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Owen W Schoppe

from Orinda, CA
Age ~41
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Also known as:
Owen Winne Schoppe
Phone and address:
15 Snow Ct, Orinda, CA 94563
Related to:
Gerald T O'Donnell, 87Peter Lynch, 67Kathleen Donnelly, 65Michael Donnelly, 70Walter DonnellyMitya Schoppe, 55
Connected to:
Andrea M Schoppe, 36Andrew L Schoppert, 47Charles J Schoppe, 87Gerald E Schoppe, 79Jeremy M Schopper, 46Kevin W Schoppe, 45Laurel A Schoppet, 60Steve Schoppenhorst, 48Thomas V Schoppe, 58Yoshi M Schoppe, 55

Owen Schoppe Phones & Addresses

  • 15 Snow Ct, Orinda, CA 94563
  • South San Francisco, CA
  • San Francisco, CA
  • Chicago, IL
  • Ellsworth, ME
  • Tivoli, NY

Publications

Us Patents

Machine-Learning Based Generation Of Text Style Variations For Digital Content Items

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US Patent:
20220245322, Aug 4, 2022
Filed:
Jan 29, 2021
Appl. No.:
17/163162
Inventors:
- San Francisco CA, US
Owen Winne Schoppe - Orinda CA, US
Xing Han - Austin TX, US
Michael Reynolds Sollami - Cambridge MA, US
Brian J. Lonsdorf - Moss Beach CA, US
Alan Martin Ross - San Francisco CA, US
David J. Woodward - Bozeman MT, US
Sonke Rohde - San Francisco CA, US
International Classification:
G06F 40/103
G06F 40/284
G06T 11/60
G06N 3/04
Abstract:
An online system generates a set of content item variations for a reference content item that include different styles of text for the content item. The different styles of text are generated by applying machine-learned style transfer models, for example, neural network based models to reference text of the reference content item. The text variations retain the textual content of the reference text but are synthesized with different styles. The online system can provide the content item variations to users on an online experimental platform to collect user interaction information that may indicate how users respond to different styles of text. The online system or the content providers can effectively target users with content items that include the style of text the users respond to based on the collected information.

Heuristics-Based Detection Of Image Space Suitable For Overlaying Media Content

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US Patent:
20220245820, Aug 4, 2022
Filed:
Jan 29, 2021
Appl. No.:
17/162245
Inventors:
- San Francisco CA, US
Jessica Lundin - Seattle WA, US
Michael Reynolds Sollami - Cambridge MA, US
Brian J. Lonsdorf - Moss Beach CA, US
David J. Woodward - Bozeman MT, US
Owen Winne Schoppe - Orinda CA, US
Sonke Rohde - San Francisco CA, US
International Classification:
G06T 7/11
G06T 5/00
G06T 5/20
G06K 9/00
G06T 7/13
G06T 7/194
Abstract:
Disclosed herein are system, method and computer readable storage medium for detecting space suitable for overlaying media content onto an image. The system receives an image which may be an image or a video frame. The image is processed using a number of image processing techniques in order to automatically propose spaces for inserting media content onto the image. The proposed spaces may then be further analyzed using a heuristics-based approach to select bounding boxes for inserting media content. Subsequently, one or more media content items may be selected for insertion onto the bounding boxes on the image. The system may then cause a display of the image with the selected media content item overlaid onto the image within the selected bounding boxes.

Neural Network Based Detection Of Image Space Suitable For Overlaying Media Content

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US Patent:
20220180581, Jun 9, 2022
Filed:
Dec 9, 2020
Appl. No.:
17/116944
Inventors:
- San Francisco CA, US
Michael Reynolds Sollami - Cambridge MA, US
Alan Martin Ross - San Francisco CA, US
Brian J. Lonsdorf - Belmont CA, US
David James Woodward - Westfield IN, US
Owen Winne Schoppe - Orinda CA, US
Sönke Rohde - San Francisco CA, US
International Classification:
G06T 11/60
G06K 9/62
G06T 7/70
G06N 3/08
Abstract:
Disclosed herein are system, method and computer readable storage medium for detecting space suitable for overlaying media content onto an image. The system receives a candidate image which may be an image or a video frame. The candidate image is then input into a neural network. The neural network may output coordinates and one or more dimensions representing one or more bounding boxes for inserting media content into the candidate image. The one or more bounding boxes may be transmitted with a request for a media content item to be displayed in a bounding box. In response to the request the media content item may be received, and the candidate image and the media content item overlaid on top of the candidate image within the bounding box may be displayed.

Automatic Image Conversion

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US Patent:
20230129240, Apr 27, 2023
Filed:
Jan 26, 2022
Appl. No.:
17/649045
Inventors:
- San Francisco CA, US
Sönke Rohde - San Francisco CA, US
Alan Martin Ross - San Francisco CA, US
David James Woodward - Bozeman MT, US
Jessica Lundin - Seattle WA, US
Owen Winne Schoppe - Orinda CA, US
Brian J. Lonsdorf - Soquel CA, US
Aashish Jain - Cambridge MA, US
International Classification:
G06F 9/451
G06F 9/54
G06N 3/08
G06N 3/04
Abstract:
Techniques are disclosed for automatically converting a layout image to a text-based representation. In the disclosed techniques, a server computer system receives a layout image that includes a plurality of portions representing a plurality of user interface (UI) elements included in a UI design. The server computer system transforms, via executed of a trained residual neural network (ResNet), the layout image to a text-based representation of the layout image that specifies coordinates of bounding regions of the plurality of UI elements included in the UI design, where the text-based representation is usable to generate program code executable to render the UI design. The disclosed techniques may advantageously automate one or more portions of a UI design process and, as a result save time and computing resources via the execution of an image to text-based conversion ResNet machine learning model.

One-To-Many Automatic Content Generation

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US Patent:
20230129431, Apr 27, 2023
Filed:
Jan 26, 2022
Appl. No.:
17/649016
Inventors:
- San Francisco CA, US
Sönke Rohde - San Francisco CA, US
Alan Martin Ross - San Francisco CA, US
David James Woodward - Bozeman MT, US
Jessica Lundin - Seattle WA, US
Owen Winne Schoppe - Orinda CA, US
Brian J. Lonsdorf - Soquel CA, US
Aashish Jain - Cambridge MA, US
International Classification:
G06F 3/04845
G06N 3/04
G06V 10/771
G06V 10/762
G06V 10/82
Abstract:
Techniques are disclosed for automatically generating new content using a trained 1-to-N generative adversarial network (GAN) model. In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The computer system automatically generates, using the trained 1-to-N GAN model, N different versions of new content, where a given version of new content is automatically generated based on the current content and one of N different style codes, where the value of N is at least two. After generating the N different versions of new content, the computer system transmits them to the computing device. The disclosed techniques may advantageously automate a content generation process, thereby saving time and computing resources via execution of the 1-to-N GAN machine learning model.

Learning Affinities Through Design Variations

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US Patent:
20230039283, Feb 9, 2023
Filed:
Oct 24, 2022
Appl. No.:
17/972258
Inventors:
- , US
Jessica Lundin - Seattle WA, US
Owen Winne Schoppe - Orinda CA, US
Sönke Rohde - San Francisco CA, US
Alan Ross - San Francisco CA, US
David James Woodward - Mountain View CA, US
Assignee:
Salesforce.com, inc. - San Francisco CA
International Classification:
G06F 16/9035
G06F 16/904
G06F 16/9038
G06F 16/9535
Abstract:
Disclosed herein are system, method, and computer program product embodiments for determining a user-preferred feature type. An embodiment operates by maintaining user-presented features associated with user-presented records, wherein the user-presented features comprise one or more user-presented feature types. After receiving a user-desired feature of the user-presented features, a user-preferred feature type of the user-presented feature types is determined based on the user-presented features and the user-desired feature. Thereafter, a new record and associated feature are to be presented with the new feature being of the user-preferred type.

Machine Learning-Based Inference Of Granular Font Properties

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US Patent:
20210334666, Oct 28, 2021
Filed:
Apr 22, 2020
Appl. No.:
16/854913
Inventors:
- San Francisco CA, US
Owen Winne Schoppe - Orinda CA, US
Alan Martin Ross - San Francisco CA, US
Brian J. Lonsdorf - Belmont CA, US
David James Woodward - Westfield IN, US
Sönke Rohde - San Francisco CA, US
Michael Reynolds Sollami - Cambridge MA, US
Chetan Ramaiah - Palo Alto CA, US
International Classification:
G06N 5/02
G06N 20/00
G06F 40/109
G06F 17/16
Abstract:
A textual properties model is used to infer values for certain font properties of interest given certain text-related data, such as rendered text images. The model may be used for numerous purposes, such as aiding with document layout, identifying font families that are similar to a given font families, and generating new font families with specific desired properties. In some embodiments, the model is trained from a combination of synthetic data that is labeled with values for the font properties of interest, and partially-labeled data from existing “real-world” documents.

Custom User Interface Generation For Completing A Predicted Task

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US Patent:
20210240320, Aug 5, 2021
Filed:
Jul 28, 2020
Appl. No.:
16/941176
Inventors:
- San Francisco CA, US
Brian J. LONSDORF - Moss Beach CA, US
Owen Winne SCHOPPE - Orinda CA, US
Alan Martin ROSS - San Francisco CA, US
Jessica LUNDIN - Bellevue WA, US
Sönke ROHDE - San Francisco CA, US
Assignee:
salesforce.com, inc. - San Francisco CA
International Classification:
G06F 3/0484
G06F 9/451
G06N 20/00
G06N 5/04
G06F 3/0481
Abstract:
Disclosed herein are system, method, and computer program product embodiments for generating custom user interfaces (UIs) for completing a task. One embodiment operates by obtaining contextual information associated with a user and an application on a user device operated by the user, where the application includes a plurality of UI elements. Then, determining the user is attempting to complete a first task within the application based on the contextual information and a prediction model. The embodiment further operates by obtaining a minimum set of UI elements required for the first task. Further, the embodiment operates by transmitting a first custom UI including the minimum set of UI elements for the first task to the user device for display to the user.
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Owen W Schoppe from Orinda, CA, age ~41 Get Report