The patent is an interesting read as it gives a summary the history of different attempts at quantifying loudspeakers (it assumes an understanding of speaker measurements and terms).

From a Speaker Manufacture's point of view, I think, there is only a down side in publishing this number.
Even if your speaker is at the high-end of the scale (unless you have the highest rating one can get) you take the chance that someone could publish one that's higher.
People being human would not take the +/- 1 as being equal ... it would be argued that a higher number is always the better speaker.

Another question of concern: could a manufacture design a speaker to maximize this number without really producing something superior?
Since I only skimmed the patent with the goal on understanding the figures presented and a bit of the math, I only have the gist of this method, In the end, I focused in on figures 5 and 6. I'm still not sure if my understanding of figure 5 (based on an anechoic model developed) & figure 6 (based on a generalized anechoic model developed) is correct. I'm kind of guessing fig #5 is the sampling used (13 speakers) to develop the algorithm and #6 is the sampling used (36 speakers) to test it's predictive measure. If that's the case, overall the blind listen tests correlated with the predictions but it's definitely far from perfect (sometimes it's a lot further off then +/- 1).

I also think there are better ways of coming up with a predictive measure then the linear regression presented ... Easiest would be to apply some pre-packaged AI algorithms . Although, If the sample size presented here is the full dataset (and not just for patent clarity) there might not be enough to train with (on the other hand given 30 independent variables there is a lot to work with). If it's not enough I might consider a few other methods to replace the linear one above. Either way it would probably improve the correlation and depending take a lot of the human tweaking (weighting) out of the equation.

If this method has merit, I think it would only really be useful for manufactures to predict if their speaker would do well in a listening test.
In which case, I'm not sure how it could be monetized as there's nothing to keep a manufacturer from using it as a internal measure (especially if they didn't advertise the fact).
Maybe someone else can fill in what I'm missing here ...