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:
Mar. 28, 2023

Filed:

Oct. 25, 2021
Applicant:

Q Bio, Inc., San Carlos, CA (US);

Inventors:

Guanhua Wang, San Carlos, CA (US);

Matteo Alessandro Francavilla, San Carlos, CA (US);

Thomas Witzel, San Mateo, CA (US);

Jeffrey H. Kaditz, Wilson, WY (US);

Assignee:

Q Bio, Inc., San Carlos, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G01R 33/56 (2006.01); G01R 33/561 (2006.01); G06T 7/00 (2017.01); A61B 5/055 (2006.01);
U.S. Cl.
CPC ...
G01R 33/5608 (2013.01); A61B 5/055 (2013.01); G01R 33/5611 (2013.01); G06T 7/0012 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01);
Abstract

A computer system that performs a sparsity technique is described. During operation, the computer system accesses or obtains information associated with non-invasive measurements performed on at least an individual, historical non-invasive measurements, and a dictionary of predetermined features or basis functions associated with the historical non-invasive measurements. Note that the non-invasive measurements and the historical non-invasive measurements may include or correspond to magnetic resonance (MR) measurements. For example, the MR measurements may include magnetic resonance imaging (MRI) scans. Then, the computer system updates the dictionary of predetermined features based at least in part on the non-invasive measurements and the historical non-invasive measurements, where the updating includes performing a minimization technique with a cost function having an L2-norm term and an L0-norm term. Next, the computer system determines weights associated with features in the updated dictionary of predetermined features based at least in part on the non-invasive measurements.


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