Personalized Spelling Correction Using Contact Data Models
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Solution Overview
Problem
Traditional search engines struggle to provide accurate spelling corrections for personal data due to insufficient training data, as existing methods rely on large-scale data models that are not effective for personal queries or contact lists, leading to inefficient and inaccurate suggestions.
Innovation Solution
A system that generates spelling correction candidates based on personal data, such as contact lists and search history, using a simplified evaluation function to provide real-time query suggestions without building complex data models, by shortening user input prefixes and calculating misspelling likelihoods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional large-scale data models are used for spelling correction, then general population spelling accuracy is improved, but personal data spelling correction accuracy deteriorates due to insufficient training data
Solution Approach 1:
The patent segments the spelling correction problem into two distinct components: (1) general spelling correction using large-scale data models for common words, and (2) personal data spelling correction using a specialized personal data model trained on user-specific data. This segmentation allows each component to be optimized for its specific purpose, resolving the contradiction between general accuracy and personal accuracy.
Solution Approach 2:
The patent applies local quality by creating a dedicated personal data model with properties specifically tailored for personal data spelling correction. Instead of using a single uniform model, the system employs a specialized model that focuses on user-specific patterns, contacts, and personal query histories, thereby achieving high accuracy for personal data while maintaining general model performance for common words.
2Measurement precision
If complex data models are built for personal spelling correction, then spelling accuracy is improved, but system complexity increases
Solution Approach 1:
The patent extracts the personal data spelling correction functionality from the general spelling correction system. By separating personal data processing into a dedicated model and system component, the patent avoids the complexity of trying to handle both general and personal spelling correction within a single complex model. The extracted personal data model can be trained and optimized independently with simpler architecture.
Solution Approach 2:
The patent introduces a personal data model as an intermediary component between the user's personal data and the spelling correction process. This intermediary model simplifies the overall system architecture by handling personal data-specific corrections separately, rather than requiring the general model to be overly complex to accommodate personal data patterns.
3Ease of operation
If real-time query suggestions are provided, then user experience is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the personal data model offline using user-specific data from contact lists, search histories, and query logs. This pre-training phase prepares the model in advance, so that during real-time query suggestion generation, the system can quickly apply the pre-learned patterns without performing complex training operations, thereby reducing online processing time while maintaining high accuracy.
Data Source
AI summary
The present teaching relates to providing a query suggestion. In one example, a request is received for query suggestions with respect to a query prefix input by a user. Personal data of the user are obtained. One or more spelling correction candidates are generated based on the personal data and the query prefix. Each of the one or more spelling correction candidates represents a possible spelling correction of the query prefix that was likely input incorrectly in view of the personal data. One or more query suggestions are determined based on the one or more spelling correction candidates. The one or more query suggestions are provided as a response to the request.