The DNLE core technology is significantly different than current search engine technology in use today:
Although search engines (i.e. word indexing applications) can efficiently store the words in a document or web page to a database, and provide a mechanism to search for documents that have specific words, they don't understand the meaning of the words (and sentences) they are storing.
For example, a search engine can certainly index a document with the words: the quick brown fox jumped over the lazy dog, but it won't understand that the sentence is about animals. DNLE will index the words of the document, but it also understands that the sentence is about two animals, that the animals are vertebrates, that they are, in fact, mammals, that the sentence involves the motion of one animal moving over the other, and so on. This content signature is digitally stored in the DNLE knowledgebase, and allows DNLE to search for and match documents based on meaning not just individual words. (i.e. DNLE will return this document as a match when searching for: the agile dark-colored mammal leaped over the slothful vertebrate).
DNLE also has the uncanny ability to understand which words and phrases are most important in a document, and can give the important parts of a document more weight when searching for matches, thus enabling DNLE to provide the results that are most relevant. (or, even better, the single document that is best.)
Computers are difficult to use because they are very good at processing digital information (e.g. numbers) and not very good at understanding the complexities and subtleties of human language.
For example, if you type a social security number into a form and click 'OK', your computer will go find it. But, if you ask your computer: My car is making an unusual sound. Could you please tell me what's wrong with it?, your computer - of course - won't provide any useful information. (or, if you use a search engine, it might provide a lot of information, but very little of it will be useful.)
But, if we have a special computer that can understand our language, and our computer is very patient and listens quietly to an automobile mechanic as he has conversations with his customers, and the conversations can be stored - digitally - as two-part documents: a multi-threaded context document and a response, then, at some point, the computer would be able to provide intelligent answers and - more importantly - after a lot of listening, the computer would be able to carry on a meaningful conversation.
DNLE is important because it provides the essential language engine that (1) understands the meaning of a collection of words and can efficiently store a digital content signature of that meaning, and (2) given a search document, can quickly find the single most similar document (based on meaning) in a large-scale document library.