Patents by Inventor Fangkai Yang
Fangkai Yang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12061305Abstract: A system and method that include receiving an oilfield operational plan and converting the oilfield operational plan into pixel-grids that are interpreted as images. The system and method also include inputting the pixel-grids to an image analysis convolution neural network to execute an image interpretation process. The system and method additionally include determining a current state of the oilfield operational plan based on the image interpretation process. The system and method further include determining a next state of the oilfield operational plan.Type: GrantFiled: July 6, 2023Date of Patent: August 13, 2024Assignee: Schlumberger Technology CorporationInventors: Maria Fox, Derek Long, Fangkai Yang
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Publication number: 20240217557Abstract: In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.Type: ApplicationFiled: March 12, 2024Publication date: July 4, 2024Inventors: Fangkai Yang, David Nister, Yizhou Wang, Rotem Aviv, Julia Ng, Birgit Henke, Hon Leung Lee, Yunfei Shi
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Patent number: 12001958Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.Type: GrantFiled: March 19, 2020Date of Patent: June 4, 2024Assignee: NVIDIA CorporationInventors: Alexey Kamenev, Nikolai Smolyanskiy, Ishwar Kulkarni, Ollin Boer Bohan, Fangkai Yang, Alperen Degirmenci, Ruchi Bhargava, Urs Muller, David Nister, Rotem Aviv
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Patent number: 11926346Abstract: In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.Type: GrantFiled: August 5, 2021Date of Patent: March 12, 2024Assignee: NVIDIA CorporationInventors: Fangkai Yang, David Nister, Yizhou Wang, Rotem Aviv, Julia Ng, Birgit Henke, Hon Leung Lee, Yunfei Shi
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Publication number: 20240059285Abstract: In various examples, techniques for using future trajectory predictions for adaptive cruise control (ACC) are described. For instance, a vehicle may determine a future path(s) of the vehicle and a future path(s) of an object(s). The vehicle may then use a speed profile(s) and the future path(s) to determine a trajectory(ies) for the vehicle. The vehicle may then select a trajectory, such as based on the future path(s) of the object(s). Based on the trajectory, ACC of the vehicle may cause the vehicle to navigate at a speed or a velocity. This way, the vehicle is able to continue using ACC even when the driver makes a maneuver(s) or the system determined to make a maneuver, such as switching lanes or choosing a lane when a road splits.Type: ApplicationFiled: August 19, 2022Publication date: February 22, 2024Inventors: Julia Ng, Jian Wei Leong, Nikolai Smolyanskiy, Yizhou Wang, Fangkai Yang, Nianfeng Wan, Chang Liu
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Publication number: 20230401103Abstract: A method for dynamically adjusting a number of virtual machines for a workload, includes: receiving a probability indicator for each of a plurality of N sequential stages, where N is a natural number greater than 1, of a likelihood that a virtual machine assigned to a workload will be evicted during the N sequential stages; predicting a target number of virtual machines to configure in a current stage for a subsequent stage from among the plurality of N sequential stages based on the probability indicator, a target capacity for the workload, and a current price for maintaining a virtual machine; and configuring a number of virtual machines for the workload during the current stage based on the target number to be loaded for the workload for the subsequent stage.Type: ApplicationFiled: June 9, 2022Publication date: December 14, 2023Inventors: Soumya RAM, Preston Tapley STEPHENSON, Alexander David FISCHER, Mahmoud SAYED, Robert Edward MINNEKER, Eli Cortex Custodio VILARINHO, Felipe VIEIRA FRUJERI, Inigo GOIRI PRESA, Sidhanth M. PANJWANI, Yandan WANG, Camille Jean COUTURIER, Jue ZHANG, Fangkai YANG, Si QIN, Qingwei LIN, Chetan BANSAL, Bowen PANG, Vivek GUPTA
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Publication number: 20230341585Abstract: A system and method that include receiving an oilfield operational plan and converting the oilfield operational plan into pixel-grids that are interpreted as images. The system and method also include inputting the pixel-grids to an image analysis convolution neural network to execute an image interpretation process. The system and method additionally include determining a current state of the oilfield operational plan based on the image interpretation process. The system and method further include determining a next state of the oilfield operational plan.Type: ApplicationFiled: July 6, 2023Publication date: October 26, 2023Inventors: Maria Fox, Derek Long, Fangkai Yang
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Patent number: 11740384Abstract: A method includes representing oilfield operational plan information as pixels where the pixels include pixels that correspond to a plurality of different state variables associated with oilfield operations; training a deep neural network based at least in part on the pixels to generate a trained deep neural network; implementing the trained deep neural network during generation of an oilfield operational plan; and outputting the oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.Type: GrantFiled: December 6, 2017Date of Patent: August 29, 2023Assignee: Schlumberger Technology CorporationInventors: Maria Fox, Derek Long, Fangkai Yang
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Publication number: 20230037767Abstract: In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.Type: ApplicationFiled: August 5, 2021Publication date: February 9, 2023Inventors: Fangkai Yang, David Nister, Yizhou Wang, Rotem Aviv, Julia Ng, Birgit Henke, Hon Leung Lee, Yunfei Shi
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Patent number: 11423493Abstract: A method includes receiving oilfield operational plan information; determining oilfield operational plan actions based at least in part on the oilfield operational plan information by implementing a combinatorial solver; assessing at least a portion of the oilfield operational plan actions by implementing a logical solver; and, based at least in part on the determining and the assessing, outputting an oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.Type: GrantFiled: December 7, 2017Date of Patent: August 23, 2022Assignee: Schlumberger Technology CorporationInventors: Maria Fox, Derek Long, Fangkai Yang
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Publication number: 20220138568Abstract: In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle.Type: ApplicationFiled: November 1, 2021Publication date: May 5, 2022Inventors: Nikolai Smolyanskiy, Alexey Kamenev, Lirui Wang, David Nister, Ollin Boer Bohan, Ishwar Kulkarni, Fangkai Yang, Julia Ng, Alperen Degirmenci, Ruchi Bhargava, Rotem Aviv
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Publication number: 20210295171Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.Type: ApplicationFiled: March 19, 2020Publication date: September 23, 2021Inventors: Alexey Kamenev, Nikolai Smolyanskiy, Ishwar Kulkarni, Ollin Boer Bohan, Fangkai Yang, Alperen Degirmenci, Ruchi Bhargava, Urs Muller, David Nister, Rotem Aviv
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Publication number: 20190333164Abstract: A method includes receiving oilfield operational plan information; determining oilfield operational plan actions based at least in part on the oilfield operational plan information by implementing a combinatorial solver; assessing at least a portion of the oilfield operational plan actions by implementing a logical solver; and, based at least in part on the determining and the assessing, outputting an oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.Type: ApplicationFiled: December 7, 2017Publication date: October 31, 2019Inventors: Maria Fox, Derek Long, Fangkai Yang
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Publication number: 20190302310Abstract: A method includes representing oilfield operational plan information as pixels where the pixels include pixels that correspond to a plurality of different state variables associated with oilfield operations; training a deep neural network based at least in part on the pixels to generate a trained deep neural network; implementing the trained deep neural network during generation of an oilfield operational plan; and outputting the oilfield operational plan as a digital plan that specifies at least one control action for oilfield equipment.Type: ApplicationFiled: December 6, 2017Publication date: October 3, 2019Inventors: Maria Fox, Derek Long, Fangkai Yang