NLU-Based Sponsored Search Ranking Simulation
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Solution Overview
Problem
Current search engines struggle to effectively rank and place sponsored search results using natural language queries, as traditional keyword-based systems fail to capture the semantic meaning and context of user queries, leading to irrelevant and inefficient advertising.
Innovation Solution
The development of natural language understanding (NLU) systems, such as Terrier, processes user queries and sponsored trigger patterns into deep structures for semantic matching, allowing for more precise and context-aware ranking and placement of sponsored results, enabling sponsors to define natural language triggers and price points for improved relevance and budget utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If keyword-based systems are used for sponsored search, then the system is simple and easy to implement, but the relevance of sponsored results to natural language queries deteriorates
Solution Approach 1:
The patent introduces natural language understanding (NLU) systems as an intermediary layer between keyword-based search and sponsored result delivery. The NLU system processes user queries to extract semantic meaning, entities, and intent, then matches these against sponsored content using semantic similarity rather than exact keyword matching. This mediator enables the system to handle natural language queries effectively while maintaining sponsorship relevance.
Solution Approach 2:
The patent transforms the matching parameters from simple keyword equality to complex semantic features including entity types, relationships, contextual meaning, and intent classification. By changing the parameter space from discrete keywords to continuous semantic representations, the system achieves better relevance for natural language queries while still supporting sponsored content delivery through semantic pattern matching.
2Manufacturing precision
If natural language understanding systems are implemented for sponsored search, then the relevance of sponsored results improves, but the system complexity and processing requirements increase
Solution Approach 1:
The patent segments the NLU processing into distinct modular components: query analysis module, entity recognition module, semantic pattern matching module, and ranking module. Each component handles a specific aspect of natural language understanding independently, allowing for optimized processing and easier maintenance. This segmentation reduces overall system complexity by breaking down the complex NLU task into manageable, specialized sub-tasks.
Solution Approach 2:
The patent implements preliminary processing of both user queries and sponsored content into standardized semantic representations before the actual matching occurs. Query normalization, entity extraction, and semantic feature extraction are performed in advance, so that the core matching algorithm works with pre-processed, structured data rather than raw natural language. This preliminary action reduces the computational burden during real-time sponsored result delivery.
3Measurement precision
If semantic pattern matching is used instead of keyword matching, then the accuracy of trigger pattern identification improves, but the processing time and computational resources increase
Solution Approach 1:
The patent implements a two-stage matching process where a fast, approximate semantic similarity check is performed first to filter out obviously non-matching sponsored results. Only the most promising candidates then undergo full, accurate semantic pattern matching. This partial action approach achieves high accuracy for final results while reducing overall processing time by avoiding exhaustive detailed matching for all candidates.
Solution Approach 2:
The patent pre-computes and stores semantic features, entity relationships, and contextual patterns for sponsored content in advance, creating simplified semantic copies or representations that can be quickly compared against user queries. These pre-processed semantic copies enable faster matching operations during real-time query processing while maintaining the accuracy benefits of full semantic analysis.
Data Source
AI summary
The technology disclosed relates to natural language understanding-based search engines, ranking sponsored search results and simulated ranking of sponsored search results. Tools and methods describe how to simulate the ranking of sponsored search results. The tools further identify instances of user queries within the scope of trigger patterns, optionally providing examples both of user queries for which a sponsored search result is likely to be displayed and examples for which the sponsored search result will not rank highly enough to be displayed, at least on the first page of search results.