公开(公告)号：US20220237386A1

公开(公告)日：2022-07-28

申请号：US17577745

申请日：2022-01-18

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Wenchao Yu ,  Haifeng Chen

IPC: G06F40/40 ,  G06F40/205 ,  G06F40/284 ,  G06Q30/06

Abstract: Rating prediction systems and methods include extracting aspect-sentiment pairs from an input text. An attention-property-aware rating is estimated for the input text using the extracted aspect-sentiment pairs with a neural network that captures implicit and explicit features of the text. A response to the input text is performed based on the estimated rating.

公开(公告)号：US11323465B2

公开(公告)日：2022-05-03

申请号：US16562805

申请日：2019-09-06

Applicant: NEC Laboratories America, Inc.

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

IPC: G06N3/08 ,  H04L29/06

Abstract: Systems and methods for implementing sequence data based temporal behavior analysis (SDTBA) to extract features for characterizing temporal behavior of network traffic are provided. The method includes extracting communication and profile data associated with one or more devices to determine sequences of data associated with the devices. The method includes generating temporal features to model anomalous network traffic. The method also includes inputting, into an anomaly detection process for anomalous network traffic, the temporal features and the sequences of data associated with the devices and formulating a list of prediction results of anomalous network traffic associated with the devices.

公开(公告)号：US20220092402A9

公开(公告)日：2022-03-24

申请号：US16987789

申请日：2020-08-07

Applicant: NEC Laboratories America, Inc.

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

IPC: G06N3/08 ,  G06N3/04

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.

公开(公告)号：US20220058482A1

公开(公告)日：2022-02-24

申请号：US17391427

申请日：2021-08-02

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen ,  Yiwei Sun

IPC: G06N3/08 ,  G06N20/00 ,  G06F16/901

Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

公开(公告)号：US20220019892A1

公开(公告)日：2022-01-20

申请号：US17379078

申请日：2021-07-19

Applicant: NEC Laboratories America, Inc.

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

IPC: G06N3/08 ,  G06N7/00 ,  G16H20/40

Abstract: A method for training a predictive model includes training a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information. An augmented model is trained to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information.

公开(公告)号：US11221617B2

公开(公告)日：2022-01-11

申请号：US16653033

申请日：2019-10-15

Applicant: NEC Laboratories America, Inc.

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

IPC: G05B23/02 ,  G06N20/10 ,  G06F16/901 ,  G06K9/62 ,  G06F17/18

Abstract: Systems and methods for predicting system device failure are provided. The method includes performing graph-based predictive maintenance (GBPM) to determine a trained ensemble classification model for detecting maintenance ready components that includes extracted node features and graph features. The method includes constructing, based on testing data and the trained ensemble classification model, an attributed temporal graph and the extracted node features and graph features. The method further includes concatenating the extracted node features and graph features. The method also includes determining, based on the trained ensemble classification model, a list of prediction results of components that are to be scheduled for component maintenance.

公开(公告)号：US20210255363A1

公开(公告)日：2021-08-19

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 ,  G06N3/04 ,  G06N3/08

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：US20210133017A1

公开(公告)日：2021-05-06

申请号：US17075309

申请日：2020-10-20

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Shuchu Han ,  Haifeng Chen

IPC: G06F11/07 ,  G06F16/22 ,  G06N20/00

Abstract: Systems and methods for predicting device failure, including inputting a plurality of records for electronic communication devices, each including one or more attributes and a label, as a table to a modeling algorithm, wherein there are separate tables for each period in a time sequence; building a multi-stage decision tree from the time sequence of records using the modeling algorithm running on a processor device; inputting a record for a device having an empty label value into the decision tree to determine the likelihood of entity failure; and reporting a predicted failure for the device to a user on a display to initiate replacement before a next time period.

公开(公告)号：US20210065278A1

公开(公告)日：2021-03-04

申请号：US16995052

申请日：2020-08-17

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Haifeng Chen ,  Bo Zong ,  Xin Dong ,  Wei Cheng

IPC: G06Q30/06 ,  G06N20/00

Abstract: A method for implementing a recommendation system using an asymmetrically hierarchical network includes, for a user and an item corresponding to a user-item pair, aggregating, using asymmetrically designed sentence aggregators, respective ones of a set of item sentence embeddings and a set of user sentence embeddings to generate a set of item review embeddings based on first item attention weights and a set of user review embeddings based on first user attention weights, respectively, aggregating, using asymmetrically designed review aggregators, respective ones of the set of item review embeddings and the set of user review embeddings to generate an item embedding based on a second item attention weights and a user embedding based on second user attention weights, respectively, and predicting a rating of the user-item pair based on the item embedding and the user embedding.

公开(公告)号：US20210064999A1

公开(公告)日：2021-03-04

申请号：US17003112

申请日：2020-08-26

Applicant: NEC Laboratories America, Inc.

Inventor： Yanchi Liu ,  Wei Cheng ,  Bo Zong ,  LuAn Tang ,  Haifeng Chen ,  Denghui Zhang

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods and systems for allocating network resources responsive to network traffic include modeling spatial correlations between fine spatial granularity traffic and coarse spatial granularity traffic for different sites and regions to determine spatial feature vectors for one or more sites in a network. Temporal correlations at a fine spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. Temporal correlations at a coarse spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. A traffic flow prediction is determined for the one or more sites in the network, based on the temporal correlations at the fine spatial granularity and the temporal correlations at the coarse spatial granularity. Network resources are provisioned at the one or more sites in accordance with the traffic flow prediction.