Rule-Based System for Search Query Attribute Association
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Solution Overview
Problem
Existing search technologies face challenges in efficiently processing and analyzing vast volumes of search queries to identify user interests and preferences, leading to suboptimal content recommendation and title selection for online content.
Innovation Solution
A rule-based system that parses search queries, applies multiple rules to associate attributes such as intent, query type, and tags, and uses dictionaries to disambiguate words, enabling the selection of potential titles and content recommendations based on user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processing is applied to search queries, then information extraction efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of search query analysis into distinct components: a rule engine that applies structured rules and a machine learning model that handles pattern recognition. This segmentation allows each component to specialize in specific aspects of query understanding, improving overall efficiency while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary layer (the rule engine) that processes search queries before they reach the machine learning model. This intermediary applies predefined rules to filter and prepare queries, reducing the complexity burden on the ML model and improving overall processing efficiency by handling routine cases rule-based.
2Measurement precision
If multiple rules are applied to search queries, then attribute association accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by applying rule-based filtering and attribute association before invoking the machine learning model. This preliminary processing handles straightforward cases quickly with high accuracy, reserving more time-consuming ML operations for complex queries that require deeper analysis.
Solution Approach 2:
The patent applies partial action by selectively applying multiple rules based on query characteristics. Not all rules are applied to every query; instead, the system determines which rules are relevant to each specific query, applying only the necessary subset to maintain accuracy while minimizing processing time.
3Measurement precision
If search query logs are mined for user interests, then content recommendation quality is improved, but data processing complexity increases
Solution Approach 1:
The data processing pipeline is segmented into distinct stages: initial rule-based attribute extraction, machine learning-based pattern recognition, and final recommendation generation. This segmentation allows each stage to focus on specific aspects of user interest extraction, improving recommendation quality while managing processing complexity through structured modular processing.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with a hybrid system combining rule-based processing and machine learning. This substitution enables the system to handle large volumes of query logs more efficiently, extracting user interests with higher precision while reducing the computational complexity compared to pure ML approaches.
Data Source
AI summary
A method implemented in a data processing system includes receiving a plurality of text strings. A plurality of rules are applied to the text strings. If a condition specified in one of the rules exists in a given text string, one or more attributes are associated to that text string as metadata. One or more of the text strings are selected, using the metadata, as a potential title for the content. A final title is prepared based on the potential title, and the content is published online under the final title.