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    System and Method for Detecting Objects Obstructing a Driver's View of a Road
    43.
    发明申请
    System and Method for Detecting Objects Obstructing a Driver's View of a Road 有权
    用于检测物体的系统和方法阻碍驾驶员对道路的看法

    公开(公告)号：US20160054452A1

    公开(公告)日：2016-02-25

    申请号：US14830873

    申请日：2015-08-20

    Applicant: NEC Laboratories America, Inc.

    Inventor： Eric Cosatto ,  Hans Peter Graf

    IPC: G01S19/51 ,  B60K35/00 ,  G01S19/48 ,  G01C21/36

    CPC classification number: G01S19/51 ,  B60K35/00 ,  B60K2350/1084 ,  B60K2350/962 ,  B60K2350/965 ,  G01C21/3629 ,  G01C21/3697 ,  G01S11/12 ,  G01S13/867 ,  G01S13/931 ,  G01S17/89 ,  G01S17/936 ,  G01S19/14 ,  G01S19/48 ,  G01S19/49

    Abstract: A system and method for a motorized land vehicle that detects objects obstructing a driver's view of an active road, includes an inertial measurement unit-enabled global position system (GPS/IMU) subsystem for obtaining global position system (GPS) position and heading data of a land vehicle operated by the driver as the vehicle travels along a road, a street map subsystem for obtaining street map data of the GPS position of the vehicle using the GPS position and heading data as the vehicle travels along the road, and a three-dimensional (3D) object detector subsystem for detecting objects ahead of the vehicle and determining a 3D position and 3D size data of each of the detected objects ahead of the vehicle. The street map subsystem merges the street map data, the GPS position and heading data of the vehicle and the 3D position data and 3D size data of the detected objects, to create real-time two-dimensional (2D) top-view map representation of a traffic scene ahead of the vehicle. The street map subsystems finds active roads ahead of the vehicle in the traffic scene, and finds each active road segment of the active roads ahead of the vehicle that is obstructed by one of the detected objects. A driver alert subsystem notifies a driver of the vehicle of each of the active road segments that is obstructed by one of the detected objects.

    Abstract translation: 一种用于检测妨碍驾驶员对主动道路的观察的物体的电动陆地车辆的系统和方法包括：具有惯性测量单元的全球定位系统（GPS / IMU）子系统，用于获得全球定位系统（GPS）的位置和航向数据 当车辆沿着道路行驶时由驾驶员操作的陆地车辆;街道地图子系统，用于当车辆沿着道路行进时，使用GPS位置和航向数据获取车辆的GPS位置的街道地图数据; （3D）物体检测器子系统，用于检测车辆前方的物体，并确定车辆前方的每个检测物体的3D位置和3D尺寸数据。 街道地图子系统将街道地图数据，车辆的GPS位置和航向数据以及检测到的物体的3D位置数据和3D尺寸数据进行合并，以创建实时二维（2D）顶视图地图 车辆前方的交通情况。 街道地图子系统在交通场景中找到车辆前方的活动道路，并且找到被检测到的对象之一阻挡的车辆之前的活动道路的每个活动路段。 驾驶员警报子系统通知被检测对象之一阻塞的每个活动道路段的车辆的驾驶员。

    DISENTANGLED WASSERSTEIN AUTOENCODER FOR PROTEIN ENGINEERING
    44.
    发明公开
    DISENTANGLED WASSERSTEIN AUTOENCODER FOR PROTEIN ENGINEERING 审中-公开

    公开(公告)号：US20240078430A1

    公开(公告)日：2024-03-07

    申请号：US18449748

    申请日：2023-08-15

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf ,  Tianxiao Li

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: A computer-implemented method for learning disentangled representations for T-cell receptors to improve immunotherapy is provided. The method includes optionally introducing a minimal number of mutations to a T-cell receptor (TCR) sequence to enable the TCR sequence to bind to a peptide, using a disentangled Wasserstein autoencoder to separate an embedding space of the TCR sequence into functional embeddings and structural embeddings, feeding the functional embeddings and the structural embeddings to a long short-term memory (LSTM) or transformer decoder, using an auxiliary classifier to predict a probability of a positive binding label from the functional embeddings and the peptide, and generating new TCR sequences with enhanced binding affinity for immunotherapy to target a particular virus or tumor.

    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY
    45.
    发明公开
    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY 审中-公开

    公开(公告)号：US20240071571A1

    公开(公告)日：2024-02-29

    申请号：US18471641

    申请日：2023-09-21

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

    IPC: G16B40/00 ,  G06N3/08 ,  G16B15/30

    CPC classification number: G16B40/00 ,  G06N3/08 ,  G16B15/30

    Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.

    TCR ENGINEERING WITH DEEP REINFORCEMENT LEARNING FOR INCREASING EFFICACY AND SAFETY OF TCR-T IMMUNOTHERAPY
    46.
    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.

    T-CELL RECEPTOR OPTIMIZATION WITH REINFORCEMENT LEARNING AND MUTATION POLICIES FOR PRECISION IMMUNOTHERAPY
    48.
    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.

    PEPTIDE-BASED VACCINE GENERATION SYSTEM
    50.
    发明申请
    PEPTIDE-BASED VACCINE GENERATION SYSTEM 有权

    公开(公告)号：US20210319847A1

    公开(公告)日：2021-10-14

    申请号：US17197166

    申请日：2021-03-10

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Wenchao Yu ,  Hans Peter Graf ,  Igor Durdanovic

    IPC: G16B15/30 ,  G16B40/00 ,  G06N3/08 ,  G06N3/04

    Abstract: A method is provided for peptide-based vaccine generation. The method receives a dataset of positive and negative binding peptide sequences. The method pre-trains a set of peptide binding property predictors on the dataset to generate training data. The method trains a Wasserstein Generative Adversarial Network (WGAN) only on the positive binding peptide sequences, in which a discriminator of the WGAN is updated to distinguish generated peptide sequences from sampled positive peptide sequences from the training data, and a generator of the WGAN is updated to fool the discriminator. The method trains the WGAN only on the positive binding peptide sequences while simultaneously updating the generator to minimize a kernel Maximum Mean Discrepancy (MMD) loss between the generated peptide sequences and the sampled peptide sequences and maximize prediction accuracies of a set of pre-trained peptide binding property predictors with parameters of the set of pre-trained peptide binding property predictors being fixed.

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