Multi-Level Voice Search System for Contextual Query Refinement
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Solution Overview
Problem
Current voice-based searching systems are limited in providing specific and contextually relevant results, as they convert voice queries to broad textual representations, failing to dynamically adjust to changing user queries, which limits their ability to support detailed inquiries beyond the initial search intent.
Innovation Solution
A multi-level voice search system that converts voice queries to text, uses natural language processing to determine user intent and context, and dynamically adjusts search queries through a conversational pattern, linking metadata to update and cache relevant information, allowing for more specific and contextual searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If voice queries are converted to broad textual representations for searching, then the search system can process queries using standard text-based algorithms, but the search results become too general and fail to capture specific user intent and context
Solution Approach 1:
The patent segments the search process into multiple hierarchical levels: initial broad search, followed by subsequent searches at increasingly specific levels. Each search level focuses on progressively more specific aspects of the user's intent, transforming a single broad text search into a multi-level conversational search process that maintains both accessibility and specificity.
Solution Approach 2:
The system performs preliminary analysis of the voice query to determine user intent and context before executing the search. By pre-processing the query to extract semantic meaning, user intent, and contextual information, the system prepares targeted search parameters in advance, enabling subsequent searches to be more specific without requiring complex real-time processing.
2Adaptability or versatility
If the search system performs only initial broad searches, then the system remains simple and fast, but it cannot dynamically adjust to changing user queries or support detailed follow-up inquiries
Solution Approach 1:
The patent implements periodic action by structureing the search as a sequence of discrete search levels rather than continuous processing. The system performs an initial search, evaluates results, and then periodically executes subsequent searches at higher levels of specificity based on conversation flow. This periodic, multi-level approach allows dynamic adaptation while maintaining efficient response times through clear temporal boundaries between search stages.
Solution Approach 2:
The search system dynamically adjusts its behavior based on conversation context and user responses. The system transitions from static broad searches to dynamic multi-level searches that adapt to changing user intent. Each subsequent search is dynamically tailored to the specific context established in previous interactions, enabling the system to respond flexibly to evolving query requirements.
3Measurement precision
If the system performs multiple levels of contextual searches, then search results become more relevant and specific to user intent, but the processing complexity and computational resources increase
Solution Approach 1:
The patent segments the complex processing task into manageable hierarchical levels. Instead of performing all contextual analysis simultaneously, the system divides processing into discrete search levels, where each level handles a specific aspect of the query. This segmentation reduces the computational burden at any single processing stage while achieving high overall accuracy through cumulative refinement.
Solution Approach 2:
The system performs preliminary processing to extract user intent, context, and key parameters from the initial voice query before executing subsequent searches. By pre-analyzing the query and preparing contextual information in advance, the system reduces the complexity of subsequent processing stages, as later searches can leverage the structured information already extracted rather than re-processing the entire query.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for improved text and voice based searching. In various embodiments, a multi-level voice search system is implemented. In various embodiments, a voice based search is converted to text, where the text is used to determine a user's intent and context of the search. The search can be for a particular object and for data or information related to the object. Subsequent searches, which can be from a conversation using voice based searching can change the context and be directed to a different more specific level of the object. Metadata can link data or information, such as documents to one another. Based on search history or patterns, data or information can be updated.