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:
Apr. 28, 2026

Filed:

May. 12, 2023
Applicant:

Shoreline Iot, Inc., Campbell, CA (US);

Inventors:

Mark Stubbs, Felton, CA (US);

Sameer Bidichandani, Los Gatos, CA (US);

Kabir Manghnani, Saratoga, CA (US);

Assignee:

q, Campbell, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/04 (2023.01); G06N 20/00 (2019.01); G16Y 10/75 (2020.01); G16Y 20/20 (2020.01); H04L 67/10 (2022.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06N 20/00 (2019.01); G16Y 10/75 (2020.01); G16Y 20/20 (2020.01); H04L 67/10 (2013.01);
Abstract

The invention is generally directed to systems and methods of monitoring or predicting a service event for an industrial asset using an artificial intelligence of things (AIoT) system including an AIoT device, AIoT cloud, and a self-learning AI classification and analytics engine. The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration. Classification may be based on narrow-band analysis and/or machine learning models. If an anomaly is detected power may be provided to a communication module to send sensor data to the signal processor for classification and/or further processing. Classifications or determinations made by the signal processor or detected through a work-order system may be used to automatically retrain the inference model on the edge, so that the system is self-learning.


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