
Solution Approach 1:
The system segments the virtual assistant architecture into distinct functional modules: wake-word extractor, wake-word parser, wake-word classifier, and wake-word context processor. Each module handles a specific aspect of wake-word processing, making the overall complex system more manageable and maintainable while enabling personalized responses through specialized processing at each stage
Solution Approach 2:
The wake-word database serves multiple functions: storing user preferences, historical interaction data, and contextual information. The wake-word context processor also performs multiple tasks including parsing, classification, and context generation. This multi-functionality reduces the need for separate dedicated components, balancing personalization capabilities with system complexity
3Measurement precision
If wake-word information is extracted and processed, then information retrieval accuracy is improved, but processing time increases
Solution Approach 1:
The wake-word extractor and context processor perform preliminary information extraction and organization before the main knowledge base search. By pre-processing wake-word information to generate relevant context and filters, the system narrows down the search space early, which actually reduces the overall time needed for accurate information retrieval from the knowledge base
Data Source
AI summary
An electronic device for generating a personalized response from a virtual assistant includes a network communication circuitry; a memory; and at least one processor configured to, when receiving a user query including a wake-word, parse the user query to separate the wake-word from the user query; process the wake-word; extract wake-word related information from a wake-word database; parse the extracted wake-word related information along with a plurality of user preference information; classify information received from a wake-word parser; produce a wake-word context from the classified information; process the user query; and retrieve a query response from at least one knowledge base based on a plurality of action steps.