Search Query Clustering via SERP Content Matrix
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Solution Overview
Problem
Current SEO techniques lack the ability to effectively analyze and cluster search queries based on user intent, especially in a rapidly changing online environment where new keywords are constantly being added, making it difficult for web site owners to keep their content strategies up-to-date and optimized.
Innovation Solution
A method and system that access a list of search queries, submit them to a search engine, extract information elements from the search engine result pages, populate a content matrix, compute similarity scores, and cluster search queries based on these scores to identify common user intents, ensuring the most recent search results are integrated into the clustering process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual preparation of content strategy is used, then web site owners can create content based on keyword lists, but the process is time-consuming and cannot keep up with constantly changing search engine keywords and web information
Solution Approach 1:
The system performs automatic keyword extraction, clustering, and content strategy generation without requiring manual human intervention. The automated system continuously monitors search engines, extracts keywords, and generates content strategies autonomously, eliminating the time-consuming manual preparation process while maintaining up-to-date content recommendations.
Solution Approach 2:
The patent replaces the manual mechanical process of keyword research and content planning with an automated computational system. The system uses search engine APIs, data processing algorithms, and machine learning models to automatically analyze search queries, extract keywords, cluster them by intent, and generate content strategies, substituting human manual work with automated computational mechanisms.
2Reliability
If comprehensive keyword lists are created to cover all search queries, then better SEO coverage is achieved, but the complexity of filtering and clustering thousands of keywords into thematic groups increases significantly
Solution Approach 1:
The patent introduces an intermediary clustering system that acts as a bridge between raw search queries and final content themes. The system uses search intent classification as an intermediary step, grouping keywords by user intent categories (informational, navigational, transactional) before further thematic clustering, thereby simplifying the overall process and making it more manageable.
Solution Approach 2:
The patent segments the large set of keywords into smaller, manageable clusters based on search intent and thematic categories. Instead of dealing with thousands of keywords as a single undifferentiated group, the system divides them into intent-based segments, then further clusters each segment into relevant themes, making the complex process systematic and scalable.
3Measurement precision
If search engines process different queries differently based on syntax, then specific query variations can be optimized, but queries with different syntax but same intent return different results reducing SEO efficiency
Solution Approach 1:
The patent changes the parameter used for query matching from syntactic structure to semantic intent. Instead of optimizing for exact syntax matches, the system analyzes the underlying intent of search queries and groups keywords by their semantic meaning. This allows different syntactic variations of the same intent to be recognized and optimized together, improving both precision and adaptability.
Solution Approach 2:
The patent implements a dynamic intent recognition system that adapts to different query formulations. The system continuously learns from search engine results and user behavior patterns, adjusting its intent classification to accommodate new query variations and evolving search behaviors, making the SEO strategy flexible and adaptable rather than rigid and syntax-dependent.
Data Source
AI summary
A method of and a system for clustering search queries. The method comprising accessing a list of search queries, each search query of the list of search queries comprising one or more keywords and, for each search query of the list of search queries, submitting the search query to a search engine; receiving a search engine result page (SERP) from the search engine; extracting a plurality of information elements from the SERP and populating a content matrix with the information elements extracted from the SERP. The method may further comprises computing, based on the content matrix, a distance matrix comprising similarity scores measuring similarities between each search query of the list of search queries and clustering, based on the distance matrix, the search queries of the list of search queries.