The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 03, 2022

Filed:

Apr. 20, 2021
Applicant:

Sharecare Ai, Inc., Palo Alto, CA (US);

Inventors:

Axel Sly, Palo Alto, CA (US);

Srivatsa Akshay Sharma, Santa Clara, CA (US);

Brett Robert Redinger, Oakland, CA (US);

Devin Daniel Reich, Olympia, WA (US);

Geert Trooskens, Meise, BE;

Meelis Lootus, London, GB;

Young Jin Lee, Vancouver, CA;

Ricardo Lopez Arredondo, Schertz, TX (US);

Frederick Franklin Kautz, IV, Fremont, CA (US);

Satish Srinivasan Bhat, Fremont, CA (US);

Scott Michael Kirk, Belmont, CA (US);

Walter Adolf De Brouwer, Los Altos Hills, CA (US);

Kartik Thakore, Santa Clara, CA (US);

Assignee:

SHARECARE AI, INC., Palo Alto, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06F 21/45 (2013.01); G06N 20/00 (2019.01); H04L 9/32 (2006.01); G16H 10/60 (2018.01); G06F 21/32 (2013.01); G06K 9/62 (2022.01); G06K 7/14 (2006.01); G06N 5/04 (2006.01); H04L 9/08 (2006.01); H04L 29/06 (2006.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06F 21/45 (2013.01); G06F 21/32 (2013.01); G06K 7/1417 (2013.01); G06K 9/00892 (2013.01); G06K 9/6256 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G16H 10/60 (2018.01); H04L 9/085 (2013.01); H04L 9/0841 (2013.01); H04L 9/0894 (2013.01); H04L 9/3228 (2013.01); H04L 9/3231 (2013.01); H04L 9/3236 (2013.01); H04L 9/3239 (2013.01); H04L 9/3247 (2013.01); H04L 9/3297 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); H04L 63/0861 (2013.01);
Abstract

The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.


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