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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.

Patent No.:

US 11657418 B1

PDFFull Text
Date of Patent:
May. 23, 2023

Filed:

Mar. 05, 2021

Capacity optimized electronic model based prediction of changing physical hazards and inventory items

Applicant:

Yembo, Inc., San Diego, CA (US);

Inventors:

Devin Waltman, San Diego, CA (US);

Siddharth Mohan, San Diego, CA (US);

Anoop Jakka, San Diego, CA (US);

Marc Eder, San Diego, CA (US);

Maciej Halber, San Diego, CA (US);

Zachary Rattner, San Diego, CA (US);

Assignee:

YEMBO, INC., San Diego, CA (US);

Attorney:

Pillsbury Winthrop Shaw Pittman LLP

Primary Examiner:

Scott C Anderson

Int. Cl.
CPC ...
G06Q 30/02 (2023.01); G06Q 30/0204 (2023.01); G06Q 40/08 (2012.01); G06F 16/29 (2019.01); G06N 20/00 (2019.01); G06Q 10/0875 (2023.01); G06Q 10/0639 (2023.01); G06T 7/00 (2017.01); G06Q 30/0201 (2023.01); G06N 3/08 (2023.01); G06T 17/00 (2006.01); G06T 7/80 (2017.01); G06T 7/50 (2017.01); G06F 3/04842 (2022.01); G06T 7/20 (2017.01); G06T 17/20 (2006.01); G06V 20/00 (2022.01); G06N 3/044 (2023.01); H04N 23/63 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/64 (2022.01); G01S 17/06 (2006.01); G06Q 50/16 (2012.01); G01S 19/42 (2010.01); G06F 18/24 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0205 (2013.01); G06F 3/04842 (2013.01); G06F 16/29 (2019.01); G06N 3/044 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 10/06395 (2013.01); G06Q 10/0875 (2013.01); G06Q 30/0206 (2013.01); G06Q 40/08 (2013.01); G06T 7/0002 (2013.01); G06T 7/20 (2013.01); G06T 7/50 (2017.01); G06T 7/80 (2017.01); G06T 17/00 (2013.01); G06T 17/20 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/00 (2022.01); G06V 20/64 (2022.01); H04N 23/631 (2023.01); G01S 17/06 (2013.01); G01S 19/42 (2013.01); G06F 18/24 (2023.01); G06Q 50/16 (2013.01); G06T 2207/20081 (2013.01);
Abstract

The process of obtaining an insurance quote is currently based on information provided by filing online forms. Insurance companies use the consumer input data from online the forms to provide a quote for property insurance and for insurance underwriting. To complete the underwriting, the insurance companies have to make assumptions on various factors; for example, the structure and layout of a consumer's home, what type of content the consumer owns, its value, and its condition, among several others. It is desirable to get more accurate data on the consumer content, the structure and layout of the home, as well as other factors (e.g., hazards such as whether a house is on a canyon) in an automated fashion which would let insurance companies underwrite policies with a more accurate risk profile as well as save costs by not having to send an onsite inspector.

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