My summary after a quick flick through is that it is a better classification(/clustering?) model for text, because it takes Word2Vec-style similarity into account, which plain LDA doesn't. That sounds like a reasonable approach to me, and nice to see someone get it working.
I think. Comments?
Here is the version with notes. I haven't read this through yet: http://www.slideshare.net/ChristopherMoody3/word2vec-lda-and...
Code here, BTW: https://github.com/cemoody/lda2vec
Ultimately, the goal is to use all of the information that is usually available alongside text. Word2vec treats this text like one long string. LDA has the notion of documents. But lda2vec can use more features (for example) the zip code a client comment might come from (and so you get regional topics, like outer wear in Vermont or cowboy boots in Texas) the client ID a comment comes from (so you get that a client might be a sporty client, or a expecting mother) in addition to document-level topics (which might surface customer comments like "perfect service!" or package delivery problems). Those topics are readily consumed by analysts and can be used to understand the business from the client's perspective; word2vec on the other hand produces representations that are hard for anything but machines to consume.
Few people seem to agree with you, and whilst there certainly are similarities it looks to me like there are more differences.
I understand that you think your patent is being ignored, but I don't think commenting everywhere that mentioned Word2Vec is going to help you.