Patents by Inventor Thomas William Randall Jacobs

Thomas William Randall Jacobs 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).

  • Patent number: 10943257
    Abstract: Techniques and systems are described for analyzing components of digital content. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital content.
    Type: Grant
    Filed: May 5, 2020
    Date of Patent: March 9, 2021
    Assignee: Adobe Inc.
    Inventors: Oliver Isaac Goldman, Thomas William Randall Jacobs, Kent Andrew Edmonds, Kevin Gary Smith, Pradeep Saikalyanachakravarthi Javangula, Ashley Manning Still
  • Publication number: 20200401380
    Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
    Type: Application
    Filed: August 31, 2020
    Publication date: December 24, 2020
    Applicant: Adobe Inc.
    Inventors: Thomas William Randall Jacobs, Peter Raymond Fransen, Kevin Gary Smith, Kent Andrew Edmonds, Jen-Chan Jeff Chien, Gavin Stuart Peter Miller
  • Patent number: 10795647
    Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
    Type: Grant
    Filed: October 16, 2017
    Date of Patent: October 6, 2020
    Assignee: Adobe, Inc.
    Inventors: Thomas William Randall Jacobs, Peter Raymond Fransen, Kevin Gary Smith, Kent Andrew Edmonds, Jen-Chan Jeff Chien, Gavin Stuart Peter Miller
  • Publication number: 20200265463
    Abstract: Techniques and systems are described for analyzing components of digital content. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital content.
    Type: Application
    Filed: May 5, 2020
    Publication date: August 20, 2020
    Applicant: Adobe Inc.
    Inventors: Oliver Isaac Goldman, Thomas William Randall Jacobs, Kent Andrew Edmonds, Kevin Gary Smith, Pradeep Saikalyanachakravarthi Javangula, Ashley Manning Still
  • Patent number: 10685375
    Abstract: Techniques and systems are described for analyzing components of digital marketing content as part of a digital marketing campaign. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital marketing content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital marketing content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital marketing content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital marketing content items for digital marketing campaigns.
    Type: Grant
    Filed: October 12, 2017
    Date of Patent: June 16, 2020
    Assignee: Adobe Inc.
    Inventors: Oliver Isaac Goldman, Thomas William Randall Jacobs, Kent Andrew Edmonds, Kevin Gary Smith, Pradeep Saikalyanachakravarthi Javangula, Ashley Manning Still
  • Publication number: 20190114680
    Abstract: Techniques and system are described to control output of digital marketing content with respect to a digital video that address the added complexities of digital video over other types of digital content, such as webpages. In one example, the techniques and systems are configured to control a time, at which, digital marketing content is to be output with respect to the digital video, e.g., by selecting a commercial break or output as a banner ad in conjunction with the video.
    Type: Application
    Filed: October 13, 2017
    Publication date: April 18, 2019
    Applicant: Adobe Systems Incorporated
    Inventors: Jen-Chan Jeff Chien, Thomas William Randall Jacobs, Kent Andrew Edmonds, Kevin Gary Smith, Peter Raymond Fransen, Gavin Stuart Peter Miller, Ashley Manning Still
  • Publication number: 20190114151
    Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
    Type: Application
    Filed: October 16, 2017
    Publication date: April 18, 2019
    Applicant: Adobe Systems Incorporated
    Inventors: Thomas William Randall Jacobs, Peter Raymond Fransen, Kevin Gary Smith, Kent Andrew Edmonds, Jen-Chan Jeff Chien, Gavin Stuart Peter Miller
  • Publication number: 20190114663
    Abstract: Techniques and systems are described for analyzing components of digital marketing content as part of a digital marketing campaign. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital marketing content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital marketing content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital marketing content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital marketing content items for digital marketing campaigns.
    Type: Application
    Filed: October 12, 2017
    Publication date: April 18, 2019
    Applicant: Adobe Systems Incorporated
    Inventors: Oliver Isaac Goldman, Thomas William Randall Jacobs, Kent Andrew Edmonds, Kevin Gary Smith, Pradeep Saikalyanachakravarthi Javangula, Ashley Manning Still
  • Publication number: 20190114664
    Abstract: Techniques and systems are described to enable users to optimize a digital marketing content system by analyzing an effect of components of digital marketing content on audience segments, environments of consumption, and channels of consumption. A computing device of an analytics system receives user interaction data describing an effect of user interaction with multiple items of digital marketing content on achieving an action for multiple audience segments. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system generates data identifying different aspects that likely had an effect on the achieving an action on the items of digital marketing content, such as components of the items of digital marketing content, environments of consumption, channels of consumption. The analytics system outputs a result based on the data in a user interface.
    Type: Application
    Filed: October 12, 2017
    Publication date: April 18, 2019
    Applicant: Adobe Systems Incorporated
    Inventors: Oliver Isaac Goldman, Thomas William Randall Jacobs, Kent Andrew Edmonds, Kevin Gary Smith, Pradeep Saikalyanachakravarthi Javangula, Ashley Manning Still
  • Publication number: 20190114672
    Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
    Type: Application
    Filed: October 16, 2017
    Publication date: April 18, 2019
    Applicant: Adobe Systems Incorporated
    Inventors: Thomas William Randall Jacobs, Peter Raymond Fransen, Kevin Gary Smith, Kent Andrew Edmonds, Jen-Chan Jeff Chien, Gavin Stuart Peter Miller
  • Publication number: 20190095949
    Abstract: Techniques and system are described to control output of digital marketing content with respect to digital content. This is achieved by leveraging additional insight that may be gained from external service systems that describe the digital content, e.g., social network systems, digital content review systems, and so forth. In one example, the techniques and systems are configured to collect social network data that describes social network communications communicated via a social network system. Natural language processing techniques are then performed as part of machine learning to detect interest of a user population associated with the social network communications.
    Type: Application
    Filed: September 26, 2017
    Publication date: March 28, 2019
    Applicant: Adobe Systems Incorporated
    Inventors: Jen-Chan Jeff Chien, Thomas William Randall Jacobs, Kent Andrew Edmonds, Kevin Gary Smith, Peter Raymond Fransen
  • Publication number: 20180234796
    Abstract: A digital medium environment is described to control provision of digital content within a physical environment to a mobile device associated with a user. User identification data and position data are received. The position data describes a physical location at which the mobile device is disposed within the physical environment. A user profile is selected based on the user identification data. The user profile describes user online interaction with digital content. Digital content is generated that is personalized based on the selected user profile and the position data. Output is then controlled of the generated digital content to the mobile device.
    Type: Application
    Filed: February 10, 2017
    Publication date: August 16, 2018
    Applicant: Adobe Systems Incorporated
    Inventors: Manaswi Saha, Thomas William Randall Jacobs, David M. Tompkins, Peter Raymond Fransen