Adobe, Inc.

Thomas T Donahue

Richard Sinn

Allan Morgan Young

Guy Nicholas

Chun Hao Wang

Oliver Brdiczka

Kyoung Tak Kim

Richard Pong Nam Sinn

Jakub Plichta

Gabriel Palma Coelho

Keenan Villani-Holland

Neha Gautam

The present disclosure relates to systems, methods, and non-transitory computer-readable media that recommends application features of software applications based on in-application behavior and provides the recommendations within a dynamically updating graphical user interface. For instance, in one or more embodiments, the disclosed systems utilize behavioral signals reflecting the behavior of a user with respect to one or more software applications to recommend application features of the software application(s). For instance, in some cases, the disclosed systems recommend an application feature related to recent activity user, an application feature from a curated recommendation list that has yet to be viewed, and/or an application feature determined via machine learning. In some embodiments, the disclosed systems dynamically update a graphical user interface of a client device in real time as the user utilizes the client device to access and navigate the software application(s) to display these recommendations.

Generating personalized in-application recommendations utilizing in-application behavior and intent

An improved analytics system dynamically generates, integrates, and deploys multiple models for use related to a particular user and/or user segment. The integrated model analytics system can deploy a set of models specifically identified for a user from a group of models related to the service. This set of models can be used to provide recommendations for the user related to content of the service. As the user continues to interact with the service, the integrated model analytics system can dynamically update multiple models from the set of models based on real-time user interactions. These updated models can be used to generate updated recommendations for the user that can be used to dynamically update content presented to the user. In addition, this updated model can be used when providing recommendations for users in other user segments.

Dynamically integrating interactive machine learning multi-models

Certain embodiments involve automatically controlling modifications to typeface designs. For example, a typeface design application provides a design interface for modifying a design of an input character from a typeface. The typeface design application accesses a machine-learning model that is trained, using multiple training typefaces, to recognize the input character as a reference character. The typeface design application receives, via the design interface, an input modifying the design of the input character. The typeface design application determines that the machine-learning model cannot match the reference character to the input character having a modified design. The typeface design application outputs, via the design interface, an indicator that the input character having the modified design is not recognized as the reference character.

Automatically controlling modifications to typeface designs with machine-learning models

A content manipulation application provides a graphical interface for editing graphical content. The graphical interface includes first and second control elements for performing first and second manipulations of the graphical content. If the first control element is selected, the content management application switches the graphical interface to a trackpad mode. The trackpad mode disables the second control element and thereby prevents the second control element from performing the second manipulation. While the graphical interface is in the trackpad mode, the content management application receives an input in an input area that lacks the first control element and performs the first manipulation of the graphical content responsive to receiving the input. Subsequent to the first manipulation being performed, the graphical interface is switched out of the trackpad mode, thereby enabling the second control element to perform the second manipulation.

Selectively enabling trackpad functionality in graphical interfaces

Image session identifier techniques are described. In one or more implementations, a request is received to create an image sharing session and a session identifier is generated for the image sharing session that is configured to act as a credential to join the image sharing session as a member. A communication is formed that includes the generated session identifier and that is configured for communication via a network to an originator of the request.

Image session identifier techniques

Systems and methods are disclosed for identifying digital fonts. In particular, in one or more embodiments, the disclosed systems and methods detect a digital font, font characteristics, and a text structure with regard to digital text in an electronic document. Moreover, in one or more embodiments, the disclosed systems and methods utilize the detected font characteristics and text structure to search for complementary digital fonts. In particular, one or more embodiments include systems and methods that generate a search query based on determined font characteristics and/or text structures and utilize the search query to automatically identify, recommend, and provide complementary digital fonts for utilization in the electronic document.

Automatically identifying complementary digital fonts based on digital text in electronic documents

A typeface processing application accesses a character skeleton, which has control points and curves defined by control points, and a character outline surrounding the character skeleton. The typeface processing application computes, for a design parameter of a design application, a graphics control dataset based on a particular control point. Doing so involves identifying a pair of control point positions and a pair of expansions of the character outline, where each position or expansion pair corresponds to a pair of design parameter values. The typeface processing application generates the graphics control dataset, which includes intermediate positions of the control point between the pair of positions and intermediate expansions of the character outline between the pair of expansions. The typeface processing application outputs the graphics control dataset to the design application, which can display, responsive to a selection of a design parameter value, modified character designs generated using the graphics control dataset.

Graphics control data for performing skeleton-based modifications of a typeface design

Automatic techniques to tune images based on metadata are described. In one or more implementations, image data and metadata are received that references a geographic location. Responsive to a user input, the image data is processed using one or more tuning parameters that correspond to the geographic location to change one or more visual characteristics of the image data.

Growing community of inventors

Carlsbad, CA, United States of America

Thomas T Donahue