Abstract: Described herein are mechanisms to allow users to access functionality of applications in a suite of applications. In a first aspect, when a query relating to functionality of an application is received from a user, an index containing both top-level and sub-level functionality is searched. Results are ranked using a trained machine learning model using both context describing user interactions and the search results. A subset of the ranked results are presented to the user as options. In a second aspect the index can comprise entries describing functionality from other applications so that results presented to the user can include cross-application functionality. In a third aspect, the index can be searched using the context prior to receiving a query and adjusting the user interface based on the results. In a fourth aspect, the system can recommend other applications and/or devices that are better suited to a user's intent.

Abstract: Described herein are mechanisms to allow users to access functionality of applications in a suite of applications. In a first aspect, when a query relating to functionality of an application is received from a user, an index containing both top-level and sub-level functionality is searched. Results are ranked using a trained machine learning model using both context describing user interactions and the search results. A subset of the ranked results are presented to the user as options. In a second aspect the index can comprise entries describing functionality from other applications so that results presented to the user can include cross-application functionality. In a third aspect, the index can be searched using the context prior to receiving a query and adjusting the user interface based on the results. In a fourth aspect, the system can recommend other applications and/or devices that are better suited to a user's intent.

Abstract: Described herein are mechanisms to allow users to access functionality of applications in a suite of applications. In a first aspect, when a query relating to functionality of an application is received from a user, an index containing both top-level and sub-level functionality is searched. Results are ranked using a trained machine learning model using both context describing user interactions and the search results. A subset of the ranked results are presented to the user as options. In a second aspect the index can comprise entries describing functionality from other applications so that results presented to the user can include cross-application functionality. In a third aspect, the index can be searched using the context prior to receiving a query and adjusting the user interface based on the results. In a fourth aspect, the system can recommend other applications and/or devices that are better suited to a user's intent.

Abstract: Author-created digital agents and content corpora for electronic documents are described. A content corpora service can include application programming interfaces such as for adding content to a corpus, attaching a corpus to a document, retrieving corpora associated with an author, and searching content of a corpus. An author-created digital agent can receive a request from a consumer of a document, determine corpora associated with the document, formulate a query based on the request, and search content associated with the corpora associated with the document.

Abstract: An application search system providing an application suggestion service can perform a method including receiving at least one capability from each of a plurality of applications and registering the at least one capability of each of the plurality of applications as registered capabilities. Each capability can include a feature of that application and one or more parameters to be used by that application for that feature. When the application search system receives an intent of a user, the system matches the intent of the user with at least one feature from the registered capabilities, identifies corresponding values for the one or more parameters of the at least one feature from the intent of the user, and sends the corresponding values to at least one application associated with a matched feature from the set of matched features. Results returned from the at least one application can be provided to the user.

Abstract: Described herein are mechanisms to allow users to access functionality of applications in a suite of applications. In a first aspect, when a query relating to functionality of an application is received from a user, an index containing both top-level and sub-level functionality is searched. Results are ranked using a trained machine learning model using both context describing user interactions and the search results. A subset of the ranked results are presented to the user as options. In a second aspect the index can comprise entries describing functionality from other applications so that results presented to the user can include cross-application functionality. In a third aspect, the index can be searched using the context prior to receiving a query and adjusting the user interface based on the results. In a fourth aspect, the system can recommend other applications and/or devices that are better suited to a user's intent.

Abstract: Author-created digital agents and content corpora for electronic documents are described. A content corpora service can include application programming interfaces such as for adding content to a corpus, attaching a corpus to a document, retrieving corpora associated with an author, and searching content of a corpus. An author-created digital agent can receive a request from a consumer of a document, determine corpora associated with the document, formulate a query based on the request, and search content associated with the corpora associated with the document.

Abstract: Content-aware search suggestions can be provided by performing entity extraction on content in a file that a user is consuming, authoring, or editing; storing the extracted entities in an index; and generating terms and phrases related to the extracted entities and storing the terms and phrases in the index. In response to receiving an input of at least one character in a search field: a set of search suggestions can be provided based on the terms and phrases that appear in the index that satisfy a condition with respect to the at least one character from the input.

Abstract: Author-created digital agents and content corpora for electronic documents are described. A content corpora service can include application programming interfaces such as for adding content to a corpus, attaching a corpus to a document, retrieving corpora associated with an author, and searching content of a corpus. An author-created digital agent can receive a request from a consumer of a document, determine corpora associated with the document, formulate a query based on the request, and search content associated with the corpora associated with the document.

Abstract: Author-created digital agents and content corpora for electronic documents are described. A content corpora service can include application programming interfaces such as for adding content to a corpus, attaching a corpus to a document, retrieving corpora associated with an author, and searching content of a corpus. An author-created digital agent can receive a request from a consumer of a document, determine corpora associated with the document, formulate a query based on the request, and search content associated with the corpora associated with the document.

Abstract: One or more signals are used to identify regions of interest of an image. The signals are applied to the image to generate one or more models that are based on the regions of interest. The models may present different perspectives of the image by emphasizing various features and focal points. The models may be ranked and displayed according to a scoring paradigm that is based on one or more signals. Multi-tiered feedback mechanisms allow for the collection of user intent and/or other forms of explicit input. Feedback associated to the models may be obtained and used to generate additional models that are based on one or more signals and the feedback. The feedback may also be stored and utilized for machine learning purposes.

Abstract: One or more signals are used to identify regions of interest of an image. The signals are applied to the image to generate one or more models that are based on the regions of interest. The models may present different perspectives of the image by emphasizing various features and focal points. The models may be ranked and displayed according to a scoring paradigm that is based on one or more signals. Multi-tiered feedback mechanisms allow for the collection of user intent and/or other forms of explicit input. Feedback associated to the models may be obtained and used to generate additional models that are based on one or more signals and the feedback. The feedback may also be stored and utilized for machine learning purposes.

Abstract: One or more signals are used to identify regions of interest of an image. The signals are applied to the image to generate one or more models that are based on the regions of interest. The models may present different perspectives of the image by emphasizing various features and focal points. The models may be ranked and displayed according to a scoring paradigm that is based on one or more signals. Multi-tiered feedback mechanisms allow for the collection of user intent and/or other forms of explicit input. Feedback associated to the models may be obtained and used to generate additional models that are based on one or more signals and the feedback. The feedback may also be stored and utilized for machine learning purposes.

Abstract: Input data may define an approach, model or theme for presenting text in a two-dimensional or a three-dimensional display environment. The input data may be analyzed to determine a legibility score of the text. The legibility score may be based on a number of factors including the characteristics of the text, characteristics of the environment, an aggregate contrast ratio derived from aggregate luminance values, a relative importance of legibility, other contextual information and/or combinations thereof. If the legibility score does not meet at least one threshold, one or more treatments may be applied to the input data. For example, a treatment may involve a modification of the text size, font, text position, text color and/or modifications to the display environment, to improve the legibility of the text and/or the overall aesthetics of the display environment and the text.

Abstract: One or more signals are used to identify regions of interest of an image. The signals are applied to the image to generate one or more models that are based on the regions of interest. The models may present different perspectives of the image by emphasizing various features and focal points. The models may be ranked and displayed according to a scoring paradigm that is based on one or more signals. Multi-tiered feedback mechanisms allow for the collection of user intent and/or other forms of explicit input. Feedback associated to the models may be obtained and used to generate additional models that are based on one or more signals and the feedback. The feedback may also be stored and utilized for machine learning purposes.

Abstract: Technologies are described herein for content authoring based on user intent. During content authoring, intent data can be obtained along with content data. The intent data may be obtained directly from the author or inferred from documents related to the content data. The intent data indicates the author's intent as to how to present the content data without utilizing explicit formatting instructions. A layout may be generated for the content data based on the intent data. Additional data may be utilized to generate the layout, such as the capabilities of a display device and the preferences of the content consumer.

Abstract: Concepts and technologies are described herein for transforming data into consumable content. In accordance with the concepts and technologies disclosed herein, a computing device can execute a transformation engine for transforming data into the consumable content. The computing device can be configured to analyze the data to identify relationships within data elements or other portions of the data. The computing device also can determine a visualization model to apply to the data and to choose a world based upon the determined visualization model. The computing device can obtain rules associated with the selected or chosen world, and can apply the rules to the data to generate the output. In some embodiments, the computing device can be configured to obtain and apply feedback to the output.

Abstract: Concepts and technologies are described herein for transforming data into consumable content. In accordance with the concepts and technologies disclosed herein, a computing device can execute a transformation engine for transforming data into the consumable content. The computing device can be configured to analyze the data to identify relationships within data elements or other portions of the data. The computing device also can determine a visualization model to apply to the data and to choose a world based upon the determined visualization model. The computing device can obtain rules associated with the selected or chosen world, and can apply the rules to the data to generate the output. In some embodiments, the computing device can be configured to obtain and apply feedback to the output.

Abstract: Concepts and technologies are described herein for creating variations when transforming data into consumable content. In accordance with the concepts and technologies disclosed herein, a computing device can execute a transformation engine for transforming data into the consumable content. The computing device can be configured to analyze the data to identify relationships among data elements or other portions of the data, and to identify any possible approaches to transforming the data (“worlds”) based upon the relationships and the data. The worlds can be ranked and selected based upon rank. The computing device can obtain rules associated with the selected and apply the rules to the data to generate the output. In some embodiments, the computing device can be configured to obtain and apply feedback regarding the world selection.

Abstract: Concepts and technologies are described herein for transforming data into consumable content. In accordance with the concepts and technologies disclosed herein, a computing device can execute a transformation engine for transforming data into the consumable content. The computing device can be configured to analyze the data to identify relationships within data elements or other portions of the data. The computing device also can determine a visualization model to apply to the data and to choose a world based upon the determined visualization model. The computing device can obtain rules associated with the selected or chosen world, and can apply the rules to the data to generate the output. In some embodiments, the computing device can be configured to obtain and apply feedback to the output.