Domain Name Suggestion System Using Machine Learning Ranking
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Solution Overview
Problem
Current methods for finding available domain names are limited by static language dictionaries that fail to rank popularity and consider domain name transformation types, leading to difficulties in finding generic and short domain names that are not already registered.
Innovation Solution
A system and method that aggregates knowledge base data to identify available domain names, filters out grammatically incorrect ones, and uses machine learning to rank suggestions based on frequency and co-occurrence analysis, providing intelligent domain name generation and ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static language dictionaries are used to generate domain names, then the process is simple and fast, but the quality and relevance of suggested domain names deteriorates
Solution Approach 1:
The patent transitions from static language dictionaries to dynamic machine learning models that continuously learn from user behavior patterns and domain name usage data. This parameter change enables the system to adapt to evolving language patterns and user preferences, improving domain name relevance while maintaining generation speed through optimized algorithms.
Solution Approach 2:
The system incorporates feedback loops where user interactions with suggested domain names (registration, viewing, abandonment) are fed back into the machine learning model. This feedback mechanism allows the system to continuously refine its predictions about which domain names are most likely to be useful, improving relevance over time without sacrificing speed.
2Adaptability or versatility
If more domain name suggestions are generated to increase availability options, then the user's chances of finding an available domain improves, but the time required to review and select deteriorates
Solution Approach 1:
The patent applies local quality by providing different levels of detail and customization based on user needs and context. The system can provide a comprehensive list of suggestions when needed but also offer highly filtered, pre-ranked results when users need quick options. This localized adaptation of suggestion quality optimizes both availability and time efficiency.
Solution Approach 2:
The machine learning model performs preliminary ranking and filtering of domain name suggestions based on predicted user preferences before presentation to the user. This preliminary action reduces the effective number of options the user needs to review while maintaining high availability chances, as the most relevant options are prioritized from the start.
3Measurement precision
If domain name suggestions are highly customized to user preferences, then the relevance and quality improve, but the system complexity increases
Solution Approach 1:
The system performs self-service by automatically learning and adapting to user preferences through analysis of search patterns, registration behavior, and feedback without requiring manual configuration or complex setup. The machine learning models continuously refine their understanding of user preferences autonomously, providing highly customized suggestions while keeping the user interface simple and the system manageable.
Data Source
AI summary
Systems and methods of the present invention provide for one or more server computers communicatively coupled to a network and configured to: receive a domain name search string; identify: a token, within the domain name search string, reflecting a user pattern; a next element in the sequence for the user pattern; and an available domain name comprising a string reflecting the next element in the sequence; and transmit the available domain name to a client computer communicatively coupled to the network.