Domain-Aware Autocomplete for Current Search Query Resolution
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Solution Overview
Problem
Traditional autocomplete solutions are static and provide limited vocabulary, often generating incorrectly spelled, inconsistent, and outdated suggestions that fail to predict current data on a website, leading to poor search results.
Innovation Solution
A domain-aware autocomplete model that is language agnostic and adaptable to specific domains, using a training dataset to generate domain-specific suggestions through a combination of label decorrelation, sentence classification, and search query resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional autocomplete solutions use static vocabulary based on historical query logs, then implementation is simple, but the suggestions become outdated and inaccurate for current data
Solution Approach 1:
The patent implements dynamic autocomplete suggestions by continuously updating the vocabulary with current data from the target domain website. The system extracts terms from current website content and user queries, maintaining a living vocabulary that adapts to changing domain terminology and data structures over time, rather than relying on static historical logs.
Solution Approach 2:
The system incorporates feedback mechanisms by analyzing actual user query patterns and search result performance to refine autocomplete suggestions. User interactions with the search interface provide feedback that informs future suggestion generation, allowing the system to learn from real-time usage patterns and improve accuracy iteratively.
2Adaptability or versatility
If traditional autocomplete uses aggregated user query logs, then data collection is straightforward, but suggestions become inconsistent and offer low variety
Solution Approach 1:
The patent segments the autocomplete vocabulary into domain-specific categories and subcategories organized around the target domain's data structure. This segmentation allows the system to generate varied suggestions tailored to different contextual categories within the domain, improving variety while maintaining relevance to current website content.
Solution Approach 2:
The system performs preliminary extraction and organization of domain terminology from current website content before generating autocomplete suggestions. By pre-processing and structuring the vocabulary based on current domain data, the system ensures diverse and contextually appropriate suggestions are ready for immediate use without requiring real-time analysis during user interaction.
3Reliability
If traditional autocomplete generates suggestions based on past queries, then implementation is simple, but suggestions fail to predict current data availability
Solution Approach 1:
The system performs preliminary updates of the autocomplete vocabulary by continuously monitoring and incorporating current website content before user queries are processed. This advance preparation ensures the vocabulary reflects the most recent data available, reducing time lag between data updates and suggestion generation.
Solution Approach 2:
The autocomplete system serves itself by automatically extracting and organizing domain terminology from current website content without requiring manual intervention. The system self-updates its vocabulary by analyzing new website data and user query patterns, maintaining currentness of suggestions through autonomous continuous improvement.
Data Source
AI summary
Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion. Example embodiments are also configured for initiating performance of a search query resolution based on the SCP.