Neural Network Domain Name Generation
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Solution Overview
Problem
Domain investors face challenges in identifying unregistered internet domain names with high resale value, as existing methods rely on manual enumeration and lack the ability to capture deep contextual relationships in character-level features.
Innovation Solution
A method and system using machine learning, specifically neural networks, to identify and generate unregistered domain names by vectorizing and training on a subset of registered domain names with specified characteristics, producing a trained model that generates novel domain names with high potential value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual enumeration methods are used to identify domain names, then human labor is required, but productivity is low and quality is limited
Solution Approach 1:
The system uses automated machine learning models to generate domain names without requiring manual human intervention. The neural network independently processes training data, generates candidate domain names, and evaluates their potential value, replacing manual enumeration with self-service automation.
Solution Approach 2:
The patent replaces manual mechanical operations with an electronic machine learning system. The neural network processes character-level features and generates domain names through computational algorithms, substituting human cognitive and manual labor with automated electronic processing.
2Manufacturing precision
If traditional techniques are used to generate domain names, then the process is simple, but the quality and resale value potential of generated names is lower
Solution Approach 1:
The patent introduces a trained machine learning model as an intermediary between the input data and the generated domain names. This intermediary processes character-level features, captures deep contextual relationships, and transforms raw data into high-quality domain name candidates, improving precision through intelligent mediation.
Solution Approach 2:
The system changes the parameters of domain name generation by using neural networks to analyze character-level features and contextual relationships. This transforms the generation process from simple random or rule-based methods to a sophisticated parameter-driven approach that considers multiple linguistic and contextual factors.
3Measurement precision
If deep contextual relationships are not captured, then the system is simpler, but the ability to identify high-value domain names is reduced
Solution Approach 1:
The patent segments the domain name analysis into character-level features, allowing the neural network to process and capture deep contextual relationships at the finest granularity. This segmentation enables precise measurement of contextual patterns while maintaining systematic complexity through structured feature extraction.
Data Source
AI summary
Techniques for generating unregistered internet domain names using machine learning (e.g., neural networks) are presented. The techniques can include identifying, using an electronic processor, a subset of registered domain names having at least one specified characteristic, vectorizing, using an electronic processor, a training subset of domain names in the subset of registered domain names to obtain a set of vectors, training, using an electronic processor, a machine learning algorithm with the set of vectors to produce a trained machine learning model, generating, using an electronic processor, at least one output domain name by the trained machine learning model, and outputting the at least one output domain name.