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21.发明申请公开(公告)号：US20210064998A1
公开(公告)日：2021-03-04
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
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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公开(公告)号：US20210064689A1
公开(公告)日：2021-03-04
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
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.
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公开(公告)号：US20200092316A1
公开(公告)日：2020-03-19
申请号：US16565746
申请日：2019-09-10
Applicant: NEC Laboratories America, Inc.
Inventor： LuAn Tang , Jingchao Ni , Wei Cheng , Haifeng chen , Dongjin Song , Bo Zong , Wenchao Yu
IPC: H04L29/06 , G06K9/62 , G06F16/901
Abstract: Systems and methods for implementing dynamic graph analysis (DGA) to detect anomalous network traffic are provided. The method includes processing communications and profile data associated with multiple devices to determine dynamic graphs. The method includes generating features to model temporal behaviors of network traffic generated by the multiple devices based on the dynamic graphs. The method also includes formulating a list of prediction results for sources of the anomalous network traffic from the multiple devices based on the temporal behaviors.
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公开(公告)号：US20240006070A1
公开(公告)日：2024-01-04
申请号：US18370062
申请日：2023-09-19
Applicant: NEC Laboratories America, Inc.
Inventor： Jingchao Ni , Wei Cheng , Haifeng Chen , Takayoshi Asakura
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.
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25.发明公开公开(公告)号：US20240005156A1
公开(公告)日：2024-01-04
申请号：US18370140
申请日：2023-09-19
Applicant: NEC Laboratories America, Inc.
Inventor： Jingchao Ni , Wei Cheng , Haifeng Chen
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.
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Patent Agency Ranking
26.发明公开公开(公告)号：US20240005155A1
公开(公告)日：2024-01-04
申请号：US18370129
申请日：2023-09-19
Applicant: NEC Laboratories America, Inc.
Inventor： Jingchao Ni , Wei Cheng , Haifeng Chen
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.
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公开(公告)号：US11604969B2
公开(公告)日：2023-03-14
申请号：US16553465
申请日：2019-08-28
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , LuAn Tang , Dongjin Song , Bo Zong , Haifeng Chen , Jingchao Ni , Wenchao Yu
Abstract: Systems and methods for predicting system device failure are provided. The method includes representing device failure related data associated with the devices from a predetermined domain by temporal graphs for each of the devices. The method also includes extracting vector representations based on temporal graph features from the temporal graphs that capture both temporal and structural correlation in the device failure related data. The method further includes predicting, based on the vector representations and device failure related metrics in the predetermined domain, one or more of the devices that is expected to fail within a predetermined time.
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公开(公告)号：US20230070443A1
公开(公告)日：2023-03-09
申请号：US17896590
申请日：2022-08-26
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen , Jingchao Ni , Wenchao Yu , Yuncong Chen , Dongsheng Luo
IPC: G06K9/62
Abstract: A computer-implemented method for meta-learning is provided. The method includes receiving a training time series and labels corresponding to some of the training time series. The method further includes optimizing time series augmentations of the training time series using a time series augmentation selection process performed by a meta learner to obtain a selected augmentation from a plurality of candidate augmentations. The method also includes training a time series encoder with contrastive loss using the selected augmentation to obtain a learned time series encoder. The method additionally includes learning, by the learned time series encoder, a vector representation of another time series. The method further includes performing, by the learned time series encoder, a downstream task of label classification for at least a portion of the other time series.
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公开(公告)号：US11521255B2
公开(公告)日：2022-12-06
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
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.
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公开(公告)号：US20220319709A1
公开(公告)日：2022-10-06
申请号：US17711453
申请日：2022-04-01
Applicant: NEC Laboratories America, Inc. , NEC Corporation
Inventor： Jingchao Ni , Wei Cheng , Haifeng Chen , Takayoshi Asakura
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.
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