公开(公告)号：US20240006069A1

公开(公告)日：2024-01-04

申请号：US18370049

申请日：2023-09-19

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen ,  Takayoshi Asakura

IPC: G16H50/20 ,  G06N3/08 ,  G16H10/60 ,  G06N3/047

CPC classification number: G16H50/20 ,  G06N3/047 ,  G16H10/60 ,  G06N3/08

Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

公开(公告)号：US20220058240A9

公开(公告)日：2022-02-24

申请号：US16987734

申请日：2020-08-07

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongkuan Xu ,  Wenchao Yu

IPC: G06F17/18 ,  G06K9/62 ,  G06N5/04 ,  G06N3/04

Abstract: A method for unsupervised multivariate time series trend detection for group behavior analysis is presented. The method includes collecting multi-variate time series data from a plurality of sensors, learning piecewise linear trends jointly for all of the multi-variate time series data, dividing the multi-variate time series data into a plurality of time segments, counting a number of up/down trends in each of the plurality of time segments, for a training phase, employing a cumulative sum (CUSUM), and, for a testing phase, monitoring the CUSUM for trend changes.

公开(公告)号：US20210067558A1

公开(公告)日：2021-03-04

申请号：US17004547

申请日：2020-08-27

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Haifeng Chen ,  Bo Zong ,  LuAn Tang ,  Wei Cheng

IPC: H04L29/06 ,  G06N3/04

Abstract: Methods and systems for detecting and responding to anomalous nodes in a network include inferring temporal factors, using a computer-implemented neural network, that represent changes in a network graph across time steps, with a temporal factor for each time step depending on a temporal factor for a previous time step. An invariant factor is inferred that represents information about the network graph that does not change across the time steps. The temporal factors and the invariant factor are combined into a combined temporal-invariant representation. It is determined that an unlabeled node is anomalous, based on the combined temporal-invariant representation. A security action is performed responsive to the determination that unlabeled node is anomalous.

公开(公告)号：US10402289B2

公开(公告)日：2019-09-03

申请号：US15661625

申请日：2017-07-27

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Guofei Jiang ,  Jingchao Ni

IPC: G06F11/00 ,  G06F11/22 ,  G06N5/04 ,  G06F17/50 ,  G06N20/00

Abstract: A computer-implemented method for diagnosing system faults by fine-grained causal anomaly inference is presented. The computer-implemented method includes identifying functional modules impacted by causal anomalies and backtracking causal anomalies in impaired functional modules by a low-rank network diffusion model. An invariant network and a broken network are inputted into the system, the invariant network and the broken network being jointly clustered to learn a degree of broken severities of different clusters as a result of fault propagations.

公开(公告)号：US20240005154A1

公开(公告)日：2024-01-04

申请号：US18370092

申请日：2023-09-19

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen

IPC: G06N3/08 ,  G06N7/01

CPC classification number: G06N3/08 ,  G06N7/01

Abstract: A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.

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：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.

公开(公告)号：US20220068445A1

公开(公告)日：2022-03-03

申请号：US17408769

申请日：2021-08-23

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Bo Zong ,  Wei Cheng ,  Haifeng Chen ,  Yinjun Wu

IPC: G16H10/60 ,  G06N20/00 ,  G06N5/04 ,  G16H50/30

Abstract: A method for managing data of dialysis patients by employing a Deep Dynamic Gaussian Mixture (DDGM) model to forecast medical time series data is presented. The method includes filling missing values in an input multivariate time series by model parameters, via a pre-imputation component, by using a temporal intensity function based on Gaussian kernels and multi-dimensional correlation based on correlation parameters to be learned and storing, via a forecasting component, parameters that represent cluster centroids used by the DDGM to cluster time series for capturing correlations between different time series samples.

公开(公告)号：US11169865B2

公开(公告)日：2021-11-09

申请号：US16562755

申请日：2019-09-06

Applicant: NEC Laboratories America, Inc.

Inventor： Haifeng Chen ,  Bo Zong ,  Wei Cheng ,  LuAn Tang ,  Jingchao Ni

IPC: G06F11/07 ,  G06N3/08 ,  G06N20/10 ,  G06K9/62

Abstract: Systems and methods for implementing heterogeneous feature integration for device behavior analysis (HFIDBA) are provided. The method includes representing each of multiple devices as a sequence of vectors for communications and as a separate vector for a device profile. The method also includes extracting static features, temporal features, and deep embedded features from the sequence of vectors to represent behavior of each device. The method further includes determining, by a processor device, a status of a device based on vector representations of each of the multiple devices.

公开(公告)号：US20210248462A1

公开(公告)日：2021-08-12

申请号：US17158466

申请日：2021-01-26

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Zhengzhang Chen ,  Wei Cheng ,  Bo Zong ,  Haifeng Chen

IPC: G06N3/08 ,  G06N3/04

Abstract: A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.