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:
Oct. 29, 2024

Filed:

Mar. 04, 2021
Applicant:

Unitedhealth Group Incorporated, Minnetonka, MN (US);

Inventors:

Russell H. Amundson, Merion Station, PA (US);

Saurabh Bhargava, Eden Prairie, MN (US);

Rama Krishna Singh, Greater Noida, IN;

Ravi Pande, Noida, IN;

Vishwakant Gupta, Noida, IN;

Gaurav Mantri, Gurugram, IN;

Abhinav Agrawal, Gulabpura, IN;

Sapeksh Suman, Greater Noida West, IN;

Assignee:

UnitedHealth Group Incorporated, Minnetonka, MN (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06F 18/2137 (2023.01); G06F 18/24 (2023.01); G06T 3/60 (2024.01); G06T 7/11 (2017.01); G06T 7/33 (2017.01); G06T 7/70 (2017.01); G06T 7/90 (2017.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G16H 30/40 (2018.01); G16H 40/67 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G06F 18/21375 (2023.01); G06F 18/24 (2023.01); G06T 3/60 (2013.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/337 (2017.01); G06T 7/70 (2017.01); G06T 7/90 (2017.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G16H 30/40 (2018.01); G16H 40/67 (2018.01); G06T 2207/10024 (2013.01); G06T 2207/20024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30012 (2013.01); G06T 2207/30068 (2013.01); G06T 2207/30088 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.


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