Domain-Aware Autocomplete for Accurate Search Query Resolution
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Solution Overview
Problem
Traditional autocomplete solutions are static and provide limited vocabulary, leading to incorrectly spelled, inconsistent, and low-variety suggestions, and fail to predict current data accurately, resulting in null or irrelevant search results.
Innovation Solution
A domain-aware autocomplete model that is language agnostic and customizable to specific domains, trained using a target domain source, generates domain-specific suggestions by decorrelating keywords, categorizing suggestions, and resolving search queries based on suggestion-category pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional autocomplete solutions use static vocabulary from aggregated user query logs, then the system can provide autocomplete suggestions, but the suggestions become incorrectly spelled, inconsistent, and low in variety
Solution Approach 1:
The patent transforms the static autocomplete vocabulary from aggregated user query logs into dynamic domain-specific suggestions by changing the data source parameters. Instead of using general user logs, the system now uses domain-specific data sources (e.g., medical databases, technical documentation) and applies domain-specific processing rules to generate suggestions that maintain spelling accuracy and consistency while improving relevance.
Solution Approach 2:
The patent segments the autocomplete system into domain-specific modules. Each domain (e.g., medical, technical, commercial) has its own dedicated autocomplete model trained on domain-specific data. This segmentation allows each module to maintain high spelling accuracy and consistency within its domain while providing diverse suggestions across different domains.
2Productivity
If traditional autocomplete solutions use historical search query data, then the system can generate autocomplete suggestions, but the suggestions fail to predict current data accurately, resulting in null or irrelevant search results
Solution Approach 1:
The patent makes the autocomplete system dynamic by continuously updating domain-specific data sources and retraining models with current domain data. Instead of relying on static historical search logs, the system incorporates real-time domain data updates, ensuring that autocomplete suggestions reflect current available data and improve search query resolution accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where search result outcomes are fed back into the domain-specific data sources. When search queries return null or irrelevant results, this feedback is used to refine and update the domain-specific autocomplete models, improving their ability to predict current data accurately over time.
3Device complexity
If traditional autocomplete solutions use a limited static vocabulary, then the system can operate with simple architecture, but the autocomplete suggestions offer low variety and are inconsistent
Solution Approach 1:
The patent creates a universal domain-aware autocomplete framework that can adapt to multiple domains (medical, technical, commercial, etc.) while maintaining a consistent architectural structure. Each domain uses the same underlying framework but with domain-specific data sources and models, providing both variety in suggestions and consistency in system operation.
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.