Patents by Inventor Scott A. Cohen

Scott A. Cohen 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).

  • Publication number: 20240350172
    Abstract: An adjustable bone fixation device for moving a bone includes at least two strut units, at least one meter and a system controller. Each strut unit includes a motor to move a strut. The meter measures a signal generated by the motor during the movement of its strut. The signal is useful in determining a torque or a current of the motor. The system controller activates at least two of the motors and uses the determined torque or the determined current to identify if there is a clinical situation of the bone or a system issue and provides an alert accordingly.
    Type: Application
    Filed: April 19, 2024
    Publication date: October 24, 2024
    Applicant: Synthes GmbH
    Inventors: Albert A. Montello, Scott P. Lavoritano, Oren Cohen, Shahar Harari, Dror Albo
  • Patent number: 12118752
    Abstract: The present disclosure relates to a color classification system that accurately classifies objects in digital images based on color. In particular, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color mappings to match objects to colors. Indeed, the color classification system can accurately and efficiently detect the color of an object utilizing one or more color similarity regions generated in the multidimensional color space.
    Type: Grant
    Filed: April 11, 2022
    Date of Patent: October 15, 2024
    Assignee: Adobe Inc.
    Inventors: Zhihong Ding, Scott Cohen, Zhe Lin, Mingyang Ling
  • Patent number: 12093306
    Abstract: The present disclosure relates to an object selection system that accurately detects and optionally automatically selects user-requested objects (e.g., query objects) in digital images. For example, the object selection system builds and utilizes an object selection pipeline to determine which object detection neural network to utilize to detect a query object based on analyzing the object class of a query object. In particular, the object selection system can identify both known object classes as well as objects corresponding to unknown object classes.
    Type: Grant
    Filed: March 28, 2023
    Date of Patent: September 17, 2024
    Assignee: Adobe Inc.
    Inventors: Scott Cohen, Zhe Lin, Mingyang Ling
  • Publication number: 20240304319
    Abstract: Disclosed are systems and methods for providing automated or semi-automated technical support for patients using medical devices, such as continuous glucose monitoring systems. Disclosed embodiments of automated tech support system include collection and storage of copies of streams of medical device data on multiple servers, analysis and comparison of data streams, remote tech support initiation and usage of the automated tech support system for providing improved products and services by storing and analyzing historical tech support data.
    Type: Application
    Filed: May 20, 2024
    Publication date: September 12, 2024
    Inventors: Andrew Attila PAL, Leif N. BOWMAN, Eric COHEN, Basab DATTARAY, Edward DAY, Apurv Ullas KAMATH, Aarthi MAHALINGAM, Dana Denea MINOR, Scott A. MOSS, Neil S. PURI, Eli REIHMAN, Conrad WOODS, Laurie L. BERG, Jorge VALDES
  • Patent number: 12086130
    Abstract: Aspects related to a resource-constrained system are described herein that can provide object storage services after a service interruption is resolved, even if all of the transactions that were pending and incomplete prior to the service interruption have not yet been recovered and/or executed. For example, file systems implemented by computing systems of the resource-constrained system may treat each file or directory as a separate object. Thus, a transaction directed to one file may not affect the file's directory or other files in the directory. As a result, the resource-constrained system can achieve read-after-write consistency without first recovering and executing the pending, incomplete transactions. Instead, read-after-write consistency for an object can be achieved simply by completing any pending, incomplete transaction directed to that object.
    Type: Grant
    Filed: September 29, 2020
    Date of Patent: September 10, 2024
    Assignee: Amazon Technologies, Inc.
    Inventors: Michael F Brown, Vandana Rungta, Ernest S Cohen, Srinivasa Rao Vempati, Arkady Michael Degtiarov, Benjamin Scott Dow
  • Publication number: 20240261550
    Abstract: An apparatus for treating skin has a console with a user input device and a handpiece assembly. The handpiece assembly is configured to treat skin. A fluid line provides fluid communication between the console and the handpiece assembly. A manifold system is coupled to the console and controlled by the user input device. The manifold system is configured to hold releasably a plurality of fluid sources and deliver fluid from at least one of the plurality of fluid sources to the handpiece assembly.
    Type: Application
    Filed: April 17, 2024
    Publication date: August 8, 2024
    Inventors: Roger Ignon, Scott Mallett, Abraham Solano, William Cohen
  • Patent number: 12053607
    Abstract: An apparatus for treating skin has a console with a user input device and a handpiece assembly. The handpiece assembly is configured to treat skin. A fluid line provides fluid communication between the console and the handpiece assembly. A manifold system is coupled to the console and controlled by the user input device. The manifold system is configured to hold releasably a plurality of fluid sources and deliver fluid from at least one of the plurality of fluid sources to the handpiece assembly.
    Type: Grant
    Filed: October 16, 2023
    Date of Patent: August 6, 2024
    Assignee: HydraFacial LLC
    Inventors: Roger Ignon, Scott Mallett, Abraham Solano, William Cohen
  • Patent number: 12045963
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems detect, via a graphical user interface of a client device, a user selection of an object portrayed within a digital image. The disclosed systems determine, in response to detecting the user selection of the object, a relationship between the object and an additional object portrayed within the digital image. The disclosed systems receive one or more user interactions for modifying the object. The disclosed systems modify the digital image in response to the one or more user interactions by modifying the object and the additional object based on the relationship between the object and the additional object.
    Type: Grant
    Filed: November 23, 2022
    Date of Patent: July 23, 2024
    Assignee: Adobe Inc.
    Inventors: Scott Cohen, Zhe Lin, Zhihong Ding, Luis Figueroa, Kushal Kafle
  • Patent number: 12020414
    Abstract: The present disclosure relates to an object selection system that accurately detects and automatically selects target instances of user-requested objects (e.g., a query object instance) in a digital image. In one or more embodiments, the object selection system can analyze one or more user inputs to determine an optimal object attribute detection model from multiple specialized and generalized object attribute models. Additionally, the object selection system can utilize the selected object attribute model to detect and select one or more target instances of a query object in an image, where the image includes multiple instances of the query object.
    Type: Grant
    Filed: August 15, 2022
    Date of Patent: June 25, 2024
    Assignee: Adobe Inc.
    Inventors: Scott Cohen, Zhe Lin, Mingyang Ling
  • Publication number: 20240169630
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to synthesize shadows for object(s). For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access an object mask of the object depicting in the digital image. The disclosed systems further combine the object mask, the digital image, and a noise representation to generate a combined representation. Moreover, the disclosed systems generate a shadow for the object from the combined representation and further generates the modified digital image by combining the shadow with the digital image.
    Type: Application
    Filed: December 7, 2023
    Publication date: May 23, 2024
    Inventors: Soo Ye Kim, Zhe Lin, Scott Cohen, Jianming Zhang, Luis Figueroa
  • Publication number: 20240169685
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems receive a digital image from a client device. The disclosed systems detect, utilizing a shadow detection neural network, an object portrayed in the digital image. The disclosed systems detect, utilizing the shadow detection neural network, a shadow portrayed in the digital image. The disclosed systems generate, utilizing the shadow detection neural network, an object-shadow pair prediction that associates the shadow with the object.
    Type: Application
    Filed: November 23, 2022
    Publication date: May 23, 2024
    Inventors: Luis Figueroa, Zhe Lin, Zhihong Ding, Scott Cohen
  • Publication number: 20240169628
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides a graphical user interface experience to move objects and generate new shadows within a digital image scene. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems receive a selection to position an object in a first location within the scene. Further, the disclosed systems composite an image by placing the object at the first location within the scene of the digital image. Moreover, the disclosed systems generate a modified digital image having a shadow of the object where the shadow is consistent with the scene and provides the modified digital image to the client device.
    Type: Application
    Filed: September 1, 2023
    Publication date: May 23, 2024
    Inventors: Soo Ye Kim, Zhe Lin, Scott Cohen, Jianming Zhang, Luis Figueroa, Zhihong Ding
  • Publication number: 20240169624
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate utilizing a segmentation neural network, an object mask for each object of a plurality of objects of a digital image. The disclosed systems detect a first user interaction with an object in the digital image displayed via a graphical user interface. The disclosed systems surface, via the graphical user interface, the object mask for the object in response to the first user interaction. The disclosed systems perform an object-aware modification of the digital image in response to a second user interaction with the object mask for the object.
    Type: Application
    Filed: November 23, 2022
    Publication date: May 23, 2024
    Inventors: Jonathan Brandt, Scott Cohen, Zhe Lin, Zhihong Ding, Darshan Prasad, Matthew Joss, Celso Gomes, Jianming Zhang, Olena Soroka, Klaas Stoeckmann, Michael Zimmermann, Thomas Muehrke
  • Publication number: 20240169631
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to remove a shadow for an object. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access a shadow mask of the shadow in a first location. Further, the disclosed systems generate the modified digital image without the shadow by generating a fill for the first location that preserves a visible location of the first location. Moreover, the disclosed systems generate the digital image without the shadow for the object by combining the fill with the digital image.
    Type: Application
    Filed: December 7, 2023
    Publication date: May 23, 2024
    Inventors: Soo Ye Kim, Zhe Lin, Scott Cohen, Jianming Zhang, Luis Figueroa, Zhihong Ding
  • Publication number: 20240168617
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems detect a selection of an object portrayed in a digital image displayed within a graphical user interface of a client device. The disclosed systems provide, for display within the graphical user interface in response to detecting the selection of the object, an interactive window displaying one or more attributes of the object. The disclosed systems receive, via the interactive window, a user interaction to change an attribute from the one or more attributes. The disclosed systems modify the digital image by changing the attribute of the object in accordance with the user interaction.
    Type: Application
    Filed: November 23, 2022
    Publication date: May 23, 2024
    Inventors: Zhe Lin, Scott Cohen, Kushal Kafle
  • Publication number: 20240169501
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate, utilizing a segmentation neural network and without user input, object masks for objects in a digital image. The disclosed systems determine foreground and background abutting an object mask. The disclosed systems generate an expanded object mask by expanding the object mask into the foreground abutting the object mask by a first amount and expanding the object mask into the background abutting the object mask by a second amount that differs from the first amount. The disclosed systems inpaint a hole corresponding to the expanded object mask utilizing an inpainting neural network.
    Type: Application
    Filed: November 23, 2022
    Publication date: May 23, 2024
    Inventors: Qing Liu, Zhe Lin, Luis Figueroa, Scott Cohen
  • Publication number: 20240171848
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems provide, for display within a graphical user interface of a client device, a digital image displaying a plurality of objects, the plurality of objects comprising a plurality of different types of objects. The disclosed systems generate, utilizing a segmentation neural network and without user input, an object mask for objects of the plurality of objects. The disclosed systems determine, utilizing a distractor detection neural network, a classification for the objects of the plurality of objects. The disclosed systems remove at least one object from the digital image, based on classifying the at least one object as a distracting object, by deleting the object mask for the at least one object.
    Type: Application
    Filed: November 23, 2022
    Publication date: May 23, 2024
    Inventors: Luis Figueroa, Zhihong Ding, Scott Cohen, Zhe Lin, Qing Liu
  • Publication number: 20240169500
    Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image comprising a first region that includes content and a second region to be inpainted. Noise is then added to the image to obtain a noisy image, and a plurality of intermediate output images are generated based on the noisy image using a diffusion model trained using a perceptual loss. The intermediate output images predict a final output image based on a corresponding intermediate noise level of the diffusion model. The diffusion model then generates the final output image based on the intermediate output image. The final output image includes inpainted content in the second region that is consistent with the content in the first region.
    Type: Application
    Filed: November 22, 2022
    Publication date: May 23, 2024
    Inventors: Haitian Zheng, Zhe Lin, Jianming Zhang, Connelly Stuart Barnes, Elya Shechtman, Jingwan Lu, Qing Liu, Sohrab Amirghodsi, Yuqian Zhou, Scott Cohen
  • Publication number: 20240169502
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems detect, via a graphical user interface of a client device, a user selection of an object portrayed within a digital image. The disclosed systems determine, in response to detecting the user selection of the object, a relationship between the object and an additional object portrayed within the digital image. The disclosed systems receive one or more user interactions for modifying the object. The disclosed systems modify the digital image in response to the one or more user interactions by modifying the object and the additional object based on the relationship between the object and the additional object.
    Type: Application
    Filed: November 23, 2022
    Publication date: May 23, 2024
    Inventors: Scott Cohen, Zhe Lin, Zhihong Ding, Luis Figueroa, Kushal Kafle
  • Publication number: 20240161344
    Abstract: Systems, methods, and non-transitory computer-readable media embed a trained neural network within a digital image. For instance, in one or more embodiments, the systems identify out-of-gamut pixel values of a digital image in a first gamut, where the digital image is converted to the first gamut from a second gamut. Furthermore, the systems determine target pixel values of a target version of the digital image in the first gamut that correspond to the out-of-gamut pixel values. The systems train a neural network to predict the target pixel values in the first gamut based on the out-of-gamut pixel values. The systems embed the neural network within the digital image in the second gamut to allow for extraction of the embedded neural network from the digital image to restore the digital image to a larger gamut digital image.
    Type: Application
    Filed: November 7, 2022
    Publication date: May 16, 2024
    Inventors: Hoang M. Le, Michael S. Brown, Brian Price, Scott Cohen