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    TCR ENGINEERING WITH DEEP REINFORCEMENT LEARNING FOR INCREASING EFFICACY AND SAFETY OF TCR-T IMMUNOTHERAPY
    52.
    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
    55.
    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.

    Active microphone for increased DAS acoustic sensing capability
    57.
    发明授权
    Active microphone for increased DAS acoustic sensing capability 有权

    公开(公告)号：US11736867B2

    公开(公告)日：2023-08-22

    申请号：US17579516

    申请日：2022-01-19

    Applicant: NEC Laboratories America, Inc.

    Inventor： Junqiang Hu ,  Ting Wang

    IPC: H04R23/00 ,  G01H9/00 ,  H03G3/30 ,  H04R1/08 ,  H04R3/00

    CPC classification number: H04R23/008 ,  G01H9/004 ,  H03G3/3005 ,  H04R1/08 ,  H04R3/00 ,  H03G2201/103 ,  H04R2430/01

    Abstract: Aspects of the present disclosure describe DFOS/DAS systems, methods, and structures that employ active microphones to enhance DAS operational capabilities by using an active circuit to amplify acoustic signals including voice(s). The circuit includes a microphone to collect acoustic signal(s) resulting from voice signals in the environment, and a speaker or a vibration device driven by an amplifier. The circuit can be clipped onto the fiber, with direct contact through the speaker or vibration device. A microcontroller may advantageously be employed to control the circuit for reduced power consumption, by detecting activities locally and only enabling the speaker when needed. The microcontroller may also send other information such as battery status to the DFOS interrogator through vibration codes.

    T-CELL RECEPTOR OPTIMIZATION WITH REINFORCEMENT LEARNING AND MUTATION POLICIES FOR PRECISION IMMUNOTHERAPY
    58.
    发明公开
    T-CELL RECEPTOR OPTIMIZATION WITH REINFORCEMENT LEARNING AND MUTATION POLICIES FOR PRECISION IMMUNOTHERAPY 审中-公开

    公开(公告)号：US20230253068A1

    公开(公告)日：2023-08-10

    申请号：US18151686

    申请日：2023-01-09

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

    IPC: G16B15/30 ,  G06N20/00 ,  G16B20/50 ,  G16B40/20

    CPC classification number: G16B15/30 ,  G06N20/00 ,  G16B20/50 ,  G16B40/20

    Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.

    EFFICIENT TRANSFORMER FOR CONTENT-AWARE ANOMALY DETECTION IN EVENT SEQUENCES
    59.
    发明公开
    EFFICIENT TRANSFORMER FOR CONTENT-AWARE ANOMALY DETECTION IN EVENT SEQUENCES 审中-公开

    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.

    MULTI-MODALITY DATA ANALYSIS ENGINE FOR DEFECT DETECTION
    60.
    发明公开
    MULTI-MODALITY DATA ANALYSIS ENGINE FOR DEFECT DETECTION 审中-公开

    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

    IPC: G05B23/02 ,  G06N20/00

    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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