公开(公告)号：US20250062951A1

公开(公告)日：2025-02-20

申请号：US18801922

申请日：2024-08-13

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Lecheng Zheng

IPC: H04L41/0659 ,  H04L41/0631 ,  H04L41/0816

Abstract: Systems and methods for unsupervised multi-modal causal structure learning for root cause analysis. System logs of a cloud system can be transformed to time-series data using a log-tailored language model to obtain system log features of the cloud system. A metric causal graph and a log causal graph can be predicted from modality-specific representations and modality-invariant representations of extracted system metric features and system log features, respectively, using the deep neural network. The metric causal graph and log causal graph can be fused to obtain a fused causal graph. Root causes of system failure can be flagged for system maintenance based on ranked entities obtained from the fused causal graph to obtain flagged root causes. System maintenance can be performed autonomously based on the flagged root causes from identified system entities to optimize the cloud system with an updated configuration.

公开(公告)号：US20240379200A1

公开(公告)日：2024-11-14

申请号：US18649145

申请日：2024-04-29

Applicant: NEC Laboratories America, Inc.

Inventor： Xujiang Zhao ,  Haifeng Chen ,  Wei Cheng ,  Yanchi Liu ,  Zhengzhang Chen ,  Haoyu Wang

IPC: G16H10/60 ,  G06F40/40

Abstract: Methods and systems for information extraction include configuring a language model with an information extraction instruction prompt and at least one labeled example prompt. Configuration of the language model is validated using at least one validation prompt. Errors made by the language model in response to the at least one validation prompt are corrected using a correction prompt. Information extraction is performed on an unlabeled sentence using the language model to identify a relation from the unlabeled sentence. An action is performed responsive to the identified relation.

公开(公告)号：US20240354215A1

公开(公告)日：2024-10-24

申请号：US18594582

申请日：2024-03-04

Applicant: NEC Laboratories America, Inc.

Inventor： Peng Yuan ,  LuAn Tang ,  Haifeng Chen ,  Yuncong Chen ,  Zhengzhang Chen ,  Motoyuki Sato

IPC: G06F11/34 ,  G06F11/32

CPC classification number: G06F11/3452 ,  G06F11/327

Abstract: Systems and methods are provided for incident analysis in Cyber-Physical Systems (CPS) using a Temporal Graph-based Incident Analysis System (TGIAS) and/or Transition Based Categorical Anomaly Detection (TCAD). Dynamically gathered multimodal data from a distributed network of sensors across the CPS are preprocessed to identify abnormal sensor readings indicative of potential incidents, and a multi-layered incident timeline graph, representing abnormal sensor readings, relationships to specific CPS components, and temporal sequencing of events is constructed. Severity scores are calculated, and severity rankings are assigned to identified anomalies based on a composite index including impact on CPS operation, comparison with historical incident data, and predictive risk assessments. Probable root causes of incidents and pathways for anomaly propagation through the CPS are identified using causal interference and the incident timeline graph to detect underlying vulnerabilities and predict future system weaknesses. Recommended actions are generated and executed for incident resolution and system optimization.

公开(公告)号：US20240232638A1

公开(公告)日：2024-07-11

申请号：US18545042

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：US12008471B2

公开(公告)日：2024-06-11

申请号：US17464127

申请日：2021-09-01

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Liang Tong

IPC: G06N3/08 ,  G06F18/21 ,  G06F18/28 ,  G06V10/22 ,  G06V40/16

CPC classification number: G06N3/08 ,  G06F18/2163 ,  G06F18/28 ,  G06V10/22 ,  G06V40/171 ,  G06V40/172

Abstract: Methods and systems for evaluating and enhancing a neural network model include constructing a surrogate model that corresponds to a target neural network model, based on a degree of knowledge about the target neural network model. Adversarial attacks against the surrogate model are generated, based on an attack goal, a level of attacker capability, and an attack model. The target neural network model is tested for accuracy under the generated adversarial attacks to determine a degree of robustness of the target neural network. Robustness of the target neural network model is enhanced by replacing facial occlusions in input images before applying the input images to the target neural network.

公开(公告)号：US20240185026A1

公开(公告)日：2024-06-06

申请号：US18493391

申请日：2023-10-24

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： LuAn Tang ,  Yuncong Chen ,  Wei Cheng ,  Haifeng Chen ,  Zhengzhang Chen ,  Yuji Kobayashi

IPC: G06N3/0442 ,  G06N3/08

CPC classification number: G06N3/0442 ,  G06N3/08

Abstract: Methods and systems for defect detection include determining a first residual score by comparing a first predicted system state, determined according to previously measured environment data, to an actual system state. A second residual score is determined by comparing a second predicted system state, determined according to previously measured system state data, to the actual system state. A defect score is generated based on a difference between the first residual score and the second residual score. An automatic action is performed responsive to a determination that the defect score indicates a defect in system behavior.

公开(公告)号：US20230376758A1

公开(公告)日：2023-11-23

申请号：US18302939

申请日：2023-04-19

Applicant: NEC Laboratories America, Inc.

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

IPC: G06N3/08 ,  G06N3/0442 ,  G06N7/01

CPC classification number: G06N3/08 ,  G06N3/0442 ,  G06N7/01

Abstract: A method for employing root cause analysis is presented. The method includes embedding, by an embedding layer, a sequence of events into a low-dimension space, employing a feature extractor and representation learner to convert log data from the sequence of events to time series data, the feature extractor including an auto-encoder model and a language model, and detecting root causes of failure or fault activities from the time series data.

公开(公告)号：US20220253696A1

公开(公告)日：2022-08-11

申请号：US17582191

申请日：2022-01-24

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Yuening Li

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for employing a deep unsupervised generative approach for disentangled factor learning is presented. The method includes decomposing, via an individual factor disentanglement component, latent variables into independent factors having different semantic meaning, enriching, via a group segment disentanglement component, group-level semantic meaning of sequential data by grouping the sequential data into a batch of segments, and generating hierarchical semantic concepts as interpretable and disentangled representations of time series data.

公开(公告)号：US20220067432A1

公开(公告)日：2022-03-03

申请号：US17464127

申请日：2021-09-01

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Liang Tong

IPC: G06K9/62

Abstract: Methods and systems for evaluating and enhancing a neural network model include constructing a surrogate model that corresponds to a target neural network model, based on a degree of knowledge about the target neural network model. Adversarial attacks against the surrogate model are generated, based on an attack goal, a level of attacker capability, and an attack model. The target neural network model is tested for accuracy under the generated adversarial attacks to determine a degree of robustness of the target neural network. Robustness of the target neural network model is enhanced by replacing facial occlusions in input images before applying the input images to the target neural network.

公开(公告)号：US20210256392A1

公开(公告)日：2021-08-19

申请号：US17170254

申请日：2021-02-08

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Yuening Li

IPC: G06N3/08 ,  G06N3/04

Abstract: Systems and methods for automatically generating a neural network to perform anomaly detection. The method includes defining a search space, including parameters for neural network architectures, definition-hypothesis of an anomaly assumption, and loss functions, as a tuple, and selecting a first candidate anomaly detection architecture from the search space that defines the parameters of the neural network architecture. The method further includes feeding a data set into the neural network defined by the first and second candidate anomaly detection architectures, and selecting a second candidate anomaly detection architecture from the search space that defines the parameters of the neural network. The method further includes determining a performance difference between the first architecture and the second architecture. The method further includes repeating the defining of the neural network with subsequent candidates, and identifying a best neural network candidate from the search space based on the performance differences.