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    ADVERSARIAL IMITATION LEARNING ENGINE FOR ACTION RISK ESTIMATION BASED ON SENSOR DATA
    61.
    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
    62.
    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.

    DEMONSTRATION UNCERTAINTY-BASED ARTIFICIAL INTELLIGENCE MODEL FOR OPEN INFORMATION EXTRACTION
    63.
    发明申请
    DEMONSTRATION UNCERTAINTY-BASED ARTIFICIAL INTELLIGENCE MODEL FOR OPEN INFORMATION EXTRACTION 有权

    公开(公告)号：US20250077848A1

    公开(公告)日：2025-03-06

    申请号：US18817793

    申请日：2024-08-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xujiang Zhao ,  Haoyu Wang ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Yanchi Liu ,  Chen Ling

    IPC: G06N3/0475 ,  G16H80/00

    Abstract: Systems and methods for a demonstration uncertainty-based artificial intelligence model for open information extraction. A large language model (LLM) can generate initial structured sentences using an initial prompt for a domain-specific instruction extracted from an unstructured text input. Structural similarities between the initial structured sentences and sentences from a training dataset can be determined to obtain structurally similar sentences. The LLM can identify relational triplets from combinations of tokens from generated sentences using and the structurally similar sentences. The relational triplets can be filtered based on a calculated demonstration uncertainty to obtain a filtered triplet list. A domain-specific task can be performed using the filtered triplet list to assist the decision-making process of a decision-making entity.

    NEURAL POINT PROCESS-BASED EVENT PREDICTION FOR MEDICAL DECISION MAKING
    65.
    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.

    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES
    67.
    发明公开
    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES 审中-公开

    公开(公告)号：US20240028898A1

    公开(公告)日：2024-01-25

    申请号：US18479372

    申请日：2023-10-02

    Applicant: NEC Laboratories America, Inc.

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

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: 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.

    MULTI-MODALITY ROOT CAUSE LOCALIZATION FOR CLOUD COMPUTING SYSTEMS
    68.
    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.

    SEMI-SUPERVISED FRAMEWORK FOR EFFICIENT TIME-SERIES ORDINAL CLASSIFICATION
    69.
    发明公开
    SEMI-SUPERVISED FRAMEWORK FOR EFFICIENT TIME-SERIES ORDINAL CLASSIFICATION 审中-公开

    公开(公告)号：US20230252302A1

    公开(公告)日：2023-08-10

    申请号：US18152238

    申请日：2023-01-10

    Applicant: NEC Laboratories America, Inc.

    Inventor： Liang Tong ,  Takehiko Mizoguchi ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Nauman Ahad

    IPC: G06N3/0895 ,  G06N3/0442

    CPC classification number: G06N3/0895 ,  G06N3/0442

    Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k−1 binary classifiers on top of the semi-supervised representations to obtain k−1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k−1 binary predictions by matching the inconsistent ones to consistent ones of the k−1 binary predictions. The method further includes aggregating the k−1 binary predictions to obtain an ordinal prediction.

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