Patents by Inventor Monchu CHEN
Monchu CHEN 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: 11176415Abstract: Image annotation includes: accessing initial object prediction information associated with an image, wherein the initial object prediction information includes a plurality of initial predictions associated with a plurality of objects in the image, including bounding box information associated with the plurality of objects; presenting the image and at least a portion of the initial object prediction information to be displayed; receiving adjusted object prediction information pertaining to at least some of the plurality of objects, wherein the adjusted object prediction information is obtained from a user input made via a user interface configured for a user to make annotation adjustments to at least some of the initial object prediction information; and outputting updated object prediction information, wherein the updated object prediction information is based at least in part on the adjusted object prediction information.Type: GrantFiled: May 8, 2019Date of Patent: November 16, 2021Assignee: Figure Eight Technologies, Inc.Inventors: Humayun Irshad, Seyyedeh Qazale Mirsharif, Kiran Vajapey, Monchu Chen, Caiqun Xiao, Robert Munro
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Patent number: 11107222Abstract: A technique is disclosed for automating tracking of annotated objects and improves the throughput and efficiency of existing methods while maintaining a degree of accuracy comparable to a human annotator. In particular, the disclosed technique provides an automated annotated object tracking tool that allows machine-learning teams to annotate an object within a frame and have that annotation persist across frames as the annotated object is tracked within a series of frames, still ensuring that every frame is accurately reviewed by a human where high quality annotation is required. This technique incorporates human feedback via a user adjustment that allows the tool to adapt and improve its accuracy in tracking an annotated object across a sequence of frames.Type: GrantFiled: October 18, 2019Date of Patent: August 31, 2021Assignee: Figure Eight Technologies, Inc.Inventors: Kiran Vajapey, Robert Munro, Joseph Richard Cloughley, Matthew Allen Gordon, Humayun Irshad, Monchu Chen, Seyyedeh Qazale Mirsharif, Caiqun Xiao
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Patent number: 11017266Abstract: Image annotation includes: accessing an image and a plurality of annotation data sets for the image, wherein the plurality of annotation data sets are made by a plurality of contributors, and the image has a plurality of original image channels; aggregating the plurality of annotation data sets to obtain an aggregated annotation data set for the image; and outputting the aggregated annotation data set. Aggregating the plurality of annotation data sets to obtain an aggregated annotation data set for the image includes: generating an additional image channel based at least in part on weight averages of confidence measures of the plurality of contributors; and applying an object detection model to at least a part of the plurality of original image channels and at least a part of the additional image channel to generate the aggregated annotation data set.Type: GrantFiled: May 7, 2019Date of Patent: May 25, 2021Assignee: Figure Eight Technologies, Inc.Inventors: Humayun Irshad, Seyyedeh Qazale Mirsharif, Kiran Vajapey, Monchu Chen, Caiqun Xiao, Robert Munro
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Patent number: 10713535Abstract: A computer system may provide Encrypted Deep Learning Service (EDLS) to a client. The computer system includes one or more processors and memory storing instructions. When instructions are executed by the one or more processors, the instructions cause the computer system to perform acts including: receiving training data from the client, where the training data comprise cipher images that are encrypted using an orthogonal transformation that hides sensitive information in original images. The acts further include training a deep neural network using the training data in the computer system; and producing cipher inference using the deep neural network when the computer system receives new data including new images encrypted using the orthogonal transformation.Type: GrantFiled: September 14, 2018Date of Patent: July 14, 2020Assignee: NovuMind LimitedInventor: Monchu Chen
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Publication number: 20200151884Abstract: A technique is disclosed for automating tracking of annotated objects and improves the throughput and efficiency of existing methods while maintaining a degree of accuracy comparable to a human annotator. In particular, the disclosed technique provides an automated annotated object tracking tool that allows machine-learning teams to annotate an object within a frame and have that annotation persist across frames as the annotated object is tracked within a series of frames, still ensuring that every frame is accurately reviewed by a human where high quality annotation is required. This technique incorporates human feedback via a user adjustment that allows the tool to adapt and improve its accuracy in tracking an annotated object across a sequence of frames.Type: ApplicationFiled: October 18, 2019Publication date: May 14, 2020Inventors: Kiran Vajapey, Robert Munro, Joseph Richard Cloughley, Matthew Allen Gordon, Humayun Irshad, Monchu Chen, Seyyedeh Qazale Mirsharif, Caiqun Xiao
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Publication number: 20200065739Abstract: A profile configuration comprising desired feature configurations for contributors for a task is provided. Among a plurality of available contributors, a selected set of one or more contributors that substantially meets a set of one or more objectives is identified, with the identification being based at least in part on the profile configuration. The selected set of one or more contributors is recruited to perform the task.Type: ApplicationFiled: January 10, 2019Publication date: February 27, 2020Inventors: Natã Miccael Barbosa, Monchu Chen, Jennifer Prendki
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Publication number: 20190362186Abstract: Image annotation includes: accessing initial object prediction information associated with an image, wherein the initial object prediction information includes a plurality of initial predictions associated with a plurality of objects in the image, including bounding box information associated with the plurality of objects; presenting the image and at least a portion of the initial object prediction information to be displayed; receiving adjusted object prediction information pertaining to at least some of the plurality of objects, wherein the adjusted object prediction information is obtained from a user input made via a user interface configured for a user to make annotation adjustments to at least some of the initial object prediction information; and outputting updated object prediction information, wherein the updated object prediction information is based at least in part on the adjusted object prediction information.Type: ApplicationFiled: May 8, 2019Publication date: November 28, 2019Inventors: Humayun Irshad, Seyyedeh Qazale Mirsharif, Kiran Vajapey, Monchu Chen, Caiqun Xiao, Robert Munro
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Publication number: 20190362185Abstract: Image annotation includes: accessing an image and a plurality of annotation data sets for the image, wherein the plurality of annotation data sets are made by a plurality of contributors, and the image has a plurality of original image channels; aggregating the plurality of annotation data sets to obtain an aggregated annotation data set for the image; and outputting the aggregated annotation data set. Aggregating the plurality of annotation data sets to obtain an aggregated annotation data set for the image includes: generating an additional image channel based at least in part on weight averages of confidence measures of the plurality of contributors; and applying an object detection model to at least a part of the plurality of original image channels and at least a part of the additional image channel to generate the aggregated annotation data set.Type: ApplicationFiled: May 7, 2019Publication date: November 28, 2019Inventors: Humayun Irshad, Seyyedeh Qazale Mirsharif, Kiran Vajapey, Monchu Chen, Caiqun Xiao, Robert Munro
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Patent number: 10489918Abstract: A technique is disclosed for automating tracking of annotated objects and improves the throughput and efficiency of existing methods while maintaining a degree of accuracy comparable to a human annotator. In particular, the disclosed technique provides an automated annotated object tracking tool that allows machine-learning teams to annotate an object within a frame and have that annotation persist across frames as the annotated object is tracked within a series of frames, still ensuring that every frame is accurately reviewed by a human where high quality annotation is required. This technique incorporates human feedback via a user adjustment that allows the tool to adapt and improve its accuracy in tracking an annotated object across a sequence of frames.Type: GrantFiled: December 20, 2018Date of Patent: November 26, 2019Assignee: Figure Eight Technologies, Inc.Inventors: Kiran Vajapey, Robert Munro, Joseph Richard Cloughley, Matthew Allen Gordon, Humayun Irshad, Monchu Chen, Seyyedeh Qazale Mirsharif, Caiqun Xiao
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Publication number: 20190347806Abstract: A technique is disclosed for automating tracking of annotated objects and improves the throughput and efficiency of existing methods while maintaining a degree of accuracy comparable to a human annotator. In particular, the disclosed technique provides an automated annotated object tracking tool that allows machine-learning teams to annotate an object within a frame and have that annotation persist across frames as the annotated object is tracked within a series of frames, still ensuring that every frame is accurately reviewed by a human where high quality annotation is required. This technique incorporates human feedback via a user adjustment that allows the tool to adapt and improve its accuracy in tracking an annotated object across a sequence of frames.Type: ApplicationFiled: December 20, 2018Publication date: November 14, 2019Inventors: Kiran Vajapey, Robert Munro, Joseph Richard Cloughley, Matthew Allen Gordon, Humayun Irshad, Monchu Chen, Seyyedeh Qazale Mirsharif, Caiqun Xiao
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Publication number: 20190087689Abstract: A computer system may provide Encrypted Deep Learning Service (EDLS) to a client. The computer system includes one or more processors and memory storing instructions. When instructions are executed by the one or more processors, the instructions cause the computer system to perform acts including: receiving training data from the client, where the training data comprise cipher images that are encrypted using an orthogonal transformation that hides sensitive information in original images. The acts further include training a deep neural network using the training data in the computer system; and producing cipher inference using the deep neural network when the computer system receives new data including new images encrypted using the orthogonal transformation.Type: ApplicationFiled: September 14, 2018Publication date: March 21, 2019Applicant: NovuMind LimitedInventor: Monchu CHEN