Michael P Greene

US Patent:

59269561, Jul 27, 1999

Filed:

Feb 5, 1998

Appl. No.:

9/019335

Inventors:

Kirk Langman - Los Angeles CA
Michael Greene - Sherman Oaks CA
Kenneth Tarlow - Corte Madera CA

Assignee:

Kirk Langman - Los Angeles CA

International Classification:

A45D 2902

US Classification:

30 28

Abstract:

A combined toe and finger nail clipper having a common metal strip with a toe nail cutting edge at one end and a finger nail cutting edge at the opposite end. Mating nail cutting edges are at the ends of two additional metal strips which are each welded above and below at opposite ends of the common metal strip. A pair of vertical retaining posts located at each end of the nail clipping unit hold lever arms which can be stored in the flat position or rotated around and put into the use position as in conventional nail clippers. In this way a manufacturer can produce a combined toe and finger nail clipper more economically than two separate nail clippers and the user can store the combined nail clipper in a smaller space without worrying about locating two separate clippers. The clipper can be constructed so that while one of the clippers is in use the cutting edges of its other are closed or only slightly open so as to avoid the corners of the cutting edges catching or scratching a finger of the user.

US Patent:

20140200930, Jul 17, 2014

Filed:

Mar 17, 2014

Appl. No.:

14/217231

Inventors:

- Hermitage TN, US
Cheng-Sheng Peter Wu - Arcadia CA, US
Michael F. Greene - Boston MA, US
James C. Guszcza - Santa Monica CA, US
Jun Yan - Avon CT, US
Jonathan Vanden Bosch - Santa Monica CT, US
John R. Lucker - Simsbury CT, US

International Classification:

G06Q 40/08

US Classification:

705 4

Abstract:

Methods and systems for determining the importance of each of the variables, or combinations of variables, that contribute to the overall score generated by a predictive statistical model are presented. In a specialized case, for each variable in the model, an importance is calculated based on the calculated slope and deviance of the predictive variable. In a more general case, for each variable in the model, an importance is calculated based on setting that variable to have the average value for the data set, and then calculating the change in score. The totality of variables (or combinations thereof) is then ranked by the Δscore, or a magnitude of it, such as |Δscore|.