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:
Oct. 10, 2023

Filed:

Aug. 07, 2020
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Wei Cheng, Princeton Junction, NJ (US);

Haifeng Chen, West Windsor, NJ (US);

Jingchao Ni, Princeton, NJ (US);

Dongkuan Xu, State College, PA (US);

Wenchao Yu, Plainsboro, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 17/18 (2006.01); G06N 5/04 (2023.01); G06F 18/214 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 17/18 (2013.01); G06F 18/214 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 5/04 (2013.01);
Abstract

A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.


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