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公开(公告)号：US20240013920A1
公开(公告)日：2024-01-11
申请号：US18370074
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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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
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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公开(公告)号：US11842271B2
公开(公告)日：2023-12-12
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
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.
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公开(公告)号：US20230252139A1
公开(公告)日：2023-08-10
申请号：US18157180
申请日：2023-01-20
Applicant: NEC Laboratories America, Inc.
Inventor： Yanchi Liu , Xuchao Zhang , Haifeng Chen , Wei Cheng , Shengming Zhang
IPC: G06F21/55
CPC classification number: G06F21/554
Abstract: A method for implementing a self-attentive encoder-decoder transformer framework for anomaly detection in event sequences is presented. The method includes feeding event content information into a content-awareness layer to generate event representations, inputting, into an encoder, event sequences of two hierarchies to capture long-term and short-term patterns and to generate feature maps, adding, in the decoder, a special sequence token at a beginning of an input sequence under detection, during a training stage, applying a one-class objective to bound the decoded special sequence token with a reconstruction loss for sequence forecasting using the generated feature maps from the encoder, and during a testing stage, labeling any event representation whose decoded special sequence token lies outside a hypersphere as an anomaly.
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公开(公告)号：US20230152791A1
公开(公告)日：2023-05-18
申请号：US17984413
申请日：2022-11-10
Applicant: NEC Laboratories America, Inc.
Inventor： LuAn Tang , Yuncong Chen , Wei Cheng , Haifeng Chen , Zhengzhang Chen , Yuji Kobayashi
CPC classification number: G05B23/0221 , G06N20/00 , G05B23/0235 , G05B23/0237
Abstract: Systems and methods for defect detection for vehicle operations, including collecting a multiple modality input data stream from a plurality of different types of vehicle sensors, extracting one or more features from the input data stream using a grid-based feature extractor, and retrieving spatial attributes of objects positioned in any of a plurality of cells of the grid-based feature extractor. One or more anomalies are detected based on residual scores generated by each of cross attention-based anomaly detection and time-series-based anomaly detection. One or more defects are identified based on a generated overall defect score determined by integrating the residual scores for the cross attention-based anomaly detection and the time-series based anomaly detection being above a predetermined defect score threshold. Operation of the vehicle is controlled based on the one or more defects identified.
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Patent Agency Ranking
公开(公告)号：US20230112441A1
公开(公告)日：2023-04-13
申请号：US17961169
申请日：2022-10-06
Applicant: NEC Laboratories America, Inc. , NEC Corporation
Inventor： LuAn Tang , Yuncong Chen , Wei Cheng , Zhengzhang Chen , Haifeng Chen , Yuji Kobayashi , Yuxiang Ren
Abstract: Systems and methods for data fusion and analysis of vehicle sensor data, including receiving a multiple modality input data stream from a plurality of different types of vehicle sensors, determining latent features by extracting modality-specific features from the input data stream, and aligning a distribution of the latent features of different modalities by feature-level data fusion. Classification probabilities can be determined for the latent features using a fused modality scene classifier. A tree-organized neural network can be trained to determine path probabilities and issue driving pattern judgments, with the tree-organized neural network including a soft tree model and a hard decision leaf. One or more driving pattern judgments can be issued based on a probability of possible driving patterns derived from the modality-specific features.
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公开(公告)号：US20220383108A1
公开(公告)日：2022-12-01
申请号：US17728071
申请日：2022-04-25
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Dongkuan Xu , Haifeng Chen
IPC: G06N3/08
Abstract: A method for performing contrastive learning for graph tasks and datasets by employing an information-aware graph contrastive learning framework is presented. The method includes obtaining two semantically similar views of a graph coupled with a label for training by employing a view augmentation component, feeding the two semantically similar views into respective encoder networks to extract latent representations preserving both structure and attribute information in the two views, optimizing a contrastive loss based on a contrastive mode by maximizing feature consistency between the latent representations, training a neural network with the optimized contrastive loss, and predicting a new graph label or a new node label in the graph.
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公开(公告)号：US11423436B2
公开(公告)日：2022-08-23
申请号：US16787657
申请日：2020-02-11
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen
Abstract: A system is provided for interpretable viewing interest. A transformer with multi-head self-attention derives different hierarchical orders of input features. Hierarchical attention layers (i) aggregate the different hierarchical orders to obtain aggregated single-order feature representations and (iii) derive aggregation attention weights for the different hierarchical orders based on an applied order of the hierarchical attention layers. An attentional scoring layer evaluates the aggregated representations to output a significance of each order with respect to various CTR predictions. A hierarchical interpretation layer determines a respective importance of each input feature in various combinations from which the various CTR predictions are derived based on the aggregation attention weights and the significance of each order. A display device displays each of the various combinations for the various CTR predictions along with the respective importance of each of the constituent one of the input features in the various input feature combinations.
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公开(公告)号：US20220075945A1
公开(公告)日：2022-03-10
申请号：US17464005
申请日：2021-09-01
Applicant: NEC Laboratories America, Inc.
Inventor： Xuchao Zhang , Yanchi Liu , Bo Zong , Wei Cheng , Haifeng Chen , Junxiang Wang
IPC: G06F40/284 , G06F40/205 , G06F40/295 , G06N3/04
Abstract: A computer-implemented method is provided for cross-lingual transfer. The method includes randomly masking a source corpus and a target corpus to obtain a masked source corpus and a masked target corpus. The method further includes tokenizing, by pretrained Natural Language Processing (NLP) models, the masked source corpus and the masked target corpus to obtain source tokens and target tokens. The method also includes transforming the source tokens and the target tokens into a source dependency parsing tree and a target dependency parsing tree. The method additionally includes inputting the source dependency parsing tree and the target dependency parsing tree into a graph encoder pretrained on a translation language modeling task to extract common language information for transfer. The method further includes fine-tuning the graph encoder and a down-stream network for a specific NLP down-stream task.
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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
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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