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:
May. 13, 2025

Filed:

Jan. 11, 2022
Applicant:

Celona, Inc., Cupertino, CA (US);

Inventors:

Preethi Natarajan, San Diego, CA (US);

Mehmet Yavuz, Palo Alto, CA (US);

Assignee:

CELONA, INC., Campbell, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H04W 24/02 (2009.01); H04W 28/16 (2009.01);
U.S. Cl.
CPC ...
H04W 24/02 (2013.01); H04W 28/16 (2013.01);
Abstract

Method and apparatus for scalable machine learning (ML)-based frameworks for resource planning and recommendations for Enterprise Networks are disclosed. In some embodiments, a method for determining network health assessment and providing network planning and recommendations in wireless enterprise networks is provided. The method quantifies resource health of a plurality of network elements operating within the enterprise network by calculating a plurality of distance metrics between acceptable health parameters and observed network resource utilization values associated and corresponding to each selected network element. The method ranks each of the network elements based upon the distance metrics calculated for each network element. Factors that negatively impact the resource health of the network elements are ranked in accordance to how severely they impact the performance of the network elements. The method and apparatus provide suggestions and recommendations to improve network performance of the network elements. An apparatus for scalable machine-learning (ML)-based frameworks for resource planning in enterprise networks is also disclosed.


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