公开(公告)号：US20240303149A1

公开(公告)日：2024-09-12

申请号：US18599322

申请日：2024-03-08

Applicant: NEC Laboratories America, Inc.

Inventor： Yuncong Chen ,  Haifeng Chen ,  LuAn Tang ,  Zhengzhang Chen

IPC: G06F11/07 ,  G16H50/30

CPC classification number: G06F11/079 ,  G06F11/0721 ,  G16H50/30

Abstract: Methods and systems for anomaly detection include encoding a time series with a time series encoder and encoding an event sequence with an event sequence encoder. A latent code is generated from outputs of the time series encoder and the event sequence encoder. The time series is reconstructed from the latent code using a time series decoder. The event sequence is reconstructed from the latent code using an event sequence decoder. An anomaly score is determined based on a reconstruction loss of the reconstructed time series and a reconstruction loss of the reconstructed event sequence. An action is performed responsive to the anomaly score.

公开(公告)号：US20240214414A1

公开(公告)日：2024-06-27

申请号：US18359389

申请日：2023-07-26

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Dongjie Wang

IPC: H04L9/40 ,  H04L41/0631 ,  H04L41/16

CPC classification number: H04L63/145 ,  H04L41/0631 ,  H04L41/16

Abstract: A computer-implemented method for identifying attack origins is provided. The method includes detecting a trigger point from entity metrics data and key performance indicator (KPI) data, generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph, backtracking from an attack detection point, via an incident backtrack and system recovery component, by using the learned causal graph to identify an attack origin when an intrusion or attack occurs, and displaying data relating to the attack origin on a visualization display for user analysis.

公开(公告)号：US20240135188A1

公开(公告)日：2024-04-25

申请号：US18545055

申请日：2023-12-19

Applicant: NEC Laboratories America, Inc.

Inventor： Liang Tong ,  Takehiko Mizoguchi ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Nauman Ahad

IPC: G06N3/0895 ,  G06N3/0442

CPC classification number: G06N3/0895 ,  G06N3/0442

Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k-1 binary classifiers on top of the semi-supervised representations to obtain k-1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k-1 binary predictions by matching the inconsistent ones to consistent ones of the k-1 binary predictions. The method further includes aggregating the k-1 binary predictions to obtain an ordinal prediction.

公开(公告)号：US20240134736A1

公开(公告)日：2024-04-25

申请号：US18493374

申请日：2023-10-23

Applicant: NEC Laboratories America, Inc.

Inventor： Yuncong Chen ,  LuAn Tang ,  Yanchi Liu ,  Zhengzhang Chen ,  Haifeng Chen

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0709 ,  G06F11/0793 ,  G16H50/20

Abstract: Methods and systems for anomaly detection include encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector. Anomaly detection is performed using the feature vector to identify an anomaly within a system. A corrective action is performed responsive to the anomaly to correct or mitigate an effect of the anomaly. The detected anomaly can be used in a healthcare context to support decision making by medical professionals with respect to the treatment of a patient. The encoding may include machine learning models to implement the transformers and the aggregation network using deep learning.

公开(公告)号：US20240127072A1

公开(公告)日：2024-04-18

申请号：US18545025

申请日：2023-12-19

Applicant: NEC Laboratories America, Inc.

Inventor： Liang Tong ,  Takehiko Mizoguchi ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Nauman Ahad

IPC: G06N3/0895 ,  G06N3/0442

CPC classification number: G06N3/0895 ,  G06N3/0442

Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k−1 binary classifiers on top of the semi-supervised representations to obtain k−1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k−1 binary predictions by matching the inconsistent ones to consistent ones of the k−1 binary predictions. The method further includes aggregating the k−1 binary predictions to obtain an ordinal prediction.

公开(公告)号：US20240061739A1

公开(公告)日：2024-02-22

申请号：US18359309

申请日：2023-07-26

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Liang Tong ,  Dongjie Wang

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0709

Abstract: A computer-implemented method for identifying root cause failure and fault events is provided. The method includes detecting a trigger point, converting, via an encoder, previous system state data, new batch data in a next system state, and a causal graph to system state-invariant embeddings and system state-dependent embeddings, generating a learned causal graph, via a graph generation layer, by integrating state-invariant and state-dependent information, and predicting, by a prediction layer, future time-series data on the learned causal graph.

公开(公告)号：US11782812B2

公开(公告)日：2023-10-10

申请号：US17491632

申请日：2021-10-01

Applicant: NEC Laboratories America, Inc.

Inventor： Yuncong Chen ,  Zhengzhang Chen ,  Cristian Lumezanu ,  Masanao Natsumeda ,  Xiao Yu ,  Wei Cheng ,  Takehiko Mizoguchi ,  Haifeng Chen

IPC: G06F11/34 ,  G06N3/08 ,  G06N3/045

CPC classification number: G06F11/3476 ,  G06N3/045 ,  G06N3/08

Abstract: A method for system metric prediction and influential events identification by concurrently employing metric logs and event logs is presented. The method includes concurrently modeling multivariate metric series and individual events in event series by a multi-stream recurrent neural network (RNN) to improve prediction of future metrics, where the multi-stream RNN includes a series of RNNs, one RNN for each metric and one RNN for each event sequence and modeling causality relations between the multivariate metric series and the individual events in the event series by employing an attention mechanism to identify target events most responsible for fluctuations of one or more target metrics.

公开(公告)号：US11650351B2

公开(公告)日：2023-05-16

申请号：US17165515

申请日：2021-02-02

Applicant: NEC Laboratories America, Inc.

Inventor： Yanchi Liu ,  Jingchao Ni ,  Bo Zong ,  Haifeng Chen ,  Zhengzhang Chen ,  Wei Cheng ,  Denghui Zhang

IPC: G01W1/00 ,  G06N3/08 ,  G06N3/02 ,  G01W1/10 ,  G06N20/00 ,  G01W1/02 ,  G06N3/04 ,  G06N5/00 ,  G06N3/088

CPC classification number: G01W1/00 ,  G06N3/0454 ,  G06N3/08 ,  G01W1/02 ,  G01W1/10 ,  G01W2001/003 ,  G06N3/02 ,  G06N3/0445 ,  G06N3/088 ,  G06N5/003 ,  G06N20/00

Abstract: A method for employing a unified semi-supervised deep learning (DL) framework for turbulence forecasting is presented. The method includes extracting historical and forecasted weather features of a spatial region, calculating turbulence indexes to fill feature cubes, each feature cube representing a grid-based 3D region, and building an encoder-decoder framework based on convolutional long short-term memory (ConvLSTM) to model spatio-temporal correlations or patterns causing turbulence. The method further includes employing a dual label guessing component to dynamically integrate complementary signals from a turbulence forecasting network and a turbulence detection network to generate pseudo-labels, reweighing the generated pseudo-labels by a heuristic label quality detector based on KL-Divergence, applying a hybrid loss function to predict turbulence conditions, and generating a turbulence dataset including the predicted turbulence conditions.

公开(公告)号：US11620492B2

公开(公告)日：2023-04-04

申请号：US16998280

申请日：2020-08-20

Applicant: NEC Laboratories America, Inc.

Inventor： Jiaping Gui ,  Zhengzhang Chen ,  Junghwan Rhee ,  Haifeng Chen ,  Pengyang Wang

IPC: G06N3/04

Abstract: Systems and methods for predicting road conditions and traffic volume is provided. The method includes generating a graph of one or more road regions including a plurality of road intersections and a plurality of road segments, wherein the road intersections are represented as nodes and the road segments are represented as edges. The method can also include embedding the nodes from the graph into a node space, translating the edges of the graph into nodes of a line graph, and embedding the nodes of the line graph into the node space. The method can also include aligning the nodes from the line graph with the nodes from the graph, and optimizing the alignment, outputting a set of node and edge representations that predicts the traffic flow for each of the road segments and road intersections based on the optimized alignment of the nodes.

公开(公告)号：US20230069074A1

公开(公告)日：2023-03-02

申请号：US17888819

申请日：2022-08-16

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Jingchao Ni ,  Zheng Wang ,  Liang Tong

IPC: G06N3/08 ,  G06F11/34

Abstract: A method is provided for training a hierarchical graph neural network. The method includes using a time series generated by each of a plurality of nodes to train a graph neural network to generate a causal graph, and identifying interdependent causal networks that depict hierarchical causal links from low-level nodes to high-level nodes to the system key performance indicator (KPI). The method further includes simulating causal relations between entities by aggregating embeddings from neighbors in each layer, and generating output embeddings for entity metrics prediction and between-level aggregation.