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    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES
    31.
    发明公开
    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES 审中-公开

    公开(公告)号：US20240028897A1

    公开(公告)日：2024-01-25

    申请号：US18479326

    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.

    MEDICAL EVENT PREDICTION USING A PERSONALIZED DUAL-CHANNEL COMBINER NETWORK
    32.
    发明公开
    MEDICAL EVENT PREDICTION USING A PERSONALIZED DUAL-CHANNEL COMBINER NETWORK 审中-公开

    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

    IPC: G16H50/20 ,  G06N3/08 ,  G16H10/60 ,  G06N3/047

    CPC classification number: G16H50/20 ,  G06N3/08 ,  G16H10/60 ,  G06N3/047

    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.

    Medical Event Prediction Using a Personalized Dual-Channel Combiner Network
    33.
    发明公开
    Medical Event Prediction Using a Personalized Dual-Channel Combiner Network 审中-公开

    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

    IPC: G16H50/20 ,  G06N3/08 ,  G16H10/60 ,  G06N3/047

    CPC classification number: G16H50/20 ,  G06N3/047 ,  G16H10/60 ,  G06N3/08

    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.

    TCR ENGINEERING WITH DEEP REINFORCEMENT LEARNING FOR INCREASING EFFICACY AND SAFETY OF TCR-T IMMUNOTHERAPY
    37.
    发明公开
    TCR ENGINEERING WITH DEEP REINFORCEMENT LEARNING FOR INCREASING EFFICACY AND SAFETY OF TCR-T IMMUNOTHERAPY 审中-公开

    公开(公告)号：US20230304189A1

    公开(公告)日：2023-09-28

    申请号：US18174799

    申请日：2023-02-27

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf

    IPC: C40B30/04 ,  G06N3/092 ,  G06N3/0442 ,  C40B20/04 ,  G16B40/00

    CPC classification number: C40B30/04 ,  G06N3/092 ,  G06N3/0442 ,  C40B20/04 ,  G16B40/00

    Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs for immunotherapy includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from patients, predicting interaction scores between the extracted peptides and the TCRs from the patients, developing a deep reinforcement learning framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions, outputting mutated TCRs, ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells, and for each top-ranked TCR candidate, repeatedly identifying a set of self-peptides that the top-ranked TCR candidate binds to and further optimizing it greedily by maximizing a sum of its interaction scores with a given set of peptide antigens while minimizing a sum of its interaction scores with the set of self-peptides until stopping criteria of efficacy and safety are met.

    HISTOGRAM MODEL FOR CATEGORICAL ANOMALY DETECTION
    40.
    发明公开
    HISTOGRAM MODEL FOR CATEGORICAL ANOMALY DETECTION 审中-公开

    公开(公告)号：US20230280739A1

    公开(公告)日：2023-09-07

    申请号：US18173452

    申请日：2023-02-23

    Applicant: NEC Laboratories America, Inc.

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

    IPC: G05B23/02

    CPC classification number: G05B23/0283

    Abstract: Methods and systems for anomaly detection include training an anomaly detection histogram model using historical categorical value data. Training the anomaly detection histogram model includes generating a histogram template based on historical categorical data, converting the historical categorical data to a histogram using the histogram template, and determining a normal range and anomaly threshold for the categorical data using the histogram.

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