Patents by Inventor Chun Lok
Chun Lok 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: 11943761Abstract: A user equipment (UE) may make a joint decision of adaptive receive diversity (ARD) and adaptive transmit diversity (ATD) configurations, including transmit (Tx) and receive (Rx) antennas selection and/or blanking based on downlink (DL) and uplink (UL) traffic conditions. The UE may disable at least one Tx chain for a transmission of a codebook-based sounding reference signal (SRS) (SRS-CB) based on one or more of at least one DL traffic condition or at least one UL traffic condition, and transmit, to a base station, upon disabling the at least one Tx chain, the SRS-CB via an antenna associated with at least one active Tx chain.Type: GrantFiled: September 21, 2021Date of Patent: March 26, 2024Assignee: QUALCOMM IncorporatedInventors: Peter Pui Lok Ang, Enoch Shiao-Kuang Lu, Alexei Yurievitch Gorokhov, Aamod Khandekar, Brian Clarke Banister, Raghu Narayan Challa, Kuo-Chun Lee, Arvind Vardarajan Santhanam, Jianming Zhu, Arash Ebadi Shahrivar, Pranay Sudeep Rungta
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Patent number: 11922682Abstract: Disease detection from medical images is provided. In various embodiments, a medical image of a patient is read. The medical image is provided to a trained anatomy segmentation network. A feature map is received from the trained anatomy segmentation network. The feature map indicates the location of at least one feature within the medical image. The feature map is provided to a trained classification network. The trained classification network was pre-trained on a plurality of feature map outputs of the segmentation network. A disease detection is received from the trained classification network. The disease detection indicating the presence or absence of a predetermined disease.Type: GrantFiled: April 2, 2021Date of Patent: March 5, 2024Assignee: MERATIVE US L.P.Inventors: Mehdi Moradi, Chun Lok Wong
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Patent number: 11763931Abstract: Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.Type: GrantFiled: April 8, 2019Date of Patent: September 19, 2023Assignee: MERATIVE US L.P.Inventors: Alexandros Karargyris, Chun Lok Wong, Joy Wu, Mehdi Moradi
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Publication number: 20230205295Abstract: An example non-transitory computer-readable medium includes instructions to monitor a time-averaged power consumption of a computing device that includes a plurality of power-consuming components including a processor, and compare the time-averaged power consumption to a first threshold to detect if the time-averaged power consumption exceeds the first threshold. In response to detecting that the time-averaged power consumption of the computing device exceeds the first threshold, the instructions are to decrease an operational power of the processor from a normal operating power, and start monitoring an instantaneous power consumption of the computing device to determine whether to make a further decrease or an increase to the operational power of the processor.Type: ApplicationFiled: June 3, 2020Publication date: June 29, 2023Applicant: Hewlett-Packard Development Company, L.P.Inventors: Robert Cleveland Brooks, Chun Lok Ng, Mark Andrew Piwonka, Michael Richard Durham
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Publication number: 20220405596Abstract: Methods and systems for performing transfer learning with basis scaling and pruning. One method includes obtaining a pre-trained deep convolutional neural network (DCNN), decomposing each weight matrix of the DCNN, and decomposing each convolutional layer by applying the respective decomposed weight matrix to the convolution layer to form a first layer which comprises the left matrix for convolution, and a second layer which comprises the right matrix for convolution. The method also includes providing a basis-scaling convolutional layer having a weight matrix that is derived by a function of singular values and the right singular vectors and training the basis scaling factors of the basis-scaling convolutional layers.Type: ApplicationFiled: June 16, 2021Publication date: December 22, 2022Inventors: Chun Lok Wong, Mehdi Moradi, Satyananda Kashyap
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Publication number: 20220230068Abstract: A computer-implemented method, a computer program product, and a computer system for introducing channel-scaling layers in a deep neural network. A computer receives a pre-trained deep neural network including convolutional layers followed by respective ones of activation layers, adds channel-scaling layers after the respective ones of the activation layers, where each of the channel-scaling layers includes scaling weights. The computer trains the scaling weights in the channel-scale layers. The computer removes, in the convolutional layers, channels whose corresponding scaling weights are lower than a predetermined threshold. The computer removes the channel-scaling layers. In response to determining that at least one convergence criterion is met, the computer provides a finally trained deep neural network.Type: ApplicationFiled: January 21, 2021Publication date: July 21, 2022Inventors: Chun Lok Wong, Mehdi Moradi, Satyananda Kashyap
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Patent number: 11246539Abstract: A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.Type: GrantFiled: October 11, 2019Date of Patent: February 15, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Vaishnavi Subramanian, Hongzhi Wang, Tanveer Syeda-Mahmood, Joy Tzung-yu Wu, Chun Lok Wong
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Patent number: 11244755Abstract: Mechanisms are provided to implement an automated medical imaging report generator which receives an input medical image and inputs the input medical image into a machine learning (ML) computer model trained to predict finding labels based on patterns of image features extracted from the medical image. The ML computer model generates a prediction of a finding label applicable to the input medical image in terms of a finding label prediction output vector. Based on the finding label prediction output vector, a lookup operation is performed, in a medical report database of previously processed medical imaging report data structures, to find a matching medical imaging report data structure corresponding to the finding label. An output medical imaging report is generated for the input medical image based on natural language content of the matching medical imaging report data structure.Type: GrantFiled: October 2, 2020Date of Patent: February 8, 2022Assignee: International Business Machines CorporationInventors: Tanveer Syeda-Mahmood, Chun Lok Wong, Joy Tzung-yu Wu, Yaniv Gur, Anup Pillai, Ashutosh Jadhav, Satyananda Kashyap, Mehdi Moradi, Alexandros Karargyris, Hongzhi Wang
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Publication number: 20210224995Abstract: Disease detection from medical images is provided. In various embodiments, a medical image of a patient is read. The medical image is provided to a trained anatomy segmentation network. A feature map is received from the trained anatomy segmentation network. The feature map indicates the location of at least one feature within the medical image. The feature map is provided to a trained classification network. The trained classification network was pre-trained on a plurality of feature map outputs of the segmentation network. A disease detection is received from the trained classification network. The disease detection indicating the presence or absence of a predetermined disease.Type: ApplicationFiled: April 2, 2021Publication date: July 22, 2021Inventors: Mehdi Moradi, Chun Lok Wong
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Patent number: 11023783Abstract: Systems and methods generate a segmentation network for image segmentation using global optimization. A method for automatic generation of at least one segmentation network includes providing an initial set of hyperparameters to construct a segmentation network. The hyperparameters define operations for a set of block structures and connections between the block structures. The segmentation network is trained using a first set of images with ground truth. An objective function value for the trained segmentation network is generated using a second set of images having ground truth. The set of hyperparameters is updated by performing a derivative-free optimization algorithm on the objective function value to construct an updated segmentation network. The training of the segmentation network, the generating of the objective function, and the updating of the set of hyperparameters for the updated segmentation network are iterated to generate a network architecture for the segmentation network.Type: GrantFiled: September 11, 2019Date of Patent: June 1, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventor: Chun Lok Wong
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Patent number: 10991100Abstract: Disease detection from medical images is provided. In various embodiments, a medical image of a patient is read. The medical image is provided to a trained anatomy segmentation network. A feature map is received from the trained anatomy segmentation network. The feature map indicates the location of at least one feature within the medical image. The feature map is provided to a trained classification network. The trained classification network was pre-trained on a plurality of feature map outputs of the segmentation network. A disease detection is received from the trained classification network. The disease detection indicating the presence or absence of a predetermined disease.Type: GrantFiled: January 9, 2020Date of Patent: April 27, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Mehdi Moradi, Chun Lok Wong
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Publication number: 20210106286Abstract: A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.Type: ApplicationFiled: October 11, 2019Publication date: April 15, 2021Inventors: Vaishnavi Subramanian, Hongzhi Wang, Tanveer Syeda-Mahmood, Joy Tzung-yu Wu, Chun Lok Wong
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Publication number: 20210073588Abstract: Systems and methods generate a segmentation network for image segmentation using global optimization. A method for automatic generation of at least one segmentation network includes providing an initial set of hyperparameters to construct a segmentation network. The hyperparameters define operations for a set of block structures and connections between the block structures. The segmentation network is trained using a first set of images with ground truth. An objective function value for the trained segmentation network is generated using a second set of images having ground truth. The set of hyperparameters is updated by performing a derivative-free optimization algorithm on the objective function value to construct an updated segmentation network. The training of the segmentation network, the generating of the objective function, and the updating of the set of hyperparameters for the updated segmentation network are iterated to generate a network architecture for the segmentation network.Type: ApplicationFiled: September 11, 2019Publication date: March 11, 2021Inventor: Chun Lok Wong
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Publication number: 20210074000Abstract: Systems and methods generate a segmentation network for image segmentation using global optimization. A method for automatic generation of at least one segmentation network includes providing an initial set of hyperparameters to construct a segmentation network. The hyperparameters define operations for a set of block structures and connections between the block structures. The segmentation network is trained using a first set of images with ground truth. An objective function value for the trained segmentation network is generated using a second set of images having ground truth. Generating the objective function includes setting the objective function to a predetermined value responsive to identifying an untrainable condition of the trained initial segmentation network. The set of hyperparameters is updated by performing an optimization algorithm on the objective function value to construct an updated segmentation network.Type: ApplicationFiled: September 11, 2019Publication date: March 11, 2021Inventor: Chun Lok Wong
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Patent number: 10943353Abstract: Systems and methods generate a segmentation network for image segmentation using global optimization. A method for automatic generation of at least one segmentation network includes providing an initial set of hyperparameters to construct a segmentation network. The hyperparameters define operations for a set of block structures and connections between the block structures. The segmentation network is trained using a first set of images with ground truth. An objective function value for the trained segmentation network is generated using a second set of images having ground truth. Generating the objective function includes setting the objective function to a predetermined value responsive to identifying an untrainable condition of the trained initial segmentation network. The set of hyperparameters is updated by performing an optimization algorithm on the objective function value to construct an updated segmentation network.Type: GrantFiled: September 11, 2019Date of Patent: March 9, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventor: Chun Lok Wong
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Patent number: 10817758Abstract: Methods for image segmentation and smoothing of image segmentations are provided. In various embodiments, a plurality of training images is provided to a segmentation network. A candidate segmentation is obtained from the segmentation network for each of the plurality of training images. Each candidate segmentation is compared to a ground truth segmentation to compute a loss metric for each candidate segmentation. Based on the gradient of the loss, the segmentation network is trained to minimize level set smoothing energy. In various embodiments, an input image is downsampled from a first resolution to a second, lower resolution. The downsampled image is provided to a segmentation network. A segmentation at the second resolution is obtained from the segmentation network. The segmentation from the second resolution is upsampled to the first resolution. The input image and the upsampled segmentation are provided at the first resolution to a convolutional network.Type: GrantFiled: June 20, 2018Date of Patent: October 27, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Hui Tang, Mehdi Moradi, Chun Lok Wong
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Patent number: 10803591Abstract: 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes is provided. In various embodiments, an artificial neural network is trained to label an anatomical feature in medical imagery by: i) providing at least one medical image to the artificial neural network; ii) determining from the artificial neural network a predicted segmentation for the at least one medical image; iii) comparing the predicted segmentation to ground truth segmentation, and computing therefrom a loss function, the loss function having an exponential-logarithmic term; and iv) updating the artificial neural network based on the loss function.Type: GrantFiled: August 28, 2018Date of Patent: October 13, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventor: Chun Lok Wong
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Publication number: 20200321101Abstract: Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.Type: ApplicationFiled: April 8, 2019Publication date: October 8, 2020Inventors: Alexandros Karargyris, Chun Lok Wong, Joy Wu, Mehdi Moradi
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Publication number: 20200294664Abstract: The present disclosure provides a computer-implemented method of graphically representing events relating to a plurality of users. The method comprises: graphically representing a knowledge base, the knowledge base comprising concepts that are linked by relations; receiving a plurality of interim graphs each relating to an event, said interim graphs each comprising a plurality of nodes including a node identifying the user associated with the event and a node identifying a concept describing an outcome of the event; linking the plurality of interim graphs with the knowledge base to form a relation between the nodes in the interim graphs identifying the concepts and corresponding concepts in the knowledge base to produce a graphical representation of a user profile including the knowledge base augmented with the interim graphs relating to a plurality of users.Type: ApplicationFiled: March 14, 2019Publication date: September 17, 2020Inventors: Georgios STOILOS, Domenico CORAPI, Hugh SIMPSON, Forat LATIF, Chun Lok LING, Szymon WARTAK, Samuel WRIGHT, Mohammad KHODADADI
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Publication number: 20200294666Abstract: The present disclosure provides a computer-implemented method of building a user profile for a medical diagnostic system. The method includes receiving a new event including data describing a consultation with the user from a conversation module of the diagnostic system. The method also includes encoding the new event using JavaScript Object Notation (JSON). The method also includes storing the encoded new event in a queue of events. The method also includes decoding and translating the new event into a form compatible with the user profile. The method also includes adding the translated new event to the user profile.Type: ApplicationFiled: April 4, 2019Publication date: September 17, 2020Inventors: Georgios STOILOS, Domenico CORAPI, Hugh SIMPSON, Forat LATIF, Chun Lok LING, Szymon WARTAK, Samuel WRIGHT, Mohammad KHODADADI