they're patenting a specific method of doing so.
There is nothing specific about the methods they're patenting. I just worked on a very similar project, and after reading the patent, I see very little separating what they patented from what we did. Indeed we don't use dimensionality reduction the way they suggest (although we did use it for a while), and we don't provide specific names for the objects we discover (though we have talked about doing so via crowdsourcing). Indeed our work is more recent than the patent filing, but people have been attempting similar things for ages (e.g. [1], [2]...they are very easy to find). Worse, the two papers I cite provide enough detail to actually produce a working system, whereas the patent provides little detail beyond a few references to well-known machine learning and computer vision techniques. And even when they suggest methodology, it's always "maybe we'll use this, maybe not", and further they tend to list several potential methods without any indication that they've researched which ones work.
The reason for this is that the more students they fail, the better they look.
This is also incorrect. Far more important in the school's rankings are (a) the percent of their admitted class to accept the admissions offer, and (b) a higher number of students who get job offers after graduating. This incentivizes schools to lower failure rates (US News and World Report reports graduation rates and rolls them into rankings because they know it turns off most prospective students), and also to increase grades to make their students' resumes look better.
"When the going gets tough, the tough get empirical." -- Jon Carroll