Keyword Ranking System Using ML and TAM Analysis
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Solution Overview
Problem
Existing keyword selection and ranking methods for search engine optimization (SEO) are inefficient due to the vast universe of potential keywords, high time and cost requirements, inconsistency, and susceptibility to keyword competition, algorithm updates, and other issues, leading to suboptimal results and repeated mistakes.
Innovation Solution
A method and system using Total Addressable Market (TAM) analysis to generate and filter potential keywords, assign semantic similarity scores, and guide keyword selection through machine learning and natural language processing, enhancing keyword ranking and selection over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual keyword assessment and analysis is performed to select desirable keywords, then keyword selection accuracy can be improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical keyword assessment with an automated computer-based system that uses machine learning models and natural language processing to evaluate keywords, calculate metrics like keyword opportunity scores and total addressable market estimates, and generate recommendations without human intervention
Solution Approach 2:
The system performs self-service by automatically generating keyword lists, assessing their potential value through ML models, calculating TAM estimates, and providing ranked recommendations without requiring manual research or analysis, enabling the system to autonomously complete the keyword selection process
2Measurement precision
If comprehensive keyword universe analysis is conducted to assess all potential keywords, then keyword selection quality improves, but processing cost and complexity increase
Solution Approach 1:
The patent segments the comprehensive keyword assessment process into distinct modular components: keyword generation, filtering, ML-based scoring, TAM calculation, and ranking. Each module handles a specific aspect of the analysis, making the overall complex process manageable and scalable
Solution Approach 2:
The system changes parameters by using machine learning models to dynamically calculate keyword opportunity scores and TAM estimates based on multiple factors including search volume, competition levels, and relevance metrics, allowing flexible adjustment of assessment criteria without increasing manual complexity
3Ease of operation
If existing keyword selection methods are used, then implementation is straightforward, but results are inconsistent and not easily replicable
Solution Approach 1:
The system incorporates feedback mechanisms where machine learning models are trained on historical keyword performance data and continuously improved based on results, ensuring consistent and replicable outcomes. The standardized scoring system provides feedback loops that maintain reliability across different implementations
Solution Approach 2:
The patent creates a universal keyword assessment system that can be applied across different industries, topics, and time periods using the same ML models and evaluation framework, making results consistent and replicable regardless of specific application context
4Loss of information
If manual keyword research and analysis is performed, then detailed insights can be obtained, but the process becomes expensive and error-prone
Solution Approach 1:
The patent replaces expensive manual research processes with automated machine learning systems that perform comprehensive keyword analysis at minimal marginal cost, maintaining or improving analysis depth through systematic evaluation of multiple keywords simultaneously
Solution Approach 2:
The system uses machine learning models that can be replicated and deployed multiple times without additional cost, allowing the same high-quality analysis to be copied and applied to different keyword sets, topics, or time periods without incurring repeated manual research expenses
Data Source
AI summary
Provided are a method and system for selecting and ranking keywords, including a process through which a universe of potential keywords tied to various topics is generated and filtered. Once this is completed, search engine ranking pages (SERPs) are generated for these potential keywords. Reference text associated with topics of interest is also generated by the system of the present invention. This can be accomplished in various ways according to the teachings herein. For example, reference text can be generated using generative AI functionality. Initial reference text can be later improved upon using machine learning techniques so as to adjust reference text that is too broad or narrow in scope. Reference text and SERP results are used in connection with a model to generate semantic similarity scores which are then used for ranking and selecting keywords.