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. 17, 2026

Filed:

Nov. 08, 2024
Applicant:

Plume Design, Inc., Palo Alto, CA (US);

Inventors:

Richard Chang, Los Altos Hills, CA (US);

Miroslav Samardzija, Mountain View, CA (US);

Xiaowei Qian, Suzhou, CN;

Mingshu Liu, Hsinchu City, TW;

Shu-Hsuan Yang, Taoyuan City, TW;

Shruti Royyuru, Mountain View, CA (US);

Dinh Hieu Liem Vo, San Jose, CA (US);

Jianshing Li, Campbell, CA (US);

Ha Do, Cumming, GA (US);

Wei-Jen Lin, Hsinchu City, TW;

Te-Wei Ho, Zhubei City, TW;

Assignee:

PLUME DESIGN, INC., Palo Alto, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/177 (2006.01); H04L 41/16 (2022.01); H04L 43/08 (2022.01); H04W 64/00 (2009.01);
U.S. Cl.
CPC ...
H04L 43/08 (2013.01); H04L 41/16 (2013.01); H04W 64/003 (2013.01);
Abstract

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and/or dynamically provides mechanisms for managing, optimizing and configuring a WiFi network at a location. The framework provides network management utilizing edge processing capabilities to bridge local WiFi and cloud systems. The framework implements comprehensive device typing through multi-layered analysis combining passive monitoring, deep packet inspection and hybrid deterministic-probabilistic classification methods. State synchronization between local and cloud networks can be achieved through hierarchical data modeling and differential synchronization algorithms. The framework can implement advanced features that include automated channel optimization, QoS management, and security monitoring. The framework incorporates self-healing capabilities using reinforcement learning techniques and maintains operational efficiency through intelligent resource management and workload distribution. The framework can operate autonomously while requiring minimal cloud connectivity, featuring extensible architecture through a plugin system that enables adaptation to evolving network requirements while maintaining stable operation of existing capabilities.


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