Kenneth Ross Currie

US Patent:

7028723, Apr 18, 2006

Filed:

Nov 2, 2004

Appl. No.:

10/979474

Inventors:

Ali Tahor Alouani - Cookville TN, US
Kenneth R. Currie - Cookeville TN, US

International Classification:

B65B 1/04

US Classification:

141 83, 141 94, 702128

Abstract:

An apparatus and method are used to perform the verification of a pharmaceutical prescription after it is filled but prior to shipment. Due to the high volume of mail/internet orders, the analysis is to be performed on the whole prescription, not on each individual pill of the prescription. The apparatus automatically gathers information about the pills contained in a pill bottle, including image and weight information, and incorporates a decision-making engine to decide whether the content of the pill bottle matches the prescription order.

US Patent:

54615593, Oct 24, 1995

Filed:

Oct 4, 1993

Appl. No.:

8/131536

Inventors:

Jeffrey J. Heyob - Beavercreek OH
Oliver D. Patterson - Beavercreek OH
Steven R. LeClair - Spring Valley OH
T. Walter Haas - Kettering OH
Kenneth Currie - Cookeville TN
Doug Moore - Okeana OH
Stephen J. Adams - Dayton OH
Victor Hunt - Cincinnati OH

Assignee:

The United States of America as represented by the Secretary of the Air
Force - Washington DC

International Classification:

G05B 1304

US Classification:

364149

Abstract:

A multi-featured control system which improves the manufacturing capability of the thin-film semiconductor growth process. This system improves repeatability and accuracy of the process, reduces the manpower requirements to operate MBE, and improves the MBE environment for scientific investigation. This system has three levels of feedback control. The first level improves the precision and tracking of the process variables, flux, and substrate temperature. The second level comprises an expert system that uses sensors to monitor the status of the product in order to tailor the process plan in real time so that the exact qualities desired are achieved. The third level features a continuously evolving neural network model of the process which is used to recommend the recipe and command inputs to achieve a desired goal. The third level is particularly useful during the development process for new materials. All three levels require models of the process which are updated during automatic process identification experiments.