Patents by Inventor SHAWN ALAN GAITHER

SHAWN ALAN GAITHER 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: 20210160221
    Abstract: Systems and techniques for privacy preserving document analysis are described that derive insights pertaining to a digital document without communication of the content of the digital document. To do so, the privacy preserving document analysis techniques described herein capture visual or contextual features of the digital document and creates a stamp representation that represents these features without included the content of the digital document. The stamp representation is projected into a stamp embedding space based on a stamp encoding model generated through machine learning techniques capturing feature patterns and interaction in the stamp representations. The stamp encoding model exploits these feature interactions to define similarity of source documents based on location within the stamp embedding space. Accordingly, the techniques described herein can determine a similarity of documents without having access to the documents themselves.
    Type: Application
    Filed: November 26, 2019
    Publication date: May 27, 2021
    Applicant: Adobe Inc.
    Inventors: Nikolaos Barmpalios, Ruchi Rajiv Deshpande, Randy Lee Swineford, Nargol Rezvani, Andrew Marc Greene, Shawn Alan Gaither, Michael Kraley
  • Publication number: 20210117666
    Abstract: Techniques are provided for identifying structural elements of a document. One Methodology includes generating a first channel of rasterized content by rasterizing a full page of the document and generating one or more additional channels of rasterized content from the page of the document by rasterizing one or more corresponding content types from the page of the document. Each of the one or more additional channels includes a specific type of content that is different from each of the other one or more additional channels. The methodology further includes inputting the first channel of rasterized content and the one or more additional channels of rasterized content into a machine learning (ML) model. The methodology continues with determining location and classification for each of a plurality of structural elements on the page of the document using the ML model.
    Type: Application
    Filed: October 17, 2019
    Publication date: April 22, 2021
    Applicant: Adobe Inc.
    Inventors: Verena Sabine Kaynig-Fittkau, Smitha Bangalore Naresh, Shawn Alan Gaither, Richard Cohn, Paul John Asente, Eylon Stroh, Emily Seminerio
  • Publication number: 20210110153
    Abstract: Techniques described herein implement heading identification and classification for a digital document in a digital medium environment. A document analysis system is leveraged to extract structural features from a digital document, identify heading candidates from among the structural features, validate the headings candidates, and classify validated headings into different headings types. The classified headings are then utilized to generate a sectioned version of the digital document (“sectioned document”) that is divided into different sections based on the headings. Further, a document directory is generated that includes the headings and that enables navigation to different sections of the sectioned document.
    Type: Application
    Filed: October 9, 2019
    Publication date: April 15, 2021
    Applicant: Adobe Inc.
    Inventors: Mohit Gupta, Uttam Dwivedi, Shawn Alan Gaither, Jayant Vaibhav Srivastava, Ashutosh Mehra
  • Publication number: 20210110151
    Abstract: Techniques are disclosed for identifying asides within a document, and detecting a display order of contents based of the identified asides. In a document, an “aside” represents a content region of the document that is distinct from the main content regions, and may be visually distinguishable from the main content region. In an example, a document is received, where the document lacks identification of asides. The document is analyzed to identify asides within the document. A display order of contents within the document is then determined, based on the identified asides. For example, in the display order, the asides are ordered between two segments of the main content and/or at a beginning or an end of the main content, but may not be ordered to be embedded in between a segment of the main content. The document is displayed in accordance with the display order.
    Type: Application
    Filed: October 10, 2019
    Publication date: April 15, 2021
    Applicant: Adobe Inc.
    Inventors: Sanjeev Tagra, Shawn Alan Gaither, Shagun Kush, Samarth Gupta, Sachin Soni, Nikolaos Barmpalios, Abhishek Jain, Naqushab Neyazee
  • Patent number: 10956731
    Abstract: Techniques described herein implement heading identification and classification for a digital document in a digital medium environment. A document analysis system is leveraged to extract structural features from a digital document, identify heading candidates from among the structural features, validate the headings candidates, and classify validated headings into different headings types. The classified headings are then utilized to generate a sectioned version of the digital document (“sectioned document”) that is divided into different sections based on the headings. Further, a document directory is generated that includes the headings and that enables navigation to different sections of the sectioned document.
    Type: Grant
    Filed: October 9, 2019
    Date of Patent: March 23, 2021
    Assignee: Adobe Inc.
    Inventors: Mohit Gupta, Uttam Dwivedi, Shawn Alan Gaither, Jayant Vaibhav Srivastava, Ashutosh Mehra
  • Publication number: 20200320329
    Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.
    Type: Application
    Filed: June 18, 2020
    Publication date: October 8, 2020
    Inventors: Trung Huu Bui, Hung Hai Bui, Shawn Alan Gaither, Walter Wei-Tuh Chang, Michael Frank Kraley, Pranjal Daga
  • Patent number: 10713519
    Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.
    Type: Grant
    Filed: June 22, 2017
    Date of Patent: July 14, 2020
    Assignee: ADOBE INC.
    Inventors: Trung Huu Bui, Hung Hai Bui, Shawn Alan Gaither, Walter Wei-Tuh Chang, Michael Frank Kraley, Pranjal Daga
  • Publication number: 20180373952
    Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.
    Type: Application
    Filed: June 22, 2017
    Publication date: December 27, 2018
    Inventors: Trung Huu Bui, Hung Hai Bui, Shawn Alan Gaither, Walter Wei-Tuh Chang, Michael Frank Kraley, Pranjal Daga
  • Patent number: 10133813
    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed at predicting values for an electronic form. In embodiments, the method can include forming synonym groupings of form field labels for a number of users. The synonym groupings can be based on an analysis of the similarity of form field values that are associated with form field labels. In embodiments a predictive model may be generated from these synonym groupings. The predictive model can correlate the synonym groupings of one user with synonym groupings of one or more additional users to enable a determination of one or more predicted form field values for the one user based on a queried form field label even though the one user may have never submitted an electronic form with the queried form field label. Other embodiments may be described and/or claimed.
    Type: Grant
    Filed: August 12, 2015
    Date of Patent: November 20, 2018
    Assignee: Adobe Systems Incorporated
    Inventors: Shawn Alan Gaither, Eylon Stroh, Priyank Mathur, Randy Swineford
  • Publication number: 20170046622
    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed at predicting values for an electronic form. In embodiments, the method can include forming synonym groupings of form field labels for a number of users. The synonym groupings can be based on an analysis of the similarity of form field values that are associated with form field labels. In embodiments a predictive model may be generated from these synonym groupings. The predictive model can correlate the synonym groupings of one user with synonym groupings of one or more additional users to enable a determination of one or more predicted form field values for the one user based on a queried form field label even though the one user may have never submitted an electronic form with the queried form field label. Other embodiments may be described and/or claimed.
    Type: Application
    Filed: August 12, 2015
    Publication date: February 16, 2017
    Inventors: SHAWN ALAN GAITHER, EYLON STROH, PRIYANK MATHUR, RANDY SWINEFORD