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1.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
发明人： Xujiang Zhao , Yanchi Liu , Wei Cheng , Haifeng Chen
IPC分类号： G06N5/02 , G06F16/332
CPC分类号： G06N5/02 , G06F16/3329
摘要： 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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公开(公告)号：US20240070232A1
公开(公告)日：2024-02-29
申请号：US18452664
申请日：2023-08-21
发明人： Wei Cheng , Jingchao Ni , Liang Tong , Haifeng Chen , Yizhou Zhang
IPC分类号： G06F18/2413 , G06F18/2415 , H04B10/69
CPC分类号： G06F18/24133 , G06F18/2415 , H04B10/697
摘要： 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
发明人： Liang Tong , Zhengzhang Chen , Wei Cheng , Haifeng Chen , Zhuohang Li
IPC分类号： G06N3/0455 , G06N3/08
CPC分类号： G06N3/0455 , G06N3/08
摘要： 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
发明人： Wenchao Yu , Wei Cheng , Haifeng Chen , Yuncong Chen , Xuchao Zhang , Tianxiang Zhao
摘要： 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
发明人： Wenchao Yu , Wei Cheng , Haifeng Chen , Yiwei Sun
摘要： 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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6.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
发明人： Jingchao Ni , Zhengzhang Chen , Wei Cheng , Bo Zong , Haifeng Chen
摘要： 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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7.发明公开公开(公告)号：US20240028897A1
公开(公告)日：2024-01-25
申请号：US18479326
申请日：2023-10-02
发明人： Jingchao Ni , Zhengzhang Chen , Wei Cheng , Bo Zong , Haifeng Chen
摘要： 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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公开(公告)号：US20240013920A1
公开(公告)日：2024-01-11
申请号：US18370074
申请日：2023-09-19
发明人： Jingchao Ni , Wei Cheng , Haifeng Chen , Takayoshi Asakura
摘要： 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.
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公开(公告)号：US20240006069A1
公开(公告)日：2024-01-04
申请号：US18370049
申请日：2023-09-19
发明人： Jingchao Ni , Wei Cheng , Haifeng Chen , Takayoshi Asakura
摘要： 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.
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公开(公告)号：US11842271B2
公开(公告)日：2023-12-12
申请号：US17003112
申请日：2020-08-26
发明人： Yanchi Liu , Wei Cheng , Bo Zong , LuAn Tang , Haifeng Chen , Denghui Zhang
摘要： Methods and systems for allocating network resources responsive to network traffic include modeling spatial correlations between fine spatial granularity traffic and coarse spatial granularity traffic for different sites and regions to determine spatial feature vectors for one or more sites in a network. Temporal correlations at a fine spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. Temporal correlations at a coarse spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. A traffic flow prediction is determined for the one or more sites in the network, based on the temporal correlations at the fine spatial granularity and the temporal correlations at the coarse spatial granularity. Network resources are provisioned at the one or more sites in accordance with the traffic flow prediction.
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