公开(公告)号：US20250062953A1

公开(公告)日：2025-02-20

申请号：US18800726

申请日：2024-08-12

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Haoyu Wang ,  Xujiang Zhao ,  Chengyuan Deng

IPC: H04L41/08

Abstract: Systems and methods for correlation-aware explainable online change point detection. Collected data metrics from the cloud system can be transformed to correlation matrices. Correlation shifts from the correlation matrices can be captured as differences of correlation between batches of collected data metrics through determined statistics of the batches of collected data metrics across timesteps. Change points in the cloud system can be detected based on the correlation shifts to obtain detected change points. System maintenance can be performed autonomously based on the detected change points from identified system entities to optimize the cloud system with an updated configuration.

公开(公告)号：US20250148540A1

公开(公告)日：2025-05-08

申请号：US18620099

申请日：2024-03-28

Applicant: NEC Laboratories America, Inc.

Inventor： LuAn Tang ,  Haoyu Wang ,  Haifeng Chen ,  Wenchao Yu ,  Zhengzhang Chen

IPC: G06Q40/08

Abstract: Systems and methods are provided for classifying components include monitoring sensors to collect sensor data related to a state of a plurality of components; processing, by a computing system, the sensor data to generate an action sequence using a transformer-based policy network for each of the components. A risk score is generated for the action sequence using a Generative Adversarial Network (GAN), wherein the GAN includes a generator for generating action sequences and a discriminator to distinguish low-risk action sequences in accordance with a threshold. The low-risk action sequences are associated with components in the plurality of components based on the risk score. A status of the low-risk action sequences is communicated to the components.

公开(公告)号：US20250148292A1

公开(公告)日：2025-05-08

申请号：US18620125

申请日：2024-03-28

Applicant: NEC Laboratories America, Inc.

Inventor： LuAn Tang ,  Haoyu Wang ,  Haifeng Chen ,  Wenchao Yu ,  Zhengzhang Chen

IPC: G06N3/094 ,  G06N3/0455

Abstract: Systems and methods train a transformer-based policy network and Generative Adversarial Network (GAN) by initializing a transformer-based policy network to model action sequences by encoding temporal dependencies within sensor data. Multi-head self-attention mechanisms process sequential sensor inputs by being pre-trained on a labeled dataset having sensor data from known low-risk action sequences. A generator within the GAN is trained to produce generated action sequences, which mimic behavior of low-risk action sequences. A discriminator within the GAN is concurrently trained to differentiate between action sequences derived from the labeled dataset and synthetic action sequences produced by the generator. A feedback loop is employed to adjust parameters to produce sequences indistinguishable from real low-risk action sequences. Risk scores are generated and low-risk action sequences are identified upon reaching a predetermined threshold for accuracy in distinguishing between real and synthetic action sequences.

公开(公告)号：US20250077848A1

公开(公告)日：2025-03-06

申请号：US18817793

申请日：2024-08-28

Applicant: NEC Laboratories America, Inc.

Inventor： Xujiang Zhao ,  Haoyu Wang ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Yanchi Liu ,  Chen Ling

IPC: G06N3/0475 ,  G16H80/00

Abstract: Systems and methods for a demonstration uncertainty-based artificial intelligence model for open information extraction. A large language model (LLM) can generate initial structured sentences using an initial prompt for a domain-specific instruction extracted from an unstructured text input. Structural similarities between the initial structured sentences and sentences from a training dataset can be determined to obtain structurally similar sentences. The LLM can identify relational triplets from combinations of tokens from generated sentences using and the structurally similar sentences. The relational triplets can be filtered based on a calculated demonstration uncertainty to obtain a filtered triplet list. A domain-specific task can be performed using the filtered triplet list to assist the decision-making process of a decision-making entity.

公开(公告)号：US20250149133A1

公开(公告)日：2025-05-08

申请号：US18922837

申请日：2024-10-22

Applicant: NEC Laboratories America, Inc.

Inventor： LuAn Tang ,  Yuyang Ye ,  Haifeng Chen ,  Haoyu Wang ,  Zhengzhang Chen ,  Wenchao Yu

IPC: G16H10/60 ,  G06N3/0455

Abstract: Systems and methods for optimizing key performance indicators (KPIs) using adversarial imitation deep learning include processing sensor data received from sensors to remove irrelevant data based on correlation to a final KPI and generating, using a policy generator network with a transformer-based architecture, an optimal sequence of actions based on the processed sensor data. A discriminator network is employed to differentiate between the generated action sequences and real-world high performance sequences employing. Final KPI results are estimated based on the generated action sequences using a performance prediction network. The generated action sequences are applied to the process to optimize the KPI in real-time.

公开(公告)号：US20250148431A1

公开(公告)日：2025-05-08

申请号：US18938823

申请日：2024-11-06

Applicant: NEC Laboratories America, Inc.

Inventor： Haoyu Wang ,  Christopher A. White ,  Haifeng Chen ,  LuAn Tang ,  Zhengzhang Chen ,  Xujiang Zhao

IPC: G06Q10/30 ,  G06Q10/0637

Abstract: Systems and methods for an agent-based carbon emission reduction system. A carbon product of a supply chain system can be limited below a carbon product threshold by performing a corrective action to monitored entities based on a calculated carbon emission. The carbon emission can be calculated based on carbon-relevant data and a calculation route by utilizing an agent-based simulation model that simulates a learned relationship between a supply chain system and the carbon-relevant data. The calculation route can be determined based on the carbon-relevant data based on a relevance of a carbon product contribution of monitored entities to a goal of the monitored entities. Carbon-relevant data can be extracted from the monitored entities.

公开(公告)号：US20240379200A1

公开(公告)日：2024-11-14

申请号：US18649145

申请日：2024-04-29

Applicant: NEC Laboratories America, Inc.

Inventor： Xujiang Zhao ,  Haifeng Chen ,  Wei Cheng ,  Yanchi Liu ,  Zhengzhang Chen ,  Haoyu Wang

IPC: G16H10/60 ,  G06F40/40

Abstract: Methods and systems for information extraction include configuring a language model with an information extraction instruction prompt and at least one labeled example prompt. Configuration of the language model is validated using at least one validation prompt. Errors made by the language model in response to the at least one validation prompt are corrected using a correction prompt. Information extraction is performed on an unlabeled sentence using the language model to identify a relation from the unlabeled sentence. An action is performed responsive to the identified relation.