Robert A Michaels

US Patent:

39956660, Dec 7, 1976

Filed:

Dec 24, 1975

Appl. No.:

5/644347

Inventors:

Robert E. Michaels - Addison IL

Assignee:

VCA Corporation - Baton Rouge LA

International Classification:

B65B 3100

US Classification:

141 3

Abstract:

A method for rapidly filling an aerosol spray dispenser containing a liquid with a soluble gas propellant including lifting the cap off the container and injecting the propellant into the container at a velocity sufficient to substantially saturate the liquid with the propellant.

US Patent:

44120966, Oct 25, 1983

Filed:

Dec 24, 1980

Appl. No.:

6/219896

Inventors:

Bradly J. Edgerton - Pico Rivera CA
William F. House - Whittier CA
Robert C. Michaels - Santa Barbara CA

Assignee:

Minnesota Mining and Manufacturing Company - St. Paul MN

International Classification:

H04R 2502

US Classification:

381 60

Abstract:

An acoustically sealed earmold and a receiver adapter insertable into the ear of an individual with impaired hearing and used in conjunction with an external receiver to evaluate hearing. A unique method of coupling the receiver adapter to the earmold is also disclosed. The adapter is inserted into the molded portion of the earmold and provides a straight passageway for transmission of sound energy from the receiver to the occluded space of the external canal of the ear. The device substantially reduces the problem of acoustic feedback in high gain hearing aid fittings and provides more accurate transmission of acoustic signals to allow improved hearing evaluation. This invention enables an audiologist to fit the earmold and conduct hearing aid evaluations in a single office visit.

US Patent:

20200193382, Jun 18, 2020

Filed:

Dec 17, 2018

Appl. No.:

16/222420

Inventors:

Robert P. Michaels - Pacific Palisades CA, US

International Classification:

G06Q 10/10
G06N 20/00

Abstract:

A system, method and apparatus for semantically evaluating and matching resumes with job descriptions, and vice versa, is presented. The system may include learned word embeddings and/or learned phrase embeddings and trained models to evaluate the context and meaning of the text within each document. The system may rate the relevance of each document with respect to other documents, and to use the context and meaning to find the most relevant matches between the documents.