Abstract: Methods, systems and computer program products are provided for spoiler prevention. Media consumption applications may be placed in “spoiler-free” mode, for example, to prevent media content from spoiling first-hand user experience. A user may provide and/or authorize access to and use of spoiler prevention information. A user may request media content (e.g., while surfing the Internet). Digital media content to be presented to a user may be searched in real-time and/or pre-searched for spoiler content and/or associated spoiler indications relative to spoiler prevention information. Identified spoiler content may be concealed from users. A procedure may be provided for users to determine one or more reasons why content is concealed, to selectively reveal concealed content, and to provide feedback whether concealed content was or was not spoiler content for a user. Feedback may be used to improve spoiler prevention, for example, by retraining a machine learning model, which may be user-specific.

Abstract: Methods, systems and computer program products are provided for spoiler prevention. Media consumption applications may be placed in “spoiler-free” mode, for example, to prevent media content from spoiling first-hand user experience. A user may provide and/or authorize access to and use of spoiler prevention information. A user may request media content (e.g., while surfing the Internet). Digital media content to be presented to a user may be searched in real-time and/or pre-searched for spoiler content and/or associated spoiler indications relative to spoiler prevention information. Identified spoiler content may be concealed from users. A procedure may be provided for users to determine one or more reasons why content is concealed, to selectively reveal concealed content, and to provide feedback whether concealed content was or was not spoiler content for a user. Feedback may be used to improve spoiler prevention, for example, by retraining a machine learning model, which may be user-specific.

Abstract: Methods, systems and computer program products are provided for spoiler prevention. Media consumption applications may be placed in “spoiler-free” mode, for example, to prevent media content from spoiling first-hand user experience. A user may provide and/or authorize access to and use of spoiler prevention information. A user may request media content (e.g., while surfing the Internet). Digital media content to be presented to a user may be searched in real-time and/or pre-searched for spoiler content and/or associated spoiler indications relative to spoiler prevention information. Identified spoiler content may be concealed from users. A procedure may be provided for users to determine one or more reasons why content is concealed, to selectively reveal concealed content, and to provide feedback whether concealed content was or was not spoiler content for a user. Feedback may be used to improve spoiler prevention, for example, by retraining a machine learning model, which may be user-specific.

Abstract: Methods, systems and computer program products are provided for spoiler prevention. Media consumption applications may be placed in “spoiler-free” mode, for example, to prevent media content from spoiling first-hand user experience. A user may provide and/or authorize access to and use of spoiler prevention information. A user may request media content (e.g., while surfing the Internet). Digital media content to be presented to a user may be searched in real-time and/or pre-searched for spoiler content and/or associated spoiler indications relative to spoiler prevention information. Identified spoiler content may be concealed from users. A procedure may be provided for users to determine one or more reasons why content is concealed, to selectively reveal concealed content, and to provide feedback whether concealed content was or was not spoiler content for a user. Feedback may be used to improve spoiler prevention, for example, by retraining a machine learning model, which may be user-specific.

Abstract: Entities such as hotels, restaurants, resorts, houses, vehicles, and other places and things, are associated with images of each entity. The images are tagged using machine learning to identify what aspects of the associated entity are captured by each image. When a user is requested to select an entity from a set of entities, a user preference model is used to determine what tags the user is interested in. The tags are used to select images associated with the entities from the set of entities, and the selected images are displayed to the user. The user can then provide indicators that show which of the displayed images the user likes or dislikes. Based on the indicators, one or more entities from the set of entities is presented to the user. The model may also be updated based on the indicators.

Abstract: Entities such as hotels, restaurants, resorts, houses, vehicles, and other places and things, are associated with images of each entity. The images are tagged using machine learning to identify what aspects of the associated entity are captured by each image. When a user is requested to select an entity from a set of entities, a user preference model is used to determine what tags the user is interested in. The tags are used to select images associated with the entities from the set of entities, and the selected images are displayed to the user. The user can then provide indicators that show which of the displayed images the user likes or dislikes. Based on the indicators, one or more entities from the set of entities is presented to the user. The model may also be updated based on the indicators.

Abstract: A computing system includes at least one processor and memory storing instructions that, when executed, cause the computing system to receive an indication of a set of grammar commands for an application associated with the computing system, each grammar command corresponding to an executable command that is executable by the application to perform a corresponding application action, receive an indication of a first speech input, from a user, captured by a speech capture component, detect an ambiguity based on a correlation between the first speech input and the set of grammar commands, provide, to the user, an output that represents the detected ambiguity, receive, in response to the output, an indication of a second speech input that is captured by the speech capture component and resolves the ambiguity, and, based on the second speech input execute the executable command that corresponds to one of the grammar commands.

Abstract: Environmental signals are used to determine when to prompt a user to enable location sharing on their computer devices. These environmental signals may include the current location of the user being an unusual location for the user or a location that is tagged as a known social location such as a concert venue, stadium, or park. The environmental signals may also include one or more friends of the user being near the user. If the user chooses to enable location sharing in response to the prompt, the location of the user may be shared with some or all of their friends, or just the friends that have been determined to be near the user. After some amount of time has passed, or the environmental signals have changed, the location sharing may be automatically disabled for the user.

Abstract: A method includes parsing a text source document to construct a document node tree such that the document node tree includes text offsets indicating the location of the text within the text source document corresponding to each node of the document node tree. The method includes constructing, from the document node tree, a Document Object Model (DOM) and a view node tree that represents the DOM. The constructing of the view node tree includes mapping the view node tree to the document node tree. The method includes providing a running representation of the DOM such that DOM elements of the DOM are linked to the text within the text source document corresponding to the DOM elements via the view node tree and the document node tree.

Abstract: Entities such as hotels, restaurants, resorts, houses, vehicles, and other places and things, are associated with images of each entity. The images are tagged using machine learning to identify what aspects of the associated entity are captured by each image. When a user is requested to select an entity from a set of entities, a user preference model is used to determine what tags the user is interested in. The tags are used to select images associated with the entities from the set of entities, and the selected images are displayed to the user. The user can then provide indicators that show which of the displayed images the user likes or dislikes. Based on the indicators, one or more entities from the set of entities is presented to the user. The model may also be updated based on the indicators.

Abstract: Environmental signals are used to determine when to prompt a user to enable location sharing on their computer devices. These environmental signals may include the current location of the user being an unusual location for the user or a location that is tagged as a known social location such as a concert venue, stadium, or park. The environmental signals may also include one or more friends of the user being near the user. If the user chooses to enable location sharing in response to the prompt, the location of the user may be shared with some or all of their friends, or just the friends that have been determined to be near the user. After some amount of time has passed, or the environmental signals have changed, the location sharing may be automatically disabled for the user.

Abstract: Location data and image data are captured at a location. The location data may include GPS coordinates and the image data may include images or videos taken at the location. The image data is processed to identify anchor points associated with the location. The location data and the anchor points are provided to a cloud service that uses the location data and image data to create a map of locations and anchor points. A user can associate a virtual object with one or more anchor points associated with a location. At a later time, when the same or different user visits the location, the virtual object may be displayed to the user in an AR application at anchor points that match the one or more anchor points associated with the location.

Abstract: Environmental signals are used to determine when to prompt a user to enable location sharing on their computer devices. These environmental signals may include the current location of the user being an unusual location for the user or a location that is tagged as a known social location such as a concert venue, stadium, or park. The environmental signals may also include one or more friends of the user being near the user. If the user chooses to enable location sharing in response to the prompt, the location of the user may be shared with some or all of their friends, or just the friends that have been determined to be near the user. After some amount of time has passed, or the environmental signals have changed, the location sharing may be automatically disabled for the user.

Abstract: Environmental signals are used to determine when to prompt a user to enable location sharing on their computer devices. These environmental signals may include the current location of the user being an unusual location for the user or a location that is tagged as a known social location such as a concert venue, stadium, or park. The environmental signals may also include one or more friends of the user being near the user. If the user chooses to enable location sharing in response to the prompt, the location of the user may be shared with some or all of their friends, or just the friends that have been determined to be near the user. After some amount of time has passed, or the environmental signals have changed, the location sharing may be automatically disabled for the user.

Abstract: A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.

Abstract: Methods and systems are described for selecting, via a graphical user interface, map data to generate a three-dimensional (3D) model based on the selected map data. In one aspect, a method may include displaying, via the graphical use interface, a visual representation of map data. A selection of the map data may be received by the graphical user interface via a selector. The received selection of the map data may be modified to correspond to at least one of a boundary or an object in the map data. A 3D model of the modified selection of the map data may be generated and displayed by the graphical user interface. In some aspects, modifying the received selection of map data may be performed concurrently with receiving the selection of the map data, such as including moving the selector to at least one of the boundary or the object.

Abstract: Methods and systems are described for generating a three dimensional (3D) model from map data, for example, for 3D printing, 3D virtualization, etc. In one aspect, a method for generating a 3D model may include obtaining map data corresponding to an area or volume, for example, based on a selection of an area of a map. The map data may be translated into a local space. A surface mesh may be formed from the translated map data, and, in some cases, holes in the map data may be connected. At least one side surface may be generated at an angle relative to the surface mesh. In some cases, the side surface may include a side skirt that extends from the surface mesh to a local medium point. The at least one side surface may be combined with the surface mesh to generate the 3D model of the map data.

Abstract: A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.

Abstract: A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.

Abstract: A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.