Patents by Inventor Seyyedeh Qazale Mirsharif
Seyyedeh Qazale Mirsharif 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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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: 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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Patent number: D961601Type: GrantFiled: April 23, 2019Date of Patent: August 23, 2022Assignee: Figure Eight Technologies, Inc.Inventors: Seyyedeh Qazale Mirsharif, Jennifer Prendki, Kiran Vajapey, Robert Munro