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:
Jul. 15, 2025

Filed:

Jul. 25, 2024
Applicant:

State Farm Mutual Automobile Insurance Company, Bloomington, IL (US);

Inventors:

Nathan L. Tofte, Downs, IL (US);

Jonathan Christopher Hull, Bloomington, IL (US);

John R. Donovan, Bloomington, IL (US);

Richard Jan Tjaden, Bloomington, IL (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
F04D 15/02 (2006.01); F04D 13/08 (2006.01); F04D 13/12 (2006.01); F04D 15/00 (2006.01); G01M 1/22 (2006.01); G01M 13/045 (2019.01); G05B 13/02 (2006.01); G05B 15/02 (2006.01); G05D 9/12 (2006.01); G06F 3/0481 (2022.01); G06F 3/04842 (2022.01); G06N 20/00 (2019.01); G08B 21/18 (2006.01); H04L 12/28 (2006.01);
U.S. Cl.
CPC ...
F04D 15/0227 (2013.01); F04D 13/08 (2013.01); F04D 13/086 (2013.01); F04D 13/12 (2013.01); F04D 15/0077 (2013.01); F04D 15/0088 (2013.01); F04D 15/0209 (2013.01); F04D 15/0218 (2013.01); F04D 15/0254 (2013.01); F04D 15/0272 (2013.01); G05B 13/021 (2013.01); G05B 13/026 (2013.01); G05B 13/0265 (2013.01); G05B 15/02 (2013.01); G05D 9/12 (2013.01); G06F 3/0481 (2013.01); G06F 3/04842 (2013.01); G06N 20/00 (2019.01); G08B 21/182 (2013.01); H04L 12/282 (2013.01); H04L 12/2823 (2013.01); G01M 1/22 (2013.01); G01M 13/045 (2013.01);
Abstract

A sump pump system may implement adaptive learning and machine learning techniques to facilitate improved control of sump pumps. A sump pump system may implement the described techniques to generate, train, and/or implement a machine learning model that is capable of predicting or estimating one or more conditions of the sump pump system (e.g., water level in the basin, motor malfunction, stuck impeller, geyser effect, blocked outlet pipe, faulty level sensor/switch, faulty bearing, failure to engage pump at high-water mark, etc.) based on one or more detected input variables (e.g., acceleration or vibration patterns detected in water, on a pump, or on a pipe; capacitance values of water; audio signatures; electrical signatures, such as power or current draw; pump motor rotation speed; water pressure signatures or values, such as those detected at the bottom of a sump basin; etc.).


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