NEC Laboratories America, Inc. patent applications on May 8th, 2025
Patent Applications by NEC Laboratories America, Inc. on May 8th, 2025
NEC Laboratories America, Inc.: 31 patent applications
NEC Laboratories America, Inc. has applied for patents in the areas of G01H9/00 (7), B60W60/00 (3), G01D5/353 (3), G06N3/0455 (2), G06N3/094 (2) G01H9/004 (5), G06N3/094 (2), B60W60/001 (1), G06T15/08 (1), H02J3/0012 (1)
With keywords such as: data, methods, systems, action, sensing, fiber, based, sequences, generated, and optical in patent application abstracts.
Patent Applications by NEC Laboratories America, Inc.
Inventor(s): Manmohan Chandraker of Santa Clara CA US for nec laboratories america, inc., Francesco Pittaluga of Los Angeles CA US for nec laboratories america, inc., Vijay Kumar Baikampady Gopalkrishna of Santa Clara CA US for nec laboratories america, inc., Sharan Satish Prema of Austin TX US for nec laboratories america, inc.
IPC Code(s): B60W60/00, B60W10/04, B60W10/18, B60W10/20, G05D1/43, G06F8/35, G06F11/34
CPC Code(s): B60W60/001
Abstract: methods and systems for operating a vehicle include prompting a large language model llm to generate parameters for a rule-based planner based on historical data for vehicles in a road scene. a trajectory is generated using the parameters. a driving action is performed to implement the trajectory.
Inventor(s): Yangmin Ding of East Brunswick NJ US for nec laboratories america, inc., Ting Wang of West Windsor NJ US for nec laboratories america, inc., Yue Tian of Princeton NJ US for nec laboratories america, inc., Sarper Ozharar of Pennington NJ US for nec laboratories america, inc., Zhuocheng Jiang of Plainsboro NJ US for nec laboratories america, inc., Yanchao Wang of Princeton NJ US for nec laboratories america, inc.
IPC Code(s): G01D5/353
CPC Code(s): G01D5/35358
Abstract: methods and systems for anomaly detection include measuring time-series data about a system using an optical sensing system. the time-series data is adapted to natural language data. one or more anomaly detection models are selected based on the natural language data and a task. an anomaly is detected in the system using the selected one or more anomaly detection models. a corrective action is performed responsive to the anomaly.
20250146843. INLINE FIBER TYPE IDENTIFICATION_simplified_abstract_(nec laboratories america, inc.)
Inventor(s): Ezra Ip of West Windsor NJ US for nec laboratories america, inc., Yue-Kai Huang of Princeton NJ US for nec laboratories america, inc., Giacomo Borraccini of Princeton NJ US for nec laboratories america, inc.
IPC Code(s): G01D5/353
CPC Code(s): G01D5/35364
Abstract: methods and systems for fiber identification include emitting a pump pulse on a fiber using a transponder. a brillouin gain spectrum of reflected radiation from the pump pulse is measured using the transponder. a fiber type is determined corresponding to the brillouin gain spectrum.
Inventor(s): Zhuocheng Jiang of Plainsboro NJ US for nec laboratories america, inc., Yue Tian of Princeton NJ US for nec laboratories america, inc., Yangmin Ding of East Brunswick NJ US for nec laboratories america, inc., Sarper Ozharar of Pennington NJ US for nec laboratories america, inc., Yanchao Wang of Princeton NJ US for nec laboratories america, inc., Ting Wang of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G01H9/00, G01R31/62
CPC Code(s): G01H9/004
Abstract: systems and methods include collecting vibration data along an optical fiber cable using distributed acoustic sensing (das). the collected vibration data is preprocessed to separate the vibration data into at least two mixtures. the at least two mixtures are combined into a mixture of mixtures. the mixture of mixtures is separated into a plurality of estimated source signals using a separation model. the separation model is trained using an unsupervised loss computed between the estimated source signals and the at least two mixtures.
Inventor(s): Ming-Fang Huang of Princeton NJ US for nec laboratories america, inc., Ting Wang of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G01H9/00, G01D5/353, H04B10/079
CPC Code(s): G01H9/004
Abstract: systems and methods for cable inspection using optical fiber sensing includes a hardware processor and a memory storing a computer program which, when executed by the hardware processor, causes the hardware processor to collect data from a fiber optic cable and analyze the data with a distributed fiber optic sensing (dfos) system. losses and anomalies and their locations are identified in the cable. an alert is generated based on the losses and anomalies.
Inventor(s): Jian Fang of Princeton NJ US for nec laboratories america, inc., Wataru Kohno of Princeton NJ US for nec laboratories america, inc., Ting Wang of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G01H9/00
CPC Code(s): G01H9/004
Abstract: methods and systems for acoustic sensing include determining sensing locations along a fiber to generate a beam pattern that is directed to an acoustic source. an optical pulse is transmitted on the fiber. optical phase of backscattering light is measured from the sensing locations on the fiber. an output signal is generated by combining the measured optical phase according to the beam pattern.
Inventor(s): Yuheng Chen of South Brunswick NJ US for nec laboratories america, inc., Azita Nouri of Highland Park NJ US for nec laboratories america, inc.
IPC Code(s): G01H9/00
CPC Code(s): G01H9/004
Abstract: methods and systems for sensing include selecting sensing locations along an optical fiber. reflections from an optical pulse are measured on the optical fiber to generate a line of raw sensing data. the line of raw sensing data is compressed in accordance with present network conditions to generate compressed sensing data. the compressed sensing data is transmitted to a client.
Inventor(s): Ming-Fang HUANG of Princeton NJ US for nec laboratories america, inc., Yuheng CHEN of South Brunswick NJ US for nec laboratories america, inc., Philip JI of Cranbury NJ US for nec laboratories america, inc.
IPC Code(s): G01H9/00, G01V8/16
CPC Code(s): G01H9/004
Abstract: disclosed are integrated systems and methods employing distributed fiber optic sensing (dfos) systems and methods to locate buried and/or aerial cables, as well as loop-back aerial cable sections and slack fiber lengths, in real-time. in sharp contrast to the prior art, systems and methods according to aspects of the present disclosure provide the location of cables without necessitating the opening of manholes/hand holes or pull cables to the roundâthereby making the overall process faster, more cost-effective and more accurate and precise. our inventive systems and methods provide a reliable and accurate alternative to current methods utilizing optical time domain reflectometry (otdr), which requires known and accessible locations and may be ineffective for legacy fibers. by implementing systems and methods according to the present disclosure, service providers, carriers, and owners can efficiently maintain optical fiber networks and ensure reliable services for users.
Inventor(s): Yangmin DING of EAST BRUNSWICK NJ US for nec laboratories america, inc., Ting WANG of WEST WINDSOR NJ US for nec laboratories america, inc.
IPC Code(s): G01S5/22, G01S5/20
CPC Code(s): G01S5/22
Abstract: integrated dfos systems and methods for 3d gunshot, localization, and tracking utilizing artificial intelligence enhanced (ai-enhanced) systems and methods for infrastructure security including electrical substations. our systems and methods provide a comprehensive solution for substation security enhancement, integrating 3d gunshot localization, real-time tracking, and ai-driven analysis. utilizing distributed acoustic sensing (das) technology, our systems and methods precisely detect and triangulate the origin of gunshots in three-dimensional space. the trajectory of a bullet is determined, providing insights into the direction and potential target within the substation. al algorithms discern between various acoustic events and provide identification of genuine threats. upon detecting a potential gunshot, our system automatically correlates related acoustic events, such as the noise of a nearby vehicle, offering context and aiding in threat assessment. our ai-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Inventor(s): Yaowen LI of Princeton NJ US for nec laboratories america, inc., Hao WANG of West Windsor NJ US for nec laboratories america, inc., Yuheng CHEN of South Brunswick NJ US for nec laboratories america, inc., Ting Wang of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G01S7/481, G01S7/4865, G01S17/894
CPC Code(s): G01S7/4818
Abstract: disclosed are displacement sensors constructed from optical fibers having a long elongation with low-cost tof sensors that advantageously do not suffer the infirmities of the art. the tof sensor and the optical fiber ends that launch and receive light are packaged such that no ambient light affects measurements, and the structure is protected from contamination which eliminates optical degradation. with multi-point measurement capabilities and the low-cost features of tof sensors, many displacement sensors can be arranged in a mesh to map out displacements over a large area and over all directions for civil and/or geotechnical structures. wireless or other communications mechanisms may be employed in conjunction with our novel sensors to send real time measurement data to a central office for real time monitoring and analysis.
Inventor(s): Yangmin DING of East Brunswick NJ US for nec laboratories america, inc., Ting WANG of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G06F16/332, G06F16/34
CPC Code(s): G06F16/3329
Abstract: disclosed are integrated systems and methods providing intelligent anomaly detection for dfos systems and applications, the systems and methods utilizing a natural language processing model, such as chatgpt, to generate real-time alerts with actionable recommendations and potential consequences based on detected anomalies. our innovative solutionâoptisensegptâsolves problems left uncured by traditional methods by delivering easily understandable alerts in natural language, enabling timely response by relevant personnel. our integrated optisensegpt systems and methods disclosed provide context-aware recommendations and consequences, enhancing decision-making and improving overall performance and safety of a monitored infrastructure or environment. our optisensegpt systems and methods advantageously provide integration of natural language processing; context-aware recommendations; presentation of potential consequences; adaptability and customization; and seamless integration.
Inventor(s): Shaobo Han of Princeton NJ US for nec laboratories america, inc., Tingfeng Li of Plainsboro NJ US for nec laboratories america, inc., Renqiang Min of Princeton NJ US for nec laboratories america, inc.
IPC Code(s): G06N3/08
CPC Code(s): G06N3/08
Abstract: systems and methods include collecting real-world distributed-optic fiber sensing (dfos) sensing data from a target environment as a reference dataset. a synthetic sketch dataset is constructed as a parameterized computer program. a synthetic waterfall is generated from a deep neural network as an image translator from the sketch waterfall with nonlinear distortions and background noises added. parameters are optimized for generating the synthetic waterfall under a loss function where the loss function encodes a generalization performance on the real-world dataset and encodes granularities from a sensing process and uncontrollable factors.
Inventor(s): LuAn Tang of Cranbury NJ US for nec laboratories america, inc., Haoyu Wang of Plainsboro NJ US for nec laboratories america, inc., Haifeng Chen of West Windsor NJ US for nec laboratories america, inc., Wenchao Yu of Plainsboro NJ US for nec laboratories america, inc., Zhengzhang Chen of Princeton Junction NJ US for nec laboratories america, inc.
IPC Code(s): G06N3/094, G06N3/0455
CPC Code(s): G06N3/094
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.
Inventor(s): Junxiang Wang of Plainsboro NJ US for nec laboratories america, inc., Wei Cheng of Princeton Junction NJ US for nec laboratories america, inc., Haifeng Chen of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G06N3/094
CPC Code(s): G06N3/094
Abstract: methods and systems include adapting an initial prompt to a target domain corresponding to an input time series to generate an adapted prompt. the adapted prompt and the input time series are combined. the input time series is processed with the adapted prompt using a modular transformer encoder that has a plurality of sub-encoders, with a policy network selecting a subset of the plurality of encoders that are applied to the input time series and the adapted prompt.
Inventor(s): Philip Ji of Cranbury NJ US for nec laboratories america, inc., Shaobo Han of Princeton NJ US for nec laboratories america, inc., Ting Wang of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G06N3/096, G01H9/00
CPC Code(s): G06N3/096
Abstract: systems and methods include calibrating physical parameters of acoustic data using a deterministic model related to hardware configurations that generated the acoustic data to provide an intermediate layer of data. the intermediate layer of data is then calibrated using environmental factors related to the acoustic data by employing machine learning to provide a multichannel data output. a loss is optimized between the multichannel data output and multichannel distributed-optic fiber sensing (dfos) data to train a hybrid transfer model to translate between dfos data and acoustic data.
Inventor(s): Yangmin DING of East Brunswick NJ US for nec laboratories america, inc., Ting WANG of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G06Q10/0631
CPC Code(s): G06Q10/06315
Abstract: disclosed are integrated systems and methods that utilize a network of dfos sensors installed on power lines, communication networks, and transportation systems, which continuously monitor infrastructures and provide real-time data. this data is collected, processed, and analyzed, and used to identify any affected areas of infrastructure and assess severity of any damage. prioritization and resource allocation is performed and uses this analysis to prioritize restoration efforts and allocate resources such as repair crews, equipment, and materials to areas with a highest priority.
Inventor(s): Yangmin DING of East Brunswick NJ US for nec laboratories america, inc., Ting WANG of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): G06Q10/20
CPC Code(s): G06Q10/20
Abstract: disclosed is a deep learning and language model enhanced system and method for wind turbine monitoring using distributed fiber optic sensing (dl-lm-dfos) which combines advantages of distributed fiber optic sensing with the power of deep learning and large language models. our system and method automatically learns and extracts useful features from raw sensor data, detects complex patterns indicating potential issues, and incorporates and learns from a wide range of data, including textual data such as maintenance logs, operational notes, or alarm messages. as a result, our inventive system and method provide comprehensive, efficient, and predictive monitoring of wind turbines.
Inventor(s): Haoyu Wang of Plainsboro NJ US for nec laboratories america, inc., Christopher A. White of Neshanic Station NJ US for nec laboratories america, inc., Haifeng Chen of West Windsor NJ US for nec laboratories america, inc., LuAn Tang of Cranbury NJ US for nec laboratories america, inc., Zhengzhang Chen of Princeton Junction NJ US for nec laboratories america, inc., Xujiang Zhao of Hillsborough NJ US for nec laboratories america, inc.
IPC Code(s): G06Q10/30, G06Q10/0637
CPC Code(s): G06Q10/30
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.
Inventor(s): LuAn Tang of Cranbury NJ US for nec laboratories america, inc., Haoyu Wang of Plainsboro NJ US for nec laboratories america, inc., Haifeng Chen of West Windsor NJ US for nec laboratories america, inc., Wenchao Yu of Plainsboro NJ US for nec laboratories america, inc., Zhengzhang Chen of Princeton Junction NJ US for nec laboratories america, inc.
IPC Code(s): G06Q40/08
CPC Code(s): 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.
Inventor(s): Deep Patel of Franklin Park NJ US for nec laboratories america, inc., Iain Melvin of Princeton NJ US for nec laboratories america, inc., Alexandru Niculescu-Mizil of Plainsboro NJ US for nec laboratories america, inc.
IPC Code(s): G06T7/292, G06T7/00, G06T7/70
CPC Code(s): G06T7/292
Abstract: systems and methods for a multi-entity tracking transformer model (mctr). to train the mctr, processing track embeddings and detection embeddings of video feeds obtained from multiple cameras to generate updated track embeddings with a tracking module. the updated track embeddings can be associated with the detection embeddings to generate track-detection associations (tda) for each camera view and camera frame with an association module. a cost module can calculate a differentiable loss from the tda by combining a detection loss, a track loss and an auxiliary track loss. a model trainer can train the mctr using the differentiable loss and contiguous video segments sampled from a training dataset to track multiple objects with multiple cameras.
Inventor(s): Ziyu Jiang of Sunnyvale CA US for nec laboratories america, inc., Bingbing Zhuang of Santa Clara CA US for nec laboratories america, inc., Manmohan Chandraker of Santa Clara CA US for nec laboratories america, inc.
IPC Code(s): G06T15/08
CPC Code(s): G06T15/08
Abstract: methods and systems include training a model for rendering a three-dimensional volume using a loss function that includes a depth loss term and a distribution loss term that regularize an output of the model to produce realistic scenarios. a simulated scenario is generated based on an original scenario, with the simulated scenario including a different position and pose relative to the original scenario in a three-dimensional (3d) scene that is generated by the model from the original scenario. a self-driving model is trained for an autonomous vehicle using the simulated scenario.
Inventor(s): Bingbing Zhuang of Santa Clara CA US for nec laboratories america, inc., Ziyu Jiang of Sunnyvale CA US for nec laboratories america, inc., Manmohan Chandraker of Santa Clara CA US for nec laboratories america, inc., Shanlin Sun of Irvine CA US for nec laboratories america, inc.
IPC Code(s): G06T19/20, G06T1/20, G06T7/246, G06T7/90, G06T15/00, G06T15/06, G06T15/20, G06T15/50
CPC Code(s): G06T19/20
Abstract: a computer-implemented method for synthesizing an image includes extracting agent neural radiance fields (nerfs) from driving video logs and storing agent nerfs in a database. for a driving video log to be edited, a scene nerf and agent nerfs are extracted from the driving video log to be edited. one or more agent nerfs are selected from the database to insert into or replace existing agents in a traffic scene of the driving video log based on photorealism criteria. the traffic scene is edited by inserting a selected agent nerf into the traffic scene, replacing existing agents in the traffic scene with the selected agent nerf, or removing one or more existing agents from the traffic scene. an image of the edited traffic scene is synthesized by composing edited agent nerfs with the scene nerf and performing volume rendering.
Inventor(s): Jong-Chyi Su of San Francisco CA US for nec laboratories america, inc., Sparsh Garg of Fremont CA US for nec laboratories america, inc., Samuel Schulter of Long Island City NY US for nec laboratories america, inc., Manmohan Chandraker of Santa Clara CA US for nec laboratories america, inc., Mingfu Liang of Evanston IL US for nec laboratories america, inc.
IPC Code(s): G06V10/764, B60W60/00, G06T11/00, G06V10/25, G06V10/44, G06V10/74, G06V20/70
CPC Code(s): G06V10/764
Abstract: systems and methods for a self-improving data engine for autonomous vehicles is presented. to train the self-improving data engine for autonomous vehicles (side), multi-modality dense captioning (mmdc) models can detect unrecognized classes from diversified descriptions for input images. a vision-language-model (vlm) can generate textual features from the diversified descriptions and image features from corresponding images to the diversified descriptions. curated features, including curated textual features and curated image features, can be obtained by comparing similarity scores between the textual features and top-ranked image features based on their likelihood scores. generate annotations, including bounding boxes and labels, can be generated for the curated features by comparing the similarity scores of labels generated by a zero-shot classifier and the curated textual features. the side can be trained using the curated features, annotations, and feedback.
Inventor(s): Vijay Kumar Baikampady Gopalkrishna of Santa Clara CA US for nec laboratories america, inc., Masoud Faraki of Redwood City CA US for nec laboratories america, inc., Yumin Suh of Santa Clara CA US for nec laboratories america, inc., Manmohan Chandraker of Santa Clara CA US for nec laboratories america, inc.
IPC Code(s): G06V10/774, B60W60/00, G06F40/284, G06F40/30, G06V10/86, G06V20/56
CPC Code(s): G06V10/774
Abstract: systems and methods for leveraging semantic information for a multi-domain visual agent. semantic information can be leveraged to obtain a multi-domain visual agent. to train the multi-domain visual agent, questions can be sampled from question templates for domain-specific label spaces to obtain a unified label space. the domain-specific labels from the domain-specific label spaces can be mapped into natural language descriptions (nld) to obtain mapped nld. the mapped nld can be converted into prompts by combining the questions sampled from the unified label space and the annotations. the semantic information can be learned by iteratively generating outputs from tokens extracted from the prompts using a large-language model (llm). the multi-domain visual agent (mdva) can be trained using the semantic information.
20250148768. OPEN VOCABULARY ACTION DETECTION_simplified_abstract_(nec laboratories america, inc.)
Inventor(s): Kai Li of Plainsboro NJ US for nec laboratories america, inc., Deep Patel of Franklin Park NJ US for nec laboratories america, inc., Renqiang Min of Princeton NJ US for nec laboratories america, inc., Wentao Bao of Princeton NJ US for nec laboratories america, inc.
IPC Code(s): G06V10/80, G06V10/82, G06V20/40, G06V20/58, G06V40/20
CPC Code(s): G06V10/806
Abstract: methods and systems for action detection include encoding a text feature of an input textual description of an action using a visual language model (vlm). a video feature of an input video is encoded using the vlm. the action in the video is recognized, based on the text feature and the video feature, to localize the action within the video. a person performing the action is located within the video using the vlm.
Inventor(s): Eric Cosatto of Red Bank NJ US for nec laboratories america, inc., Evgenia Tatiani Chroni of Highland Park NJ US for nec laboratories america, inc.
IPC Code(s): G06V20/69, G06T7/00, G16H30/40, G16H50/20
CPC Code(s): G06V20/698
Abstract: systems and methods for gradient-to-parameter ratio guided feature alignment for model adaptation. to adapt an artificial intelligence (ai) model to different domains, activation statistics for the ai model can be computed from collected domain data. weights of the ai model can be adjusted based on the activation statistics of the training gradients. the ai model can be fine-tuned by focusing adaptation intensity to layers with attention mechanism by using a ratio of gradient norm over parameter norm to obtain a fine-tuned ai model. the fine-tuned ai model can be employed to perform downstream tasks such as cell segmentation from medical images.
Inventor(s): Yangmin DING of East Brunswick NJ US for nec laboratories america, inc., Ting WANG of West Windsor NJ US for nec laboratories america, inc., Yue TIAN of Princeton NJ US for nec laboratories america, inc., Sarper OZHARAR of Pennington NJ US for nec laboratories america, inc., Zhuocheng JIANG of Plainsboro NJ US for nec laboratories america, inc., Yanchao WANG of Princeton NJ US for nec laboratories america, inc.
IPC Code(s): G08B25/14, G01H9/00, G06V20/52
CPC Code(s): G08B25/14
Abstract: disclosed are integrated systems and operating methods that provide an integrated security system for substation monitoring and detection that effectively combines the strengths of distributed acoustic sensing, drones, and security cameras for comprehensive protection. the integrated system comprises a das system configured to monitor vibrations and acoustic signals along the length of fiber optic cables, one or more drones equipped with advanced sensors for aerial surveillance, and a plurality of security cameras installed throughout the substation to capture real-time video feeds and provide visual confirmation of activities. a central control system integrates and analyzes data from the das system, drones, and security cameras, and utilizes a novel, advanced algorithm, named substation security analytics (ssa), specifically designed for the unique challenges associated with substation security monitoring and detection.
Inventor(s): Manmohan Chandraker of Santa Clara CA US for nec laboratories america, inc., Francesco Pittaluga of Los Angeles CA US for nec laboratories america, inc., Bingbing Zhuang of Santa Clara CA US for nec laboratories america, inc., Wei-Jer Chang of Fremont CA US for nec laboratories america, inc.
IPC Code(s): G08G1/0967, G06N20/00, G08G1/01, G08G1/16
CPC Code(s): G08G1/096725
Abstract: methods and systems include determining actions for agents in a driving scenario using a diffusion model, based on individual controllable behavior patterns for the agents. a state of the driving scenario is updated based on the determined actions for the plurality of agents. the determination of actions and the update of the state are repeated in a closed-loop fashion to generate simulated trajectories for the plurality of agents. a planner model is trained to select actions for an operating agent based on the simulated trajectories.
Inventor(s): LuAn Tang of Cranbury CA US for nec laboratories america, inc., Yuyang Ye of Harrison NJ US for nec laboratories america, inc., Haifeng Chen of West Windsor NJ US for nec laboratories america, inc., Haoyu Wang of Plainsboro NJ US for nec laboratories america, inc., Zhengzhang Chen of Princeton Junction NJ US for nec laboratories america, inc., Wenchao Yu of Plainsboro NJ US for nec laboratories america, inc.
IPC Code(s): G16H10/60, G06N3/0455
CPC Code(s): G16H10/60
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.
Inventor(s): Yangmin Ding of East Brunswick NJ US for nec laboratories america, inc., Ting Wang of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): H02J3/00, G06F18/25, G06N3/08
CPC Code(s): H02J3/0012
Abstract: systems and methods for a risk mitigation system for electrical power grids. to mitigate risks such as natural destructive forces, collected risk data and epg data can be fused to obtain fused data. the vulnerability metric and fragility metric of the epg based on risk profiles generated from the fused data can be predicted with a physics-informed neural network (pinn) trained with the fused data. epg threat metrics can be developed by integrating the vulnerability metric, fragility metric, and the risk profiles into an integrated score that determines the probability of failure of the epg caused by natural destructive forces. the present embodiments can perform a corrective action with an automated helper to mitigate the risks to the epg caused by the natural destructive forces determined from the epg threat metrics.
Inventor(s): Zhuocheng JIANG of PLAINSBORO NJ US for nec laboratories america, inc., Yue TIAN of PRINCETON NJ US for nec laboratories america, inc., Yangmin DING of East Brunswick NJ US for nec laboratories america, inc., Sarper OZHARAR of Pennington NJ US for nec laboratories america, inc., Yanchao WANG of Princeton NJ US for nec laboratories america, inc., Wataru Kohno of Princeton NJ US for nec laboratories america, inc., Ting Wang of West Windsor NJ US for nec laboratories america, inc.
IPC Code(s): H02J13/00
CPC Code(s): H02J13/00002
Abstract: disclosed are systems and methods directed to a frequency analysis approach to transformer status monitoring using distributed fiber optic sensing to monitor a phase delay of a 120 hz vibrational signal and determining an angular difference between a designated point and a reference point. operationally, systems and methods according to aspects of the present disclosure identify phase delay patterns of a single transformer from its vibrational humming and combined vibrational signals of multiple transformers.
NEC Laboratories America, Inc. patent applications on May 8th, 2025
- NEC Laboratories America, Inc.
- B60W60/00
- B60W10/04
- B60W10/18
- B60W10/20
- G05D1/43
- G06F8/35
- G06F11/34
- CPC B60W60/001
- Nec laboratories america, inc.
- G01D5/353
- CPC G01D5/35358
- CPC G01D5/35364
- G01H9/00
- G01R31/62
- CPC G01H9/004
- H04B10/079
- G01V8/16
- G01S5/22
- G01S5/20
- CPC G01S5/22
- G01S7/481
- G01S7/4865
- G01S17/894
- CPC G01S7/4818
- G06F16/332
- G06F16/34
- CPC G06F16/3329
- G06N3/08
- CPC G06N3/08
- G06N3/094
- G06N3/0455
- CPC G06N3/094
- G06N3/096
- CPC G06N3/096
- G06Q10/0631
- CPC G06Q10/06315
- G06Q10/20
- CPC G06Q10/20
- G06Q10/30
- G06Q10/0637
- CPC G06Q10/30
- G06Q40/08
- CPC G06Q40/08
- G06T7/292
- G06T7/00
- G06T7/70
- CPC G06T7/292
- G06T15/08
- CPC G06T15/08
- G06T19/20
- G06T1/20
- G06T7/246
- G06T7/90
- G06T15/00
- G06T15/06
- G06T15/20
- G06T15/50
- CPC G06T19/20
- G06V10/764
- G06T11/00
- G06V10/25
- G06V10/44
- G06V10/74
- G06V20/70
- CPC G06V10/764
- G06V10/774
- G06F40/284
- G06F40/30
- G06V10/86
- G06V20/56
- CPC G06V10/774
- G06V10/80
- G06V10/82
- G06V20/40
- G06V20/58
- G06V40/20
- CPC G06V10/806
- G06V20/69
- G16H30/40
- G16H50/20
- CPC G06V20/698
- G08B25/14
- G06V20/52
- CPC G08B25/14
- G08G1/0967
- G06N20/00
- G08G1/01
- G08G1/16
- CPC G08G1/096725
- G16H10/60
- CPC G16H10/60
- H02J3/00
- G06F18/25
- CPC H02J3/0012
- H02J13/00
- CPC H02J13/00002