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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 12045720 B1

PDFFull Text
Date of Patent:
Jul. 23, 2024

Filed:

Sep. 13, 2021

Systems and methods for distributed data analytics

Applicant:

Xailient, Sydney, AU;

Inventors:

Lars Oleson, Bondi, AU;

Shivanthan Yohanandan, Pakenham, AU;

Ryan Mccrea, Etobicoke, CA;

Deepa Lakshmi Chandrasekharan, Strathfiled, IN;

Sabina Pokhrel, Kathmandu, NP;

Yousef Rabi, Peakhurst, AU;

Zhenhua Zhang, Chatswood, AU;

Priyadharshini Devanand, Lidcombe, IN;

Bernardo Rodeiro Croll, Kingsford, AU;

James J. Meyer, Van Meter, IA (US);

Assignee:

XAILIENT, Sydney, AU;

Attorney:

Goodwin Procter LLP

Primary Examiner:

Wesley J Tucker

Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/28 (2023.01); G06F 18/40 (2023.01); G06V 10/46 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/94 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/28 (2023.01); G06F 18/40 (2023.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/945 (2022.01); G06V 40/16 (2022.01); G06T 2207/20084 (2013.01); G06V 10/462 (2022.01); G06V 40/161 (2022.01);
Abstract

The invention provides systems and method for generating device-specific artificial neural network (ANN) models for distribution across user devices. Sample datasets are collected from devices in a particular environment or use case and include predictions by device-specific ANN models executing the user devices. The received datasets are used with existing datasets and stored ANN models to generate updated device-specific ANN models from each of the stored instances of the device ANN models based on the training data.

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