The domain registry patented an improved way to select prefixes and suffixes for domain suggestions.
The U.S. Patent and Trademark Office has granted patent number 11,468,336 (pdf) to Verisign (NASDAQ: VRSN) for Systems, devices, and methods for improved affix-based domain name suggestion.
An affix is a prefix or suffix, so Verisign has patented an improved way to stick relevant prefixes and suffixes on words to suggest domain names.
There are lots of name spinners that add affixes to domain names, but the patent states, “current domain name suggestion systems may base the suggestion on occurrences of the affix with the input string in a training set (e.g., using maximum likelihood estimates based on occurrences of the sequence), and are unable and/or have difficulty in assigning a value to affixes that do not occur with the input string in the training set.”
Getting a bit more technical, here’s how Verisign breaks down the novelty of its invention:
Selecting appropriate affixes is a complex technical task that involves using natural language processing (NLP) to determine affixes that are semantically and syntactically appropriate based on the input string.
Traditional language models rely heavily on examples in the training set to assign values to the affixes relative to the input string. For example, a value can be assigned to an affix by dividing the number of occurrences of the input string with the affix in the training set by the total number of occurrences of the input string in the training set (e.g., maximum likelihood estimates). However, because traditional language models lack the technical ability to smooth the language model, the systems may not be able to determine a value if there are no occurrences of the input string with the affix in the training set.
Accordingly, in some embodiments, the domain name suggestion system can be configured to use a neural network language model (e.g., a feed-forward neural network language model) with one or multiple non-linear hidden layers or a log-linear language model that learns a continuous distributed representation of words and multi-word expressions. Thus, such domain name suggestion systems have the technical ability to efficiently process the training set to generate a smooth language model and return more accurate results. Therefore, the use of a neural network language model with one or more multiple non-linear hidden layers or a log-linear language model are technical improvements to the operations of domain name suggestion systems.
Additionally, using a smoothed language model allows the use of a domain name system (DNS) zone file as a training set. A zone file is useful as a training set for domain name suggestion because a zone file contains already registered domain names, which can be effective indications of the semantics and syntax of desirable domain names.
However, a zone file may only include registered domain names, and domain name suggestion systems should suggest domain names that are not registered. Accordingly, values can be assigned to affix/input string combinations that do not occur in the zone file. Thus, without using a smooth language model, it may be difficult to assign values to affix/input string combinations because there may be no occurrences of the affix/input string combinations in the zone file.
Therefore, a domain name suggestion system that use a neural network language model with one or multiple non-linear hidden layers or a log-linear language model that learns a continuous representation of words and multi-word expressions (a smoothed language model) allows the use of a zone file notwithstanding the very low amount of context that the zone file provides.
I understand why, for marketing purposes, companies usually just say they’ve applied AI to make things better.
Verisign applied for the patent in 2016, and it was granted today.
The company has patented other aspects of suggesting relevant domain names to users.