Heinous Patent

[Paul Viola]

    From: Vision-List moderator Phil Kahn <Vision-List-Request at teleos.com>

    VISION-LIST Digest    Tue Feb 22 11:26:42 PDT 94     Volume 13 : Issue 8

    Date: Thu, 17 Feb 1994 22:23:00 GMT
    From: eledavis at ubvms.cc.buffalo.edu (Elliot Davis)
    Organization: University at Buffalo
    Subject: Error Reduction

    I would greatly appreciate your thoughts on the:

			ERROR TEMPLATE TECHNIQUE

    The "Error Template" technique (patent 4,802,231) provides an
    alternative method for reducing false alarms in pattern recognition
    systems. In this approach, a pattern representing a mismatched
    pattern is stored in the reference lexicon. It is a reference
    pattern to an error rather then to what is desired. THIS IS DONE
    WITH THE EXPECTATION THAT IF THE ERROR PATTERN OR A VARIATION OF IT
    IS REPEATED IT WILL TEND TO BE CLOSER TO ITSELF THEN TO THE PATTERN
    THAT IT FALSED OUT TO. 

    ...

Unless this patent is very old, I find it terrifying.  It is a concept
that is clearly part of the pattern recognition literature of the
70's.  Essentially pattern classification works by finding clusters
that represent classes.  These clusters along with a measurement model
define a probability density over the pattern space.  All this
technique is doing is adding an additional cluster which represents a
particular type of measurement error sensing a class.  Pattern
classification theory tells us that this should be done whenever there
is a particular measurement error that is not modeled well by our
measurement model.  You add a cluster when the distribution of data is
different from the probability density predicted by the model -- i.e.
a particular measurement error is more common than your model
predicts.  You can add these clusters by hand, as the patent suggests,
or you can let a density estimation scheme discover them for you (a
mixture of gaussians model trained with EM works nicely).  End of
story.

So remember, anytime someone adds another cluster to a pattern
classification model, they owe the owner of this patent money.

I wonder what the date of this fine patent is??

Paul Viola