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    ADVERSARIAL IMITATION LEARNING ENGINE FOR ACTION RISK ESTIMATION BASED ON SENSOR DATA
    71.
    发明申请
    ADVERSARIAL IMITATION LEARNING ENGINE FOR ACTION RISK ESTIMATION BASED ON SENSOR DATA 有权

    公开(公告)号：US20250148540A1

    公开(公告)日：2025-05-08

    申请号：US18620099

    申请日：2024-03-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： LuAn Tang ,  Haoyu Wang ,  Haifeng Chen ,  Wenchao Yu ,  Zhengzhang Chen

    IPC: G06Q40/08

    Abstract: Systems and methods are provided for classifying components include monitoring sensors to collect sensor data related to a state of a plurality of components; processing, by a computing system, the sensor data to generate an action sequence using a transformer-based policy network for each of the components. A risk score is generated for the action sequence using a Generative Adversarial Network (GAN), wherein the GAN includes a generator for generating action sequences and a discriminator to distinguish low-risk action sequences in accordance with a threshold. The low-risk action sequences are associated with components in the plurality of components based on the risk score. A status of the low-risk action sequences is communicated to the components.

    ADVERSARIAL IMITATION LEARNING MODEL
    72.
    发明申请
    ADVERSARIAL IMITATION LEARNING MODEL 有权

    公开(公告)号：US20250148292A1

    公开(公告)日：2025-05-08

    申请号：US18620125

    申请日：2024-03-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： LuAn Tang ,  Haoyu Wang ,  Haifeng Chen ,  Wenchao Yu ,  Zhengzhang Chen

    IPC: G06N3/094 ,  G06N3/0455

    Abstract: Systems and methods train a transformer-based policy network and Generative Adversarial Network (GAN) by initializing a transformer-based policy network to model action sequences by encoding temporal dependencies within sensor data. Multi-head self-attention mechanisms process sequential sensor inputs by being pre-trained on a labeled dataset having sensor data from known low-risk action sequences. A generator within the GAN is trained to produce generated action sequences, which mimic behavior of low-risk action sequences. A discriminator within the GAN is concurrently trained to differentiate between action sequences derived from the labeled dataset and synthetic action sequences produced by the generator. A feedback loop is employed to adjust parameters to produce sequences indistinguishable from real low-risk action sequences. Risk scores are generated and low-risk action sequences are identified upon reaching a predetermined threshold for accuracy in distinguishing between real and synthetic action sequences.

    PRE-PROCESSING TIME SERIES DATA FOR EVENT RISK PREDICTION
    73.
    发明申请
    PRE-PROCESSING TIME SERIES DATA FOR EVENT RISK PREDICTION 有权

    公开(公告)号：US20250131296A1

    公开(公告)日：2025-04-24

    申请号：US18619872

    申请日：2024-03-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： LuAn Tang ,  Peng Yuan ,  Haifeng Chen

    IPC: G06N5/048

    Abstract: Systems and methods for pre-processing time series data include assigning transition events from categorical time series data into a list of transition sets that each include transitions from a respective first category to a respective second category and determining a mean duration and standard deviation, for each transition set, of the respective first category before the transition to the respective second category. A ratio is compared between the mean duration and the standard deviation to a threshold value to identify noisy transition sets; removing noisy transition sets from the list of transition sets to output de-noised transition sets. A probability of an event occurrence is predicted using the de-noised transition sets, and an action is performed responsive to the probability.

    NEURAL POINT PROCESS-BASED EVENT PREDICTION FOR MEDICAL DECISION MAKING
    75.
    发明公开
    NEURAL POINT PROCESS-BASED EVENT PREDICTION FOR MEDICAL DECISION MAKING 审中-公开

    公开(公告)号：US20240186018A1

    公开(公告)日：2024-06-06

    申请号：US18493331

    申请日：2023-10-24

    Applicant: NEC Laboratories America, Inc.

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

    IPC: G16H50/30

    CPC classification number: G16H50/30

    Abstract: Methods and systems for event prediction include encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector. Event prediction is performed using the feature vector to identify a next event to occur within a system. A corrective action is performed responsive to the next event to prevent or mitigate an effect of the next event. The predicted next event can be used in a healthcare context to support decision making by medical professionals with respect to the treatment of a patient. The encoding may include machine learning models to implement the transformers and the aggregation network using deep learning.

    MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS
    76.
    发明公开
    MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS 审中-公开

    公开(公告)号：US20240086586A1

    公开(公告)日：2024-03-14

    申请号：US18464381

    申请日：2023-09-11

    Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

    Inventor： LuAn Tang ,  Shepard Jiang ,  Peng Yuan ,  Yuncong Chen ,  Haifeng Chen ,  Yuji Kobayashi

    IPC: G06F30/20 ,  G06N3/08

    CPC classification number: G06F30/20 ,  G06N3/08

    Abstract: A computer-implemented method for simulating vehicle data and improving driving scenario detection is provided. The method includes retrieving, from vehicle sensors, key parameters from real data of validation scenarios to generate corresponding scenario configurations and descriptions, transferring target scenario descriptions and validation scenario descriptions to target scenario scripts and validation scenario scripts, respectively, to create first raw simulation data pertaining to target scenario descriptions and second raw simulation data pertaining to validation scenario descriptions, training, by an adjuster network, a deep neural network model to minimize differences between the first raw simulation data and the second raw simulation data, refining the first and second raw simulation data of rare driving scenarios to generate rare driving scenario training data, and outputting the rare driving scenario training data to a display screen of a computing device to enable a user to train a scenario detector for an autonomic driving assistant system.

    MULTI-MODALITY ROOT CAUSE LOCALIZATION FOR CLOUD COMPUTING SYSTEMS
    77.
    发明公开
    MULTI-MODALITY ROOT CAUSE LOCALIZATION FOR CLOUD COMPUTING SYSTEMS 审中-公开

    公开(公告)号：US20230376372A1

    公开(公告)日：2023-11-23

    申请号：US18302970

    申请日：2023-04-19

    Applicant: NEC Laboratories America, Inc.

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

    IPC: G06F11/07

    CPC classification number: G06F11/079 ,  G06F11/0769 ,  G06F11/0709

    Abstract: A method for detecting pod and node candidates from cloud computing systems representing potential root causes of failure or fault activities is presented. The method includes collecting, by a monitoring agent, multi-modality data including key performance indicator (KPI) data, metrics data, and log data, employing a feature extractor and representation learner to convert the log data to time series data, applying a metric prioritizer based on extreme value theory to prioritize metrics for root cause analysis and learn an importance of different metrics, ranking root causes of failure or fault activities by using a hierarchical graph neural network, and generating one or more root cause reports outlining the potential root causes of failure or fault activities.

    EXPLAINABLE ANOMALY DETECTION FOR CATEGORICAL SENSOR DATA
    78.
    发明公开
    EXPLAINABLE ANOMALY DETECTION FOR CATEGORICAL SENSOR DATA 审中-公开

    公开(公告)号：US20230281186A1

    公开(公告)日：2023-09-07

    申请号：US18173431

    申请日：2023-02-23

    Applicant: NEC Laboratories America, Inc.

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

    IPC: G06F16/23

    CPC classification number: G06F16/2365

    Abstract: Methods and systems for anomaly correction include detecting an anomaly in a time series of categorical data values generated by a sensor, displaying a visual depiction of an anomalous time series, corresponding to the detected anomaly, on a user interface with a visual depiction of an expected normal behavior to contrast to the anomalous time series, and performing a corrective action responsive to the displayed detected anomaly. Detecting the anomaly includes framing the time series with a sliding window, generating a histogram for the categorical data values using a histogram template, generating an anomaly score for the time series using an anomaly detection histogram model on the generated histogram, and comparing the anomaly score to an anomaly threshold.

    Node classification in dynamic networks using graph factorization
    80.
    发明授权
    Node classification in dynamic networks using graph factorization 有权

    公开(公告)号：US11606393B2

    公开(公告)日：2023-03-14

    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: H04L9/40 ,  G06N3/049 ,  H04L41/16 ,  G06N3/08

    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.

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