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. 11, 2025

Filed:

May. 29, 2020
Applicant:

Iron Mountain Incorporated, Boston, MA (US);

Inventors:

Zhihong Zeng, Acton, MA (US);

Anwar Chaudhry, Mississauga, CA;

Rajesh Chandrasekhar, Franklin, TN (US);

Adam Darius Williams, Harleysville, PA (US);

Utpal N. Gandhi, Wilmington, MA (US);

Assignee:

Iron Mountain Incorporated, Boston, MA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 16/35 (2019.01); G06F 16/355 (2025.01); G06V 10/762 (2022.01); G06V 10/82 (2022.01); G06V 30/10 (2022.01); G06V 30/19 (2022.01); G06V 30/40 (2022.01); G06V 30/413 (2022.01); G06V 30/416 (2022.01);
U.S. Cl.
CPC ...
G06F 16/355 (2019.01); G06V 10/763 (2022.01); G06V 10/82 (2022.01); G06V 30/19127 (2022.01); G06V 30/40 (2022.01); G06V 30/413 (2022.01); G06V 30/416 (2022.01); G06V 30/10 (2022.01);
Abstract

A computer-implemented method, includes accessing, by a processor, a set of asset documents. The method also includes performing, by the processor, feature extraction on text of each document of the set of asset documents using a feature extraction module to generate a set of features, where each feature of the set of features represents a document of the set of asset documents. The method also includes generating, by the processor, a set of lower-dimensional features from the set of features using a singular value decomposition module. The method also includes generating, by the processor, a set of clusters from the set of lower-dimensional features using a clustering module. The method also includes training, by the processor, a machine-learning model of a classification microservice using the set of clusters generated from the clustering module.


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