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:
Nov. 04, 2025

Filed:

Mar. 03, 2023
Applicants:

Farnaz Hajiahmadifoomani, Boulder, CO (US);

Ziling Zhang, Boulder, CO (US);

Scott R. Johnson, Erie, CO (US);

William Edward Manchester, Erie, CO (US);

Inventors:

Farnaz Hajiahmadifoomani, Boulder, CO (US);

Ziling Zhang, Boulder, CO (US);

Scott R. Johnson, Erie, CO (US);

William Edward Manchester, Erie, CO (US);

Assignee:

Ricoh Company, Ltd., Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B41J 2/165 (2006.01); B41J 2/045 (2006.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
B41J 2/16579 (2013.01); B41J 2/0451 (2013.01); G06N 3/08 (2013.01);
Abstract

Systems and methods of recommending replacement of printheads. In an embodiment, a system trains a first neural network to generate anomaly scores for printheads using an unsupervised learning algorithm based on first training samples of conforming printhead data from a pool of conforming printheads. The system generates a training dataset for a recurrent second neural network by identifying training printhead data for a pool of training printheads, inputting second training samples of the training printhead data into the first neural network to generate training anomaly scores for the training printheads over a plurality of time units, and formatting third training samples for the training printheads. The system trains the recurrent second neural network to generate scaled anomaly scores for printheads using a supervised learning algorithm based on the second training dataset.


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