Patents by Inventor Devesh Upadhyay
Devesh Upadhyay 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: 12189383Abstract: Methods and systems are provided for monitoring a health of a vehicle component. In one embodiment, a method is provided, comprising dividing a population of vehicles of a connected vehicle population into a plurality of vehicle classes; for each vehicle class of the plurality of vehicle classes, training a class-specific model of the vehicle class to predict a health status variable of a vehicle component included in the vehicle class based on labelled data from historic databases and calibration data; and for each vehicle class of the plurality of vehicle classes, using a first Federated Learning strategy to request local model data from each vehicle of a plurality of vehicles of the vehicle class; receive the local model data from the plurality of vehicles; update the class-specific model based on the received local model data; and send updated parameters of the class-specific model to vehicles included in the vehicle class.Type: GrantFiled: September 1, 2021Date of Patent: January 7, 2025Assignee: Ford Global Technologies, LLCInventors: Devesh Upadhyay, Huanyi Shui, Dimitar Filev
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Patent number: 12175804Abstract: A server includes an interface configured to communicate with a plurality of vehicles; and a processor, programmed to, send a query to the plurality of vehicles, the query identifying types of vehicle data and indicating an initial sampling rate, responsive to receiving the vehicle data sampled by the vehicles, process the vehicle data to obtain a feature result including an estimated value and a variance extending from the estimated value, and responsive to the variance being greater than a first threshold, send a first updated query indicating an increased sampling rate to the plurality of vehicles.Type: GrantFiled: December 10, 2021Date of Patent: December 24, 2024Assignee: Ford Global Technologies, LLCInventors: Huanyi Shui, Zhe Wang, Devesh Upadhyay, Brandon M. Dawson
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Patent number: 12159395Abstract: A method including generating a plurality of synthetic images of a material, where each synthetic image from among the plurality of synthetic images is associated with a feasibility value greater than a threshold synthetic feasibility value. The method includes determining, for each synthetic image from among the plurality of synthetic images, one or more material properties of the material and one or more process parameters of the material based on the synthetic image and generating a plurality of data points and a pareto surface based on the one or more material properties and the one or more process parameters. The method includes selecting a target data point based on the plurality of data points and a distance between a set of data points from among the plurality of data points and the pareto surface.Type: GrantFiled: November 17, 2021Date of Patent: December 3, 2024Assignee: Ford Global Technologies, LLCInventors: Alemayehu Admasu, Devesh Upadhyay, Patrick James Blanchard, Janice Lisa Tardiff
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Publication number: 20240394514Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor to receive an image in a first neural network that outputs a first prediction based on the image, wherein weights applied to layers in the first neural network are determined by minimizing a sum of a first loss function and a second loss function. The first loss function can be determined from the first features determined in the first neural network trained to output a first prediction and from second features determined in a second neural network trained to output a second prediction. The second loss function can be determined based on comparing the first prediction to ground truth. The first prediction can be output.Type: ApplicationFiled: May 24, 2023Publication date: November 28, 2024Applicant: Ford Global Technologies, LLCInventors: Mazin Hnewa, Alireza Rahimpour, Devesh Upadhyay, Justin Miller
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Publication number: 20240319043Abstract: A system is disclosed that includes a computer and memory, the memory including instructions to transform acoustic data to an order spectrum and input the order spectrum to a decoder to determine a feature vector. The feature vector can be input to a one-class classifier to classify the order spectrum as anomalous or non-anomalous and the classified order spectrum can be output.Type: ApplicationFiled: March 21, 2023Publication date: September 26, 2024Applicant: Ford Global Technologies, LLCInventors: Huanyi Shui, Rajesh Gupta, Gurram Sujith Kumar, Devesh Upadhyay, Rajeev Kalamdani, Douglas K. Grimes, Saumuy Puchala
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Publication number: 20240320505Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor to train an agent neural network to input a first state and output a first action, input the first action to an environment and determine a second state and a reward. Koopman model neural network can be trained based on the first state, the first action and the second state to determine a fake state. The agent neural network can be re-trained and the Koopman model neural network can be re-trained based on reinforcement learning including the first state, the first action, the second state, the fake state, and the reward.Type: ApplicationFiled: March 22, 2023Publication date: September 26, 2024Applicant: Ford Global Technologies, LLCInventors: Kaushik Balakrishnan, Neeloy Chakraborty, Devesh Upadhyay
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Publication number: 20240253652Abstract: A far-infrared camera mounted in a vehicle generates an image frame. When an image of a large animal is identified in the image frame, a pixel intensity of the large animal image is determined. An estimated distance to the large animal from the far-infrared camera based on the pixel intensity is determined. When the animal is classified as a tracked animal, and future trajectories of the tracked animal and the vehicle intersect, a component in the vehicle is actuated.Type: ApplicationFiled: January 30, 2023Publication date: August 1, 2024Applicant: Ford Global Technologies, LLCInventors: Alireza Rahimpour, Navid Fallahinia, Devesh Upadhyay, Justin Miller
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Publication number: 20240231289Abstract: A method includes generating, by a given autoencoder, a given operational indicator based on sensor data obtained from one or more sensors disposed at a given manufacturing station; selectively aggregating, by the given linear propagator and based on a linear mapping model, the given operational indicator and one or more additional operational indicators associated with one or more additional manufacturing stations to selectively generate an aggregated operational indicator; generating, by the given neural network and in response to generating the aggregated operational indicator, a predicted operational characteristic of the given manufacturing station based on the given operational indicator and the aggregated operational indicator; and determining a state of the manufacturing system based on the predicted operational characteristic and one or more additional predicted operational characteristics generated by one or more additional neural networks from among the plurality of neural networks.Type: ApplicationFiled: January 11, 2023Publication date: July 11, 2024Applicant: Ford Global Technologies, LLCInventors: Zhiyi Chen, Harshal Maske, Devesh Upadhyay, Huanyi Shui, Michael Brendan Hopka, Xun Huan
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Patent number: 11995923Abstract: A vehicle includes an engine, a clutch, and a controller. The clutch is configured to crank the engine during an engine start. The controller is programmed to, in response to a command to start the engine, adjust a clutch actuator pressure to drive a disconnect clutch torque toward a desired value. The controller is further programmed to, in response to variances of the disconnect clutch torque relative to the clutch actuator pressure exceeding a threshold, issue a disconnect clutch fault.Type: GrantFiled: March 25, 2021Date of Patent: May 28, 2024Assignee: Ford Global Technologies, LLCInventors: Huanyi Shui, Yijing Zhang, Hang Yang, Devesh Upadhyay, Hiral Jayantilal Haria, Yuji Fujii
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Patent number: 11981326Abstract: A plurality of thermal images forward of a vehicle are collected. Thermal data in the plurality of thermal images is normalized based on an ambient air temperature to generate a plurality of normalized thermal images. The plurality of normalized thermal images are input to a machine learning program trained to output an identification of an object based on the ambient air temperature and a risk of collision between the vehicle and the object. A vehicle component is actuated based on the identification of the object and the risk of collision with the object.Type: GrantFiled: March 24, 2021Date of Patent: May 14, 2024Assignee: Ford Global Technologies, LLCInventors: Alireza Rahimpour, Devesh Upadhyay, Jonathan Diedrich, Mark Gehrke
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Patent number: 11977715Abstract: An identifier for first set of display content on a vehicle display is input to a statistical model that outputs a plurality of probabilities that a user input will select each of a plurality of second set of display contents for display after the first set of display content is displayed. A first probability is identified for a predicted set of display content that is a highest probability in the plurality of probabilities. The plurality of probabilities are provided to at least one of an optimization model and a neural network upon determining an accuracy of the statistical model is below a threshold. Upon receiving, from the at least one of the optimization model and the neural network, a second probability for the predicted set of display content, the display content is selected based on the first and second probabilities. The vehicle display is updated based on the selected set of display content.Type: GrantFiled: February 9, 2021Date of Patent: May 7, 2024Assignee: Ford Global Technologies, LLCInventors: Fling Finn Tseng, Johannes Geir Kristinsson, Daryl Martin, Bhagyashri Satyabodha Katti, Himanshu Verma, Shiqi Qiu, Jonathan Niemi, Devesh Upadhyay
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Patent number: 11914358Abstract: Methods and systems are provided for increasing an accuracy of anomaly detection in assets such as vehicle components. In one example, a method provides for continuous health monitoring of connected physical assets, comprising adapting thresholds for anomaly detection and root cause analysis algorithms for the connected assets based on an aggregation of new connected data using machine learning; updating and ranking advanced statistical and machine learning models based on their performance using connected data until confirming a best performing model; and deploying the best performing model to monitor the connected physical assets.Type: GrantFiled: September 1, 2021Date of Patent: February 27, 2024Assignee: Ford Global Technologies, LLCInventors: Devesh Upadhyay, Huanyi Shui, Dimitar Filev
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Patent number: 11860592Abstract: A method includes obtaining state information from one or more sensors of a digital twin. The method includes determining an action at the first routing control location based on the state information, where the action includes one of a pallet merging operation and a pallet splitting operation, and determining a consequence state based on the action. The method includes calculating a transient production value based on the consequence state and a transient objective function, calculating a steady state production value based on the consequence state and a steady state objective function, and selectively adjusting one or more reinforcement parameters of the reinforcement learning system based on the transient production value and the steady state production value.Type: GrantFiled: December 22, 2021Date of Patent: January 2, 2024Assignee: Ford Global Technologies, LLCInventors: Harshal Maske, Devesh Upadhyay, Jim Birley, Dimitar Petrov Filev, Justin Miller, Robert Bennett
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Patent number: 11833998Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input a signal received from a portable device to a machine learning program trained to output a location of the portable device relative to a vehicle, collect operating data of one or more vehicle components, predict an action of a vehicle user based on the predicted location, and, based on the predicted action of the vehicle user, actuate one or more vehicle components. The machine learning program is trained with a training dataset that is updatable to include the signal, the output predicted location, the collected operating data, the predicted action, and an identified action performed by the vehicle user.Type: GrantFiled: February 17, 2021Date of Patent: December 5, 2023Assignee: Ford Global Technologies, LLCInventors: Ryan Burke, Devesh Upadhyay
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Patent number: 11829131Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a neural network included in a memory augmented neural network based on one or more images and corresponding ground truth in a training dataset by transforming the one or more images to generate a plurality of one-hundred or more variations of the one or more images including variations in the ground truth and process the variations of the one or more images and store feature points corresponding to each variation of the one or more images in memory associated with the memory augmented neural network. The instructions can include further instructions to process an image acquired by a vehicle sensor with the memory augmented neural network, including comparing a feature variance set for the image acquired by the vehicle sensor to the stored processing parameters for each variation of the one or more images, to obtain an output result.Type: GrantFiled: October 29, 2020Date of Patent: November 28, 2023Assignee: Ford Global Technologies, LLCInventors: Iman Soltani Bozchalooi, Francois Charette, Dimitar Petrov Filev, Ryan Burke, Devesh Upadhyay
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Publication number: 20230342512Abstract: Systems and methods for automotive shape design by combining computational fluid dynamics (CFD) and Generative Adversarial Network (GAN). CFD simulations may be performed to determine aerodynamic properties and identify a set of candidate vehicle outline shapes. Vehicle shape outlines may be provided as input to a generative adversarial network (GAN) that is trained to learn aesthetic preferences for vehicle attributes. The GAN may be used to determine, by based on the vehicle outline shape, a set of vehicle attributes. The GAN may be used to generate photo-realistic images with the vehicle shape outline and filling in additional aesthetic styles for the given outline, such as different colors, lighting, visual appearance, wheel design, aspect ratio, etc.Type: ApplicationFiled: April 21, 2022Publication date: October 26, 2023Applicant: Ford Global Technologies, LLCInventors: Kaushik Balakrishnan, Devesh Upadhyay, Herbert Alexander Morriss-Andrews, Ryan Joseph Madden, Suzhou Huang, Dimitar Petrov Filev
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Publication number: 20230196217Abstract: A method includes obtaining sensor data from a plurality of sensors disposed at a plurality of routing control locations of an environment, where the sensor data is indicative of a number of a plurality of pallets at the plurality of routing control locations. The method includes calculating a plurality of difference values based on the sensor data, calculating a transient production value based on the sensor data and a transient objective function, and calculating a steady state production value based on the sensor data and a steady state objective function. The method includes generating a state vector based on the plurality of difference values, the transient production value, and the steady state production value, and defining a set of routes for a set of pallets from among the plurality of pallets based on the state vector and a digital twin of the environment.Type: ApplicationFiled: December 22, 2021Publication date: June 22, 2023Applicant: Ford Global Technologies, LLCInventors: Harshal Maske, Devesh Upadhyay, Jim Birley, Dimitar Petrov Filev, Justin Miller, Robert Bennett
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Publication number: 20230195057Abstract: A method includes obtaining state information from one or more sensors of a digital twin. The method includes determining an action at the first routing control location based on the state information, where the action includes one of a pallet merging operation and a pallet splitting operation, and determining a consequence state based on the action. The method includes calculating a transient production value based on the consequence state and a transient objective function, calculating a steady state production value based on the consequence state and a steady state objective function, and selectively adjusting one or more reinforcement parameters of the reinforcement learning system based on the transient production value and the steady state production value.Type: ApplicationFiled: December 22, 2021Publication date: June 22, 2023Applicant: Ford Global Technologies, LLCInventors: Harshal Maske, Devesh Upadhyay, Jim Birley, Dimitar Petrov Filev, Justin Miller, Robert Bennett
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Publication number: 20230195093Abstract: A method includes defining a sensor layout of a digital twin based on one or more sensor parameters and one or more routing control locations of the digital twin. The method includes simulating a manufacturing routine of a plurality of pallets and a plurality of workstations based on one or more pallet parameters associated with the plurality of pallets and one or more workstation parameters associated with the plurality of workstations and calculating, for each routing control location from among the one or more routing control locations, a transient production value and a steady state production value based on the manufacturing routine. The method includes iteratively adjusting the sensor layout of the digital twin until each transient production value is less than or equal to a threshold transient production value and each steady state production value is less than or equal to a threshold steady state production value.Type: ApplicationFiled: December 22, 2021Publication date: June 22, 2023Applicant: Ford Global Technologies, LLCInventors: Harshal Maske, Devesh Upadhyay, Jim Birley, Dimitar Petrov Filev, Justin Miller, Robert Bennett
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Publication number: 20230186691Abstract: A server includes an interface configured to communicate with a plurality of vehicles; and a processor, programmed to, send a query to the plurality of vehicles, the query identifying types of vehicle data and indicating an initial sampling rate, responsive to receiving the vehicle data sampled by the vehicles, process the vehicle data to obtain a feature result including an estimated value and a variance extending from the estimated value, and responsive to the variance being greater than a first threshold, send a first updated query indicating an increased sampling rate to the plurality of vehicles.Type: ApplicationFiled: December 10, 2021Publication date: June 15, 2023Inventors: Huanyi SHUI, Zhe WANG, Devesh UPADHYAY, Brandon M. DAWSON