Abstract: Examples of the present disclosure describe systems and methods for determining authoritative documents based on implicit interlinking and communication signals. In aspects, a search operation may be initiated from one or more applications or services. The search operation may be processed to identify search information, such as one or more content items, content topics, or entities. The identified search information may be used to search one or more data sources for implicit relationships between the search information and content items and/or entities stored by the data sources. The results of the search may be collected and ranked according to one or more criteria. The ranked results may be provided in response to the search operation.

Abstract: A method and system for identifying potential collaborators for a document may include determining a lifecycle stage of a document, determining a subject matter of the document based at least in part on content of the document. The method and system identify one or more potential collaborators to contribute to the document based at least in part on the subject matter of the document, a lifecycle stage of the document, or collaborator-specific information associated with each of the one or more potential collaborators. The method and system may also include providing data relating to the one or more potential collaborators for display.

Abstract: A method and system for intelligently organizing one or more groups of relevant files may include retrieving a user data signal including user-specific data, analyzing the user data signal to identify a parameter relating to file relevance, identifying one or more relevant files in a storage medium based on the parameter at least one of a user category property, a lifecycle stage property, a relevant activity property, or an activity level property of one or more files in the storage medium, organizing the one or more relevant file into the one or more groups of relevant files, and providing for display data relating to the one or more groups of relevant files.

Abstract: An in-application search service receives a query from a user attempting to locate information within a document. If a current index and current vector table do not exist, a content indexing service produces an index and a vector table based on the current content of the document. The vector table is based on semantic models that have been pre-trained. The query is tokenized and one of three different processing paths are taken based on the length of the tokenized query. For a single term query, a semantic search and prefix search are performed and the results are combined. If the length of tokenized query exceeds a threshold, a semantic search produces the results. Otherwise, search results are produced based on both prefix fanout and semantic fanout. Results are deduplicated, snippets are extracted, and the results and/or snippets are presented to the user.

Abstract: A method and system for intelligently ranking search results may include receiving a search request containing one or more terms for performing a search, providing the one or more terms for conducting a search, and receiving a search results index containing a list of a plurality of documents, each of the plurality of documents corresponding to one of the one or more terms or to one or more other terms associated with the at least one of the one or more terms.

Abstract: A method and system for receiving data relating to one or more activities performed by a user on a document within a specific time period, the one or more activities being performed by using an application, analyzing the data to identify a category of user activity based at least on the type of activity performed on the document, and transmitting a signal to a device for storage in association with the document, the signal including the identified category.

Abstract: A method and system for providing a personalized user interface and personalized search suggestions when using an application is disclosed. The method includes receiving a request for providing a personalized user interface such as a personalized menu for an application and examining a history of the user's usage of the application, a history of other user's usage of the application, and/or the content of the current document in the application, before identifying one or more suggested options offered by the application based on one or more of these factors. A list may then be generated and provided for being displayed as part of the personalized user interface. Upon receiving an indication of a user's intent to perform a search within the application, the user's usage history, usage history of users with similar activities, and/or content of the current document may be examined to provide the user with a list of search suggestions.

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: A method and system for providing a personalized user interface and personalized search suggestions when using an application is disclosed. The method includes receiving a request for providing a personalized user interface such as a personalized menu for an application and examining a history of the user's usage of the application, a history of other user's usage of the application, and/or the content of the current document in the application, before identifying one or more suggested options offered by the application based on one or more of these factors. A list may then be generated and provided for being displayed as part of the personalized user interface. Upon receiving an indication of a user's intent to perform a search within the application, the user's usage history, usage history of users with similar activities, and/or content of the current document may be examined to provide the user with a list of search suggestions.

Abstract: A document lens service identifies a document profile specific to an end-user that is indicative of a model document for the end-user. When a productivity document is opened to be consumed by the end-user in a user interface to a productivity application, the service modifies the productivity document based at least on the document profile to align the productivity document with the model document. The productivity document is then rendered as-modified in the user interface.

Abstract: A document lens service identifies an end-user targeted to consume a productivity document being authored in a user interface to a productivity application. The service then also identifies a document profile specific to the end-user indicative of a model document for the end-user to consume. While the productivity document is being authored, suggestions may be surfaced in the user interface for aligning the productivity document with the model document.

Abstract: A document lens service identifies an end-user targeted to consume a productivity document being authored in a user interface to a productivity application. The service then also identifies a document profile specific to the end-user indicative of a model document for the end-user to consume. While the productivity document is being authored, suggestions may be surfaced in the user interface for aligning the productivity document with the model document.

Abstract: A document lens service identifies a document profile specific to an end-user that is indicative of a model document for the end-user. When a productivity document is opened to be consumed by the end-user in a user interface to a productivity application, the service modifies the productivity document based at least on the document profile to align the productivity document with the model document. The productivity document is then rendered as-modified in the user interface.

Abstract: A text span forming either a single word or a series of two or more words that a user intended to select is predicted. A document and a location pointer that indicates a particular location in the document are received and input to different candidate text span generation methods. A ranked list of one or more scored candidate text spans is received from each of the different candidate text span generation methods. A machine-learned ensemble model is used to re-score each of the scored candidate text spans that is received from each of the different candidate text span generation methods. The ensemble model is trained using a machine learning method and features from a dataset of true intended user text span selections. A ranked list of re-scored candidate text spans is received from the ensemble model.

Abstract: A text span forming either a single word or a series of two or more words that a user intended to select is predicted. A document and a location pointer that indicates a particular location in the document are received and input to different candidate text span generation methods. A ranked list of one or more scored candidate text spans is received from each of the different candidate text span generation methods. A machine-learned ensemble model is used to re-score each of the scored candidate text spans that is received from each of the different candidate text span generation methods. The ensemble model is trained using a machine learning method and features from a dataset of true intended user text span selections. A ranked list of re-scored candidate text spans is received from the ensemble model.

Abstract: A user interface for displaying source data and a translated copy of a segment of the source data is provided. The user interface includes a first display portion for displaying at least a subset of the source data on the display. The user interface includes a second display portion for displaying a translated copy of a selected segment of source data on the display.

Abstract: A localization development system includes a platform that receives information to be localized to a target market that is distinct from a source market for which the information was originally authored. The platform calculates a recycle rate for the received information. The recycle rate is indicative of a portion of the information that has already been localized to the target market before the information to be localized was received from an authoring component.

Abstract: A user interface for displaying source data and a translated copy of a segment of the source data is provided. The user interface includes a first display portion for displaying at least a subset of the source data on the display. The user interface includes a second display portion for displaying a translated copy of a selected segment of source data on the display.

Abstract: A user interface for displaying source data and a translated copy of a segment of the source data is provided. The user interface includes a first display portion for displaying at least a subset of the source data on the display. The user interface includes a second display portion for displaying a translated copy of a selected segment of source data on the display.

Abstract: A localization platform is used to localize data to one or more distinct markets. A matching component receives data to be localized and accesses different localization components and returns localized data corresponding to the input data. A data gathering component receives localized content from an input source that is external to the localization platform along with metadata that identifies the particular input source that provided the localized content. The data gathering component then only allows the particular input source that provided the localized content to modify that localized content.