Patents by Inventor Daniel Hendricus Franciscus DIJKMAN
Daniel Hendricus Franciscus DIJKMAN 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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Publication number: 20240119363Abstract: A processor-implemented method includes observing an environment via one or more sensors associated with a robotic device. The processor-implemented method also includes generating, via an inference model, a belief of the environment based on data associated with prior actions of the robotic device in the environment. The processor-implemented method further includes controlling the robotic device to perform an action in the environment based on generating the belief.Type: ApplicationFiled: August 31, 2023Publication date: April 11, 2024Inventors: Risto VUORIO, Pim DE HAAN, Johann Hinrich BREHMER, Hanno ACKERMANN, Taco Sebastiaan COHEN, Daniel Hendricus Franciscus DIJKMAN
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Patent number: 11871299Abstract: Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform three-dimensional (3D) object location estimation.Type: GrantFiled: May 27, 2021Date of Patent: January 9, 2024Assignee: QUALCOMM IncorporatedInventors: Ilia Karmanov, Daniel Hendricus Franciscus Dijkman, Simone Merlin
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Patent number: 11755886Abstract: Disclosed are systems, methods, and non-transitory media for performing radio frequency sensing detection operations. For instance, radio frequency data can be received that is associated with at least one wireless device. The radio frequency data can be based on radio frequency signals reflected from a first object and received by the at least one wireless device. Training label data can also be obtained (e.g., from a labeling device, from the at least one wireless device, etc.). The training label data can be based at least in part on the first object and input data (e.g., received by the labeling device, the at least one wireless device, etc.). A sensing model can be generated based on the radio frequency data and the training label data.Type: GrantFiled: April 13, 2021Date of Patent: September 12, 2023Assignee: QUALCOMM IncorporatedInventors: Simone Merlin, Jamie Menjay Lin, Brian Michael Buesker, Rui Liang, Daniel Hendricus Franciscus Dijkman, Farhad Ghazvinian Zanjani, Ilia Karmanov, Vamsi Vegunta, Harshit Joshi, Bibhu Mohanty, Raamkumar Balamurthi
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Publication number: 20230259600Abstract: Certain aspects of the present disclosure provide techniques and apparatus for biometric authentication using an anti-spoofing protection model refined using online data. The method generally includes receiving a biometric data input for a user. Features for the received biometric data input are extracted through a first machine learning model. It is determined, using the extracted features for the received biometric data input and a second machine learning model, whether the received biometric data input for the user is authentic or inauthentic. It is determined whether to add the extracted features for the received biometric data input, labeled with an indication of whether the received biometric data input is authentic or inauthentic, to a finetuning data set. The second machine learning model is adjusted based on the finetuning data set.Type: ApplicationFiled: January 17, 2023Publication date: August 17, 2023Inventors: Davide BELLI, Bence MAJOR, Amir JALALIRAD, Daniel Hendricus Franciscus DIJKMAN, Fatih Murat PORIKLI
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Publication number: 20230237819Abstract: Systems and techniques are provided for unsupervised scene-decompositional normalizing flows. An example process can include obtaining a scene-decompositional model having a normalizing flow neural network architecture. The process can include determining, based on processing data depicting multiple targets in a scene using the scene-decompositional model, a distribution of scene data as a mixture of flows from one or more background components and one or more foreground components. The process can further include identifying, based on processing the distribution of scene data using the scene-decompositional model, a target associated with the one or more foreground components and included in the data depicting the multiple targets in the scene.Type: ApplicationFiled: July 7, 2022Publication date: July 27, 2023Inventors: Farhad GHAZVINIAN ZANJANI, Hanno ACKERMANN, Daniel Hendricus Franciscus DIJKMAN, Fatih Murat PORIKLI
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Patent number: 11696093Abstract: Certain aspects of the present disclosure provide techniques for object positioning using mixture density networks, comprising: receiving radio frequency (RF) signal data collected in a physical space; generating a feature vector encoding the RF signal data by processing the RF signal data using a first neural network; processing the feature vector using a first mixture model to generate a first encoding tensor indicating a set of moving objects in the physical space, a first location tensor indicating a location of each of the moving objects in the physical space, and a first uncertainty tensor indicating uncertainty of the locations of each of the moving objects in the physical space; and outputting at least one location from the first location tensor.Type: GrantFiled: February 22, 2021Date of Patent: July 4, 2023Assignee: Qualcomm IncorporatedInventors: Farhad Ghazvinian Zanjani, Arash Behboodi, Daniel Hendricus Franciscus Dijkman, Ilia Karmanov, Simone Merlin, Max Welling
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Publication number: 20230153690Abstract: Certain aspects of the present disclosure provide techniques method for self-supervised training of a machine learning model to predict the location of a device in a spatial environment, such as a spatial environment including multiple discrete planes. An example method generally includes receiving an input data set of scene data. A generator model is trained to map scene data in the input data set to points in three-dimensional space. One or more critic models are trained to backpropagate a gradient to the generator model to push the points in the three-dimensional space to one of a plurality of planes in the three-dimensional space. At least the generator is deployed.Type: ApplicationFiled: October 19, 2022Publication date: May 18, 2023Inventors: Hanno ACKERMANN, Ilia KARMANOV, Farhad GHAZVINIAN ZANJANI, Daniel Hendricus Franciscus DIJKMAN, Fatih Murat PORIKLI
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Publication number: 20230085898Abstract: Certain aspects of the present disclosure provide techniques for sensor calibration. First sensor data is received from a first sensor and second sensor data is received from a second sensor, where the first sensor data and the second sensor data each indicate detected objects in a space. The first sensor data is transformed using a first transformation profile to convert the first sensor data to a coordinate frame of the second sensor data. The first transformation profile is refined based on a difference between the transformed first sensor data and the second sensor data.Type: ApplicationFiled: September 22, 2021Publication date: March 23, 2023Inventors: Daniel Hendricus Franciscus DIJKMAN, Haitam BEN YAHIA, Sundar SUBRAMANIAN, Radhika Dilip GOWAIKAR
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Patent number: 11575452Abstract: Disclosed are systems, methods, and non-transitory media for sensing radio frequency signals. For instance, radio frequency data can be received by an apparatus and from at least one wireless device in an environment. Based on the radio frequency data received from the at least one wireless device, the apparatus can determine sensing coverage of the at least one wireless device. The apparatus can further provide the determined sensing coverage and a position of at least one device to a user device.Type: GrantFiled: April 13, 2021Date of Patent: February 7, 2023Assignee: QUALCOMM IncorporatedInventors: Simone Merlin, Bibhu Mohanty, Daniel Hendricus Franciscus Dijkman, Farhad Ghazvinian Zanjani, Ilia Karmanov, Brian Michael Buesker, Harshit Joshi, Vamsi Vegunta, Ishaque Ashar Kadampot
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Publication number: 20220383114Abstract: Certain aspects of the present disclosure provide techniques for training and inferencing with machine learning localization models. In one aspect, a method, includes training a machine learning model based on input data for performing localization of an object in a target space, including: determining parameters of a neural network configured to map samples in an input space based on the input data to samples in an intrinsic space; and determining parameters of a coupling matrix configured to transport the samples in the intrinsic space to the target space.Type: ApplicationFiled: May 31, 2022Publication date: December 1, 2022Inventors: Farhad Ghazvinian Zanjani, Ilia Karmanov, Daniel Hendricus Franciscus Dijkman, Hanno Ackermann, Simone Merlin, Brian Michael Buesker, Ishaque Ashar Kadampot, Fatih Murat Porikli, Max Welling
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Publication number: 20220337972Abstract: Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform three-dimensional (3D) object location estimation.Type: ApplicationFiled: May 27, 2021Publication date: October 20, 2022Inventors: Ilia KARMANOV, Daniel Hendricus Franciscus DIJKMAN, Simone MERLIN
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Publication number: 20220329330Abstract: Disclosed are systems, methods, and non-transitory media for sensing radio frequency signals. For instance, radio frequency data can be received by an apparatus and from at least one wireless device in an environment. Based on the radio frequency data received from the at least one wireless device, the apparatus can determine sensing coverage of the at least one wireless device. The apparatus can further provide the determined sensing coverage and a position of at least one device to a user device.Type: ApplicationFiled: April 13, 2021Publication date: October 13, 2022Inventors: Simone MERLIN, Bibhu MOHANTY, Daniel Hendricus Franciscus DIJKMAN, Farhad GHAZVINIAN ZANJANI, Ilia KARMANOV, Brian Michael BUESKER, Harshit JOSHI, Vamsi VEGUNTA, Ishaque Ashar KADAMPOT
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Publication number: 20220329973Abstract: Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform object location estimation.Type: ApplicationFiled: April 13, 2021Publication date: October 13, 2022Inventors: Ilia KARMANOV, Daniel Hendricus Franciscus DIJKMAN, Farhad GHAZVINIAN ZANJANI, Ishaque Ashar KADAMPOT, Simone MERLIN, Brian Michael BUESKER, Vamsi VEGUNTA, Harshit JOSHI, Fatih Murat PORIKLI, Joseph Binamira SORIAGA, Bibhu MOHANTY
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Publication number: 20220327360Abstract: Disclosed are systems, methods, and non-transitory media for performing radio frequency sensing detection operations. For instance, radio frequency data can be received that is associated with at least one wireless device. The radio frequency data can be based on radio frequency signals reflected from a first object and received by the at least one wireless device. Training label data can also be obtained (e.g., from a labeling device, from the at least one wireless device, etc.). The training label data can be based at least in part on the first object and input data (e.g., received by the labeling device, the at least one wireless device, etc.). A sensing model can be generated based on the radio frequency data and the training label data.Type: ApplicationFiled: April 13, 2021Publication date: October 13, 2022Inventors: Simone MERLIN, Jamie Menjay LIN, Brian Michael BUESKER, Rui LIANG, Daniel Hendricus Franciscus DIJKMAN, Farhad GHAZVINIAN ZANJANI, Ilia KARMANOV, Vamsi VEGUNTA, Harshit JOSHI, Bibhu MOHANTY, Raamkumar BALAMURTHI
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Publication number: 20220327189Abstract: Certain aspects of the present disclosure provide techniques and apparatus for biometric authentication using neural-network-based anti-spoofing protection mechanisms. An example method generally includes receiving an image of a biometric data source for a user; extracting, through a first artificial neural network, features for at least the received image; combining the extracted features for the at least the received image and a combined feature representation of a plurality of enrollment biometric data source images; determining, using the combined extracted features for the at least the received image and the combined feature representation as input into a second artificial neural network, whether the received image of the biometric data source for the user is from a real biometric data source or a copy of the real biometric data source; and taking one or more actions to allow or deny the user access to a protected resource based on the determination.Type: ApplicationFiled: April 8, 2022Publication date: October 13, 2022Inventors: Davide BELLI, Bence MAJOR, Daniel Hendricus Franciscus DIJKMAN, Fatih Murat PORIKLI
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Publication number: 20220272489Abstract: Certain aspects of the present disclosure provide techniques for object positioning using mixture density networks, comprising: receiving radio frequency (RF) signal data collected in a physical space; generating a feature vector encoding the RF signal data by processing the RF signal data using a first neural network; processing the feature vector using a first mixture model to generate a first encoding tensor indicating a set of moving objects in the physical space, a first location tensor indicating a location of each of the moving objects in the physical space, and a first uncertainty tensor indicating uncertainty of the locations of each of the moving objects in the physical space; and outputting at least one location from the first location tensor.Type: ApplicationFiled: February 22, 2021Publication date: August 25, 2022Inventors: Farhad GHAZVINIAN ZANJANI, Arash BEHBOODI, Daniel Hendricus Franciscus DIJKMAN, Ilia KARMANOV, Simone MERLIN, Max WELLING
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Publication number: 20210150347Abstract: Aspects described herein provide a method of performing guided training of a neural network model, including: receiving supplementary domain feature data; providing the supplementary domain feature data to a fully connected layer of a neural network model; receiving from the fully connected layer supplementary domain feature scaling data; providing the supplementary domain feature scaling data to an activation function; receiving from the activation function supplementary domain feature weight data; receiving a set of feature maps from a first convolution layer of the neural network model; fusing the supplementary domain feature weight data with the set of feature maps to form fused feature maps; and providing the fused feature maps to a second convolution layer of the neural network model.Type: ApplicationFiled: November 13, 2020Publication date: May 20, 2021Inventors: Shubhankar Mange BORSE, Nojun KWAK, Daniel Hendricus Franciscus DIJKMAN, Bence MAJOR
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Patent number: 10964033Abstract: A visual tracker may track an object by identifying the object in a frame, and the visual tracker by identify the object in the frame within a search region. The search region may be provided by a motion modeling system that independently models the motion of the object and models the motion of the camera. For example, an object motion model of the motion modeling system may first model the motion of the object, assuming the camera is not in motion, in order to identify the expected position of the object. A camera motion model of the motion modeling system may then update the expected position of the object, obtained from the object motion model, based on the motion of the camera.Type: GrantFiled: August 7, 2018Date of Patent: March 30, 2021Assignee: Qualcomm IncorporatedInventors: Amirhossein Habibian, Daniel Hendricus Franciscus Dijkman, Antonio Leonardo Rodriguez Lopez, Yue Hei Ng, Koen Erik Adriaan Van De Sande, Cornelis Gerardus Maria Snoek
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Publication number: 20200051254Abstract: A visual tracker may track an object by identifying the object in a frame, and the visual tracker by identify the object in the frame within a search region. The search region may be provided by a motion modeling system that independently models the motion of the object and models the motion of the camera. For example, an object motion model of the motion modeling system may first model the motion of the object, assuming the camera is not in motion, in order to identify the expected position of the object. A camera motion model of the motion modeling system may then update the expected position of the object, obtained from the object motion model, based on the motion of the camera.Type: ApplicationFiled: August 7, 2018Publication date: February 13, 2020Inventors: Amirhossein HABIBIAN, Daniel Hendricus Franciscus DIJKMAN, Antonio Leonardo RODRIGUEZ LOPEZ, Yue Hei NG, Koen Erik Adriaan VAN DE SANDE, Cornelis Gerardus Maria SNOEK
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Patent number: 10410096Abstract: Context-based priors are utilized in machine learning networks (e.g., neural networks) for detecting objects in images. The likely locations of objects are estimated based on context labels. A machine learning network identifies a context label of an entire image. Based on the context label, the network selects a set of likely regions for detecting objects of interest in the image.Type: GrantFiled: October 13, 2015Date of Patent: September 10, 2019Assignee: Qualcomm IncorporatedInventors: Daniel Hendricus Franciscus Dijkman, Regan Blythe Towal, Venkata Sreekanta Reddy Annapureddy