Abstract: Non-limiting examples of the present disclosure describe processing that generates intelligent inferences of authoring from analysis of attributes associated with a digital file being imported in an application/service. Examples described herein are configured to work with any type of application/service including an authoring application/service. For instance, a request to import a digital file is received in an application/service. The application/service may be configured to analyze the digital file and generate authoring inferences based on an analysis of attributes of the digital file. For example, a conversion data model may be utilized to identify a file type of the digital file, analyze attributes of the identified digital file (e.g. content portions, layout, formatting, metadata, etc.) and output file data in a format that is tailored for the application/service based on authoring inferences.

Abstract: A system is configured to automatically enhance user content by dynamically generating alternative layouts given an original layout of the user content. The alternative layouts are generated based on determined roles for a number of different content elements on a canvas and/or detected semantic relationships between content elements. Once the alternative layouts are generated, the system can evaluate the alternative layouts and assign a score to each of the alternative layouts. The system can then identify recommended alternative layouts that are suitable for a particular purpose (e.g., a professional presentation, a teaching lesson, etc.), and provide the recommended alternative layouts for display on a device of a user.

Abstract: Non-limiting examples of the present disclosure describe processing that generates intelligent inferences of authoring from analysis of attributes associated with a digital file being imported in an application/service. Examples described herein are configured to work with any type of application/service including an authoring application/service. For instance, a request to import a digital file is received in an application/service. The application/service may be configured to analyze the digital file and generate authoring inferences based on an analysis of attributes of the digital file. For example, a conversion data model may be utilized to identify a file type of the digital file, analyze attributes of the identified digital file (e.g. content portions, layout, formatting, metadata, etc.) and output file data in a format that is tailored for the application/service based on authoring inferences.

Abstract: A system is configured to automatically enhance user content by dynamically generating alternative layouts given an original layout of the user content. The alternative layouts are generated based on determined roles for a number of different content elements on a canvas and/or detected semantic relationships between content elements. Once the alternative layouts are generated, the system can evaluate the alternative layouts and assign a score to each of the alternative layouts. The system can then identify recommended alternative layouts that are suitable for a particular purpose (e.g., a professional presentation, a teaching lesson, etc.), and provide the recommended alternative layouts for display on a device of a user.

Abstract: Non-limiting examples of the present disclosure describe processing that generates intelligent inferences of authoring from analysis of attributes associated with a digital file being imported in an application/service. Examples described herein are configured to work with any type of application/service including an authoring application/service. For instance, a request to import a digital file is received in an application/service. The application/service may be configured to analyze the digital file and generate authoring inferences based on an analysis of attributes of the digital file. For example, a conversion data model may be utilized to identify a file type of the digital file, analyze attributes of the identified digital file (e.g. content portions, layout, formatting, metadata, etc.) and output file data in a format that is tailored for the application/service based on authoring inferences.

Abstract: Improved computer efficiency for document navigation is provided. A transparent overlay is automatically applied to an interactive content element to prevent inadvertent interaction with the element during scrolling operations. In some examples, upon determining a user's intent is to navigate a parent document comprising an embedded interactive element, the overlay is applied, and user-interaction with the interactive element is temporarily disabled. Upon determining the user's intent is to interact with the interactive element, the overlay is removed, and user-interaction with the interactive element is enabled. When the overlay is removed, a contextual hint, such as via a user interface control, can be provided to indicate how to continue navigating the parent document.

Abstract: A color coding engine and a comparison engine are provided. A color coding engine may be utilized to detect logical layout object attributes in a flow format document and apply a unique color to textual elements associated with each logical layout object attribute. The resulting color coded document may be saved as a target flow format document. The target flow format document may be converted to a fixed format document and then converted by a conversion engine to a flow format document. The resulting converted flow format document may be saved as an output flow format document. A comparison engine may be utilized to compare the output flow format document and the target flow format document to determine if layout information has been properly preserved in the document conversion process.

Abstract: Non-limiting examples of the present disclosure describe processing that generates intelligent inferences of authoring from analysis of attributes associated with a digital file being imported in an application/service. Examples described herein are configured to work with any type of application/service including an authoring application/service. For instance, a request to import a digital file is received in an application/service. The application/service may be configured to analyze the digital file and generate authoring inferences based on an analysis of attributes of the digital file. For example, a conversion data model may be utilized to identify a file type of the digital file, analyze attributes of the identified digital file (e.g. content portions, layout, formatting, metadata, etc.) and output file data in a format that is tailored for the application/service based on authoring inferences.

Abstract: Improved computer efficiency for document navigation is provided. A transparent overlay is automatically applied to an interactive content element to prevent inadvertent interaction with the element during scrolling operations. In some examples, upon determining a user's intent is to navigate a parent document comprising an embedded interactive element, the overlay is applied, and user-interaction with the interactive element is temporarily disabled. Upon determining the user's intent is to interact with the interactive element, the overlay is removed, and user-interaction with the interactive element is enabled. When the overlay is removed, a contextual hint, such as via a user interface control, can be provided to indicate how to continue navigating the parent document.

Abstract: Technologies are described herein for obtaining and processing intent-based feedback. Authored content may be generated by the use of one or more layouts. The generated layouts may then be utilized to present the content to the author or other user. The author may further provide feedback to request the generated layout be adjusted or re-generated. The feedback may include overriding feedback that overrides the intent interpretation used in the layout, and/or intent feedback that changes or adds more intent data for the content data.

Abstract: Technologies are described herein for inferring the layout intent associated with explicitly formatted document elements in a document. The layout type of a document having explicitly formatted document elements is determined. Once the layout type for the document has been determined, the layout intent of explicitly formatted document elements in the document may be determined based, at least in part, on the determined layout type of the document. Heuristic algorithms and/or machine learning classifiers may determine the layout intent of the explicitly formatted document elements in the document. An intent-based document is then created using the inferred layout intent for some or all of the explicitly formatted document elements in the document. The intent-based document may then be provided to an intent-based rendering or authoring application for rendering based upon the inferred layout intent.

Abstract: A color coding engine and a comparison engine are provided. A color coding engine may be utilized to detect logical layout object attributes in a flow format document and apply a unique color to textual elements associated with each logical layout object attribute. The resulting color coded document may be saved as a target flow format document. The target flow format document may be converted to a fixed format document and then converted by a conversion engine to a flow format document. The resulting converted flow format document may be saved as an output flow format document. A comparison engine may be utilized to compare the output flow format document and the target flow format document to determine if layout information has been properly preserved in the document conversion process.

Abstract: A fixed format document conversion engine and associated method for converting a fixed format document into a flow format document. The fixed format document conversion engine includes a sequence of layout analysis engines and semantic analysis engines to analyzes the base physical layout information obtained from the fixed format document to enrich, modify, and classify the physical layout information into progressively more advanced physical layout information and, ultimately, semantic layout information. The semantic layout information is mapped and serialized into a selected flow format document with a high level of flowability.