Real-Time Voice Feedback Search Query Enhancement
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Solution Overview
Problem
Users often encounter irrelevant search results when using software applications and online services due to the limitations of current search features, which fail to accurately identify content related to their queries, leading to a poor user experience.
Innovation Solution
A method that evaluates user feedback using a probabilistic topic model, such as Latent Dirichlet Allocation (LDA), to identify relevant topics and keywords, enhancing the initial search query with these topics and keywords to improve the relevance of search results in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search features are used to retrieve content, then the search process is simple and fast, but the search results are irrelevant and do not accurately identify content related to user queries
Solution Approach 1:
The patent introduces an intermediary component (feedback evaluation module with probabilistic topic model) between the user query and the content repository. This intermediary processes user feedback and original queries to generate enhanced search queries, thereby improving search result relevance without requiring complete system redesign.
Solution Approach 2:
The system implements a feedback mechanism where user responses to initial search results are evaluated to identify topics and keywords. This feedback is then used to generate enhanced search queries, creating a closed-loop system that continuously improves search accuracy based on actual user interactions.
2Adaptability or versatility
If user feedback is collected and processed in real-time to personalize content, then the user experience is improved through personalization, but the system complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user feedback to identify topics and keywords before the actual search enhancement occurs. The probabilistic topic model pre-analyzes feedback patterns, allowing the system to quickly generate enhanced queries without complex real-time processing during user interactions.
Solution Approach 2:
The system changes parameters by transforming original search queries into enhanced search queries based on feedback analysis. The probabilistic topic model adjusts query parameters (keywords, topics) dynamically, enabling content personalization through parameter modification rather than structural system changes.
3Measurement precision
If enhanced search queries are generated using feedback evaluation, then the quality of search results is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively enhancing only the most relevant aspects of search queries based on feedback analysis. Rather than completely reprocessing all queries, the system focuses computational resources on identifying and enhancing key topics and keywords, achieving good results with reduced processing overhead.
Data Source
AI summary
Techniques are disclosed for dynamically personalizing application content presented to a user based on “voice-of-customer” feedback, in real-time. As described, the user may provide voice-of-customer feedback characterizing an initial selection of application content presented to the user may be evaluated to identify a set of topics referenced by the voice-of-customer feedback. Keywords associated with the identified topics may be used to enhance the selection of application content presented to the user. For example, keywords associated with the identified topics may be added to an initial search query composed by the user. Doing so can improve the relevance or helpfulness of information content or software interfaces identified using the query.