Voice Query Dynamic Type Detection for Accurate Interpretation
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Solution Overview
Problem
Existing voice query systems struggle to interpret dynamic types or non-static categorizations, leading to inaccurate responses when users search for content not present in data/knowledge graphs, requiring manual clicks for precise queries.
Innovation Solution
A system that analyzes voice queries using Automatic Speech Recognition (ASR) to convert speech to text, identifies entities and dynamic types through natural language understanding (NLU), and generates tags based on contextual inputs, user history, and trends to accurately interpret and respond to dynamic queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses predetermined static categorizations from data/knowledge graphs, then the system structure is simple and queries can be processed quickly, but the system cannot interpret dynamic types or non-static categorizations leading to inaccurate responses
Solution Approach 1:
The system transitions from static categorization to dynamic type identification by analyzing word sequences, parts of speech sequences, and query patterns in real-time. The dynamic type detector identifies types based on dynamic characteristics of the query rather than predetermined static categories, enabling accurate interpretation of non-static content.
Solution Approach 2:
The patent replaces traditional mechanical keyword-matching systems with linguistic analysis mechanisms including part-of-speech tagging, sequence analysis, and pattern recognition. This substitution enables the system to interpret dynamic types through linguistic structures rather than rigid categorical matching.
2Ease of operation
If the system requires manual clicks for precise queries, then query accuracy can be achieved, but user interaction becomes cumbersome and productivity decreases
Solution Approach 1:
The system performs self-service by automatically identifying dynamic types and generating appropriate interpretations without requiring user intervention. The dynamic type detector and query interpreter work autonomously to analyze the voice query, identify entities, and determine the intended meaning, eliminating the need for manual clicking while maintaining high accuracy.
Solution Approach 2:
The system uses feedback from linguistic analysis (parts of speech, word sequences, patterns) to continuously refine query interpretation. The dynamic type identification provides feedback that guides the query interpreter to select the most appropriate interpretation from multiple possibilities, improving accuracy without user input.
3Measurement precision
If the system performs real-time dynamic type identification, then query interpretation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary linguistic analysis by identifying parts of speech and word sequences before full query interpretation. This preliminary action prepares the data structure for faster dynamic type detection, reducing the computational burden during the actual query processing phase while maintaining high accuracy.
Solution Approach 2:
The query processing is segmented into distinct stages: speech-to-text conversion, entity identification, dynamic type detection, and query interpretation. Each segment handles specific tasks independently, allowing for optimized processing at each stage and reducing overall processing time through parallelization of independent operations.
Data Source
AI summary
The system receives a voice query at an audio interface and converts the voice query to text. The system identifies entities included in the query based on comparison to an information graph, as well as dynamic types based on the structure and format of the query. The system can determine dynamic types by analyzing parts of speech, articles, parts of speech combinations, parts of speech order, influential features, and comparisons of these aspects to references. The system combines tags associated with the identified entities and tags associated with the dynamic types to generate query interpretations. The system compares the interpretations to reference templates, and selects among the query interpretations using predetermined criteria. A search query is generated based on the selected interpretation. The system retrieves content or associated identifiers, updates metadata, updates reference information, or a combination thereof. Accordingly, the system responds to queries that include non-static types.