Patents by Inventor Jayanta Kumar Dutta
Jayanta Kumar Dutta 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: 11938968Abstract: A vehicle for collecting image data of a target object for generating a classifier. The vehicle includes an image sensor and an electronic processor. The electronic processor is configured to determine a plurality of potential trajectories of the vehicle, determine, for each of the plurality of potential trajectories of the vehicle, a total number of views including the target object that would be captured by the image sensor as the vehicle moved along the respective trajectory, and determine a key trajectory of the vehicle from the plurality of potential trajectories based on the total number of views including the target of the key trajectory.Type: GrantFiled: November 10, 2021Date of Patent: March 26, 2024Assignee: Robert Bosch GmbHInventors: Luiz Ricardo Douat, Michael Erz, Jayanta Kumar Dutta, Marc Naumann, Naveen Ramakrishnan
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Patent number: 11783636Abstract: A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an “activity vector.” Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.Type: GrantFiled: June 15, 2021Date of Patent: October 10, 2023Assignee: Robert Bosch GmbHInventors: Yumi Kondo, Ryan Burt, Krishnan Bharath Navalpakkam, Alexander Hirsch, Naveen Ramakrishnan, Filipe Goncalves, Stefan Weissert, Jayanta Kumar Dutta, Ravi Kumar Satzoda
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Publication number: 20230260251Abstract: Identifying key frames of a video for use in training a machine learning model is provided. Object detection is performed to identify frames of a video including target classes of objects of interest. Feature extraction is performed on the identified frames to generate raw feature vectors. The feature vectors are compressed into lower dimension vectors. The compressed feature vectors are compressed into a plurality of clusters. The clustered compressed feature vectors are filtered to identify the key frames from each of the plurality of clusters. The key frames may be provided as a representative data set of the video.Type: ApplicationFiled: February 17, 2022Publication date: August 17, 2023Inventors: Chun-Hao LIU, Jayanta Kumar DUTTA, Naveen RAMAKRISHNAN
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Publication number: 20230254592Abstract: A computer-implemented method includes communicating with a remote network, capturing one or more images or video recordings, receiving one or more images from the camera, wherein the one or more images from the camera is a high resolution image (HRI), compressing the HRI via a compression model to a low resolution image (LRI), encoding the LRI to obtain an encoded LRI, sending the encoded LRI to a super resolution model at the remote network, decoding the encoded LRI at the remote network to obtain a reconstructed HRI, and outputting the reconstructed HRI.Type: ApplicationFiled: February 7, 2022Publication date: August 10, 2023Inventors: Jayanta Kumar DUTTA, Naveen RAMAKRISHNAN
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Publication number: 20230244924Abstract: A system and method for generating a robust pseudo-label dataset where a labeled source dataset (e.g., video) may be received and used to train a teacher neural network. A pseudo-labeled dataset may then be output from the teacher network and provided to a similarity-aware weighted box fusion (SWBF) algorithm along with an unlabeled dataset. A robust pseudo-label dataset may then be generated by the SWBF algorithm from and used to train a student neural network. The student neural network may also be further tuned using the labeled source dataset. Lastly, the teacher neural network may be replaced using the student neural network. It is contemplated the system and method may be iteratively repeated.Type: ApplicationFiled: January 31, 2022Publication date: August 3, 2023Inventors: SHU HU, CHUN-HAO LIU, JAYANTA KUMAR DUTTA, NAVEEN RAMAKRISHNAN
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Publication number: 20230143963Abstract: A vehicle for collecting image data of a target object for generating a classifier. The vehicle includes an image sensor and an electronic processor. The electronic processor is configured to determine a plurality of potential trajectories of the vehicle, determine, for each of the plurality of potential trajectories of the vehicle, a total number of views including the target object that would be captured by the image sensor as the vehicle moved along the respective trajectory, and determine a key trajectory of the vehicle from the plurality of potential trajectories based on the total number of views including the target of the key trajectory.Type: ApplicationFiled: November 10, 2021Publication date: May 11, 2023Inventors: Luiz Ricardo Douat, Michael Erz, Jayanta Kumar Dutta, Marc Naumann, Naveen Ramakrishnan
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Publication number: 20220398463Abstract: A method and system is disclosed for creating a machine learning model that is reconfigurable. A fixed parameter model is created to include fixed feature values obtained during a training process for the machine learning model. The fixed parameter model may include a fixed base classifier used by the machine learning model to classify objects detected by an ultra-sonic system within a vicinity of a vehicle. A configurable parameter model may be created to include feature values that are different from the fixed feature values, the configurable parameter model including a modified base classifier. A vehicle controller may receive and update the fixed parameter model with the configurable parameter model. The machine learning model may be updated to use the configurable parameter model to classify the objects detected by the ultra-sonic system.Type: ApplicationFiled: June 11, 2021Publication date: December 15, 2022Applicant: Robert Bosch GmbHInventors: Lisa Marion GARCIA, Ravi Kumar SATZODA, Fabio CECCHI, Abinaya KUMAR, Mark WILSON, Naveen RAMAKRISHNAN, Timo PFROMMER, Jayanta Kumar DUTTA, Juergen Johannes SCHMIDT, Tobias WINGERT, Michael TCHORZEWSKI, Michael SCHUMANN
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Publication number: 20220398414Abstract: A method and system is disclosed for tuning a machine learning classifier. An object class requirement may be provided and include rank thresholds. The object class requirements may also include a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results. A base classifier may be trained using a weighted loss function that includes one or more weight values that are computed using the one or more object class requirements. An output of the weighted loss function may be evaluated using an objective function which may be established using the one or more object class requirements. The one or more weights may also be re-tuned using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold.Type: ApplicationFiled: June 11, 2021Publication date: December 15, 2022Applicant: Robert Bosch GmbHInventors: Abinaya KUMAR, Fabio CECCHI, Ravi Kumar SATZODA, Lisa Marion GARCIA, Mark WILSON, Naveen RAMAKRISHNAN, Timo PFROMMER, Jayanta Kumar DUTTA, Juergen Johannes SCHMIDT, Tobias WINGERT, Michael TCHORZEWSKI, Michael SCHUMANN
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Publication number: 20220397666Abstract: A system and method is disclosed for classifying one or more objects within a vicinity of a vehicle. Ultra-sonic data may be received from a plurality of ultra-sonic sensors and may comprise echo signals indicating one or more objects that are proximally located within a vicinity of a vehicle. One or more features may be calculated from the ultra-sonic data using one or more signal processing algorithms unique to each of the plurality of ultra-sonic sensors. The one more features may be combined using a second-level signal processing algorithm to determine geometric relations for the one or more objects. The one or more features may then be statistically aggregated at an object level. The one or more objects may then be classified using a machine learning algorithm that compares an input of each of the one or more features to a trained classifier.Type: ApplicationFiled: June 11, 2021Publication date: December 15, 2022Applicant: Robert Bosch GmbHInventors: Fabio CECCHI, Abinaya KUMAR, Ravi Kumar SATZODA, Lisa Marion GARCIA, Mark WILSON, Naveen RAMAKRISHNAN, Timo PFROMMER, Jayanta Kumar DUTTA, Juergen Johannes SCHMIDT, Tobias WINGERT, Michael TCHORZEWSKI, Michael SCHUMANN, Chen RUOBING, Kyle ELLEFSEN
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Publication number: 20210312238Abstract: A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an “activity vector.” Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.Type: ApplicationFiled: June 15, 2021Publication date: October 7, 2021Inventors: Yumi Kondo, Ryan Burt, Krishnan Bharath Navalpakkam, Alexander Hirsch, Naveen Ramakrishnan, Filipe Goncalves, Stefan Weissert, Jayanta Kumar Dutta, Ravi Kumar Satzoda
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Patent number: 11132585Abstract: A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an “activity vector.” Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.Type: GrantFiled: December 17, 2019Date of Patent: September 28, 2021Assignee: Robert Bosch GmbHInventors: Yumi Kondo, Ryan Burt, Krishnan Bharath Navalpakkam, Alexander Hirsch, Naveen Ramakrishnan, Filipe Goncalves, Stefan Weissert, Jayanta Kumar Dutta, Ravi Kumar Satzoda
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Publication number: 20210182617Abstract: A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an “activity vector.” Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.Type: ApplicationFiled: December 17, 2019Publication date: June 17, 2021Inventors: Yumi Kondo, Ryan Burt, Krishnan Bharath Navalpakkam, Alexander Hirsch, Naveen Ramakrishnan, Filipe Goncalves, Stefan Weissert, Jayanta Kumar Dutta, Ravi Kumar Satzoda
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Patent number: 10776668Abstract: A search framework for finding effective architectural building blocks for deep convolutional neural networks is disclosed. The search framework described herein utilizes a building block which incorporates branch and skip connections. At least some operations of the architecture of the building block are undefined and treated as hyperparameters which can be automatically selected and optimized for a particular task. The search framework uses random search over the reduced search space to generate a building block and repeats the building block multiple times to create a deep convolutional neural network.Type: GrantFiled: December 7, 2018Date of Patent: September 15, 2020Assignee: Robert Bosch GmbHInventors: Jayanta Kumar Dutta, Jiayi Liu, Unmesh Kurup, Mohak Shah
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Publication number: 20190188537Abstract: A search framework for finding effective architectural building blocks for deep convolutional neural networks is disclosed. The search framework described herein utilizes a building block which incorporates branch and skip connections. At least some operations of the architecture of the building block are undefined and treated as hyperparameters which can be automatically selected and optimized for a particular task. The search framework uses random search over the reduced search space to generate a building block and repeats the building block multiple times to create a deep convolutional neural network.Type: ApplicationFiled: December 7, 2018Publication date: June 20, 2019Inventors: Jayanta Kumar Dutta, Jiayi Liu, Unmesh Kurup, Mohak Shah