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公开(公告)号：US12263849B2
公开(公告)日：2025-04-01
申请号：US17961169
申请日：2022-10-06
Applicant: NEC Laboratories America, Inc. , NEC Corporation
Inventor： LuAn Tang , Yuncong Chen , Wei Cheng , Zhengzhang Chen , Haifeng Chen , Yuji Kobayashi , Yuxiang Ren
Abstract: Systems and methods for data fusion and analysis of vehicle sensor data, including receiving a multiple modality input data stream from a plurality of different types of vehicle sensors, determining latent features by extracting modality-specific features from the input data stream, and aligning a distribution of the latent features of different modalities by feature-level data fusion. Classification probabilities can be determined for the latent features using a fused modality scene classifier. A tree-organized neural network can be trained to determine path probabilities and issue driving pattern judgments, with the tree-organized neural network including a soft tree model and a hard decision leaf. One or more driving pattern judgments can be issued based on a probability of possible driving patterns derived from the modality-specific features.
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公开(公告)号：US12205418B2
公开(公告)日：2025-01-21
申请号：US17464056
申请日：2021-09-01
Applicant: NEC Laboratories America, Inc. , NEC Corporation
Inventor： Luan Tang , Wei Cheng , Haifeng Chen , Yuji Kobayashi , Zhengzhang Chen
Abstract: A method for early warning is provided. The method clusters normal historical data of normal cars into groups based on the car subsystem to which they belong. The method extracts (i) features based on group membership and (ii) feature correlations based on correlation graphs formed from the groups. The method trains an Auto-Encoder and Auto Decoder (AE&AD) model based on the features and the feature correlations to reconstruct the normal historical data with minimum reconstruction errors. The method reconstructs, using the trained AE&AD model, historical data of specific car fault types with reconstruction errors, normalizes the reconstruction errors, and selects features of the car faults with a top k large errors as fault signatures. The method reconstructs streaming data of monitored cars using the trained AE&AD model to determine streaming reconstruction errors, comparing the streaming reconstruction errors with the fault signatures to predict and provide alerts for impending known faults.
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公开(公告)号：US20250005373A1
公开(公告)日：2025-01-02
申请号：US18749887
申请日：2024-06-21
Applicant: NEC Laboratories America, Inc.
Inventor： Junxiang Wang , Wei Cheng , LuAn Tang , Haifeng Chen
IPC: G06N3/096 , G06N3/0455 , G06N3/084
Abstract: Systems and methods are provided for adapting a model trained from multiple source time-series domains to a target time-series domain, including integrating input data from source time-series domains to pretrain a model with a set of domain-invariant representations, fine-tuning the model by learning prompts specific to each source time-series domain using data from the source time-series domains, and applying instance normalization and segmenting the time-series data into subseries-level normalized patches for the target time-series domain. The normalized patches are fed into a transformer encoder to generate high-dimensional representations of the normalized patches, and a limited number of samples from the target time-series domain are utilized to learn the prompt specific to the target domain. Cosine similarity between the prompt of the target domain and the prompts of source domains is calculated to identify a nearest neighbor prompt, which is utilized for model prediction in the target time-series domain.
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公开(公告)号：US20240419801A1
公开(公告)日：2024-12-19
申请号：US18731845
申请日：2024-06-03
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Xianjun Yang , Haifeng Chen
Abstract: Systems and methods for detecting artificial intelligence (AI) generated computer code. Lines of code can be masked from a candidate code to obtain perturbed codes. Missing code can be generated from the perturbed codes by employing an AI code generator model to obtain machine-filled codes. Probabilities of the candidate code probability and the machine-filled codes as AI-generated can be predicted by employing a surrogate model. The candidate code can be distinguished as AI-generated by comparing the probabilities against a detection threshold to obtain detection results.
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5.发明公开公开(公告)号：US20240160955A1
公开(公告)日：2024-05-16
申请号：US18503517
申请日：2023-11-07
Applicant: NEC Laboratories America, Inc.
Inventor： Xujiang Zhao , Yanchi Liu , Wei Cheng , Haifeng Chen
IPC: G06N5/02 , G06F16/332
CPC classification number: G06N5/02 , G06F16/3329
Abstract: A computer-implemented method for optimized decision making that includes labeling text data extracted from an inquiry, and linking labeled text to a knowledge graph entity. The method may further include retrieving from the knowledge graph reasoning paths; and removing irrelevant knowledge graph reasoning paths using a language model trained artificial intelligence consistent with the labeling of the text data. The method may further include employing remaining relevant graph reasoning paths to provide an answer prediction.
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Patent Agency Ranking
公开(公告)号：US20240070232A1
公开(公告)日：2024-02-29
申请号：US18452664
申请日：2023-08-21
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Jingchao Ni , Liang Tong , Haifeng Chen , Yizhou Zhang
IPC: G06F18/2413 , G06F18/2415 , H04B10/69
CPC classification number: G06F18/24133 , G06F18/2415 , H04B10/697
Abstract: Methods and systems for training a model include determining class prototypes of time series samples from a training dataset. A task corresponding to the time series samples is encoded using the class prototypes and a task-level configuration. A likelihood value is determined based on outputs of a time series density model, a task-class distance from a task embedding model, and a task density model. Parameters of the time series density model, the task embedding model, and the task density model are adjusted responsive to the likelihood value.
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公开(公告)号：US20240062043A1
公开(公告)日：2024-02-22
申请号：US18364746
申请日：2023-08-03
Applicant: NEC Laboratories America, Inc.
Inventor： Liang Tong , Zhengzhang Chen , Wei Cheng , Haifeng Chen , Zhuohang Li
IPC: G06N3/0455 , G06N3/08
CPC classification number: G06N3/0455 , G06N3/08
Abstract: A computer-implemented method for employing a graph-based adaptive domain generation framework is provided. The method includes, in a training phase, performing domain prototypical network training on source domains, constructing an autoencoding domain relation graph by applying a graph autoencoder to produce domain node embeddings, and performing, via a domain-adaptive classifier, domain-adaptive classifier training to make an informed decision. The method further includes, in a testing phase, given testing samples from a new source domain, computing a prototype by using a pretrained domain prototypical network, inferring node embedding, and making a prediction by the domain-adaptive classifier based on the domain node embeddings.
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公开(公告)号：US20240054373A1
公开(公告)日：2024-02-15
申请号：US18471570
申请日：2023-09-21
Applicant: NEC Laboratories America, Inc.
Inventor： Wenchao Yu , Wei Cheng , Haifeng Chen , Yuncong Chen , Xuchao Zhang , Tianxiang Zhao
Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.
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公开(公告)号：US20240046091A1
公开(公告)日：2024-02-08
申请号：US18484793
申请日：2023-10-11
Applicant: NEC Laboratories America, Inc.
Inventor： Wenchao Yu , Wei Cheng , Haifeng Chen , Yiwei Sun
Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.
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10.发明公开公开(公告)号：US20240037397A1
公开(公告)日：2024-02-01
申请号：US18479385
申请日：2023-10-02
Applicant: NEC Laboratories America, Inc.
Inventor： Jingchao Ni , Zhengzhang Chen , Wei Cheng , Bo Zong , Haifeng Chen
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.
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