SERP Topic Graph Generation Through Loose Keyword Clustering
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Solution Overview
Problem
Content authors face challenges in ensuring their content is ranked highly in search engine results, despite its relevance, due to unintuitive ranking criteria, leading to limited visibility among viewers.
Innovation Solution
A system generates a topic graph based on search engine results page data, clustering keywords with a threshold degree of similarity to create loosely affiliated groups, allowing for more effective content creation and optimization strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content authors create content based on traditional search engine indexing, then content may be relevant to viewers, but content visibility and ranking are limited due to unintuitive ranking criteria
Solution Approach 1:
The patent introduces a topic graph as an intermediary structure between content authors and search engine ranking algorithms. The topic graph translates content into a structured format with topics, sub-topics, and hierarchical relationships that align with search engine expectations, thereby improving visibility while maintaining relevance
Solution Approach 2:
The patent changes the parameters of content representation from traditional keyword-based indexing to a hierarchical topic-based structure. By organizing content into topics with varying levels of specificity and incorporating related terms, the system optimizes ranking parameters to achieve both relevance and visibility
2Quantity of substance
If search engines use complex indexing algorithms for ranking, then search results may be comprehensive, but ranking criteria become unintuitive and content optimization becomes difficult
Solution Approach 1:
The patent segments the content optimization process into manageable components: identifying main topics, creating sub-topics, establishing hierarchical relationships, and selecting related terms. This segmentation simplifies the complex ranking algorithm into actionable steps for content authors
Solution Approach 2:
The patent incorporates feedback mechanisms where content authors can review suggested topic graphs, adjust topics based on their content, and see predicted ranking improvements. This feedback loop makes the complex ranking criteria more transparent and easier to work with
3Adaptability or versatility
If content addresses multiple aspects of a concept to appeal to viewers, then content relevance increases, but it becomes harder to optimize for specific search keywords
Solution Approach 1:
The patent segments the content structure into hierarchical topics and sub-topics, allowing authors to address multiple aspects of a concept at different levels of granularity. This segmentation enables simultaneous optimization for broad and specific keywords while maintaining comprehensive content appeal
Solution Approach 2:
The patent adds a hierarchical dimension to content organization, moving from flat keyword lists to multi-level topic structures. This dimensional change allows content to be optimized for keywords at various levels of specificity while maintaining versatility and appeal to diverse viewer interests
Data Source
AI summary
A system generates a topic graph based on the SERP data for high-ranking keywords in a search engine. Clustering may be based on (for example) degrees of intersection between links in search results of keywords from the SERP data, or keyword embeddings on the SERP data. The topic graph loosely clusters the keywords, such that the keywords have at least a threshold degree of similarity to their clusters, but not necessarily to all the other keywords in the cluster. As a consequence of the loose clustering, a given topic contains keywords that represent different aspects of the same concept, such that a content viewer would likely be interested in a piece of content that addresses the different aspects, and a search engine would be more likely to highly rank the content within its search results for one of the keywords. The system may also provide a user interface permitting a user to browse and filter the topics in the topic graph according to search criteria, as well as to see the topics ordered according to topic ROI estimates computed by the system.