Abstract: In some implementations, a computing device may create a semantic mapping that includes identified features that appear in a particular percentage of assets in a subset of assets of a media library. Also, the computing device may analyze assets of the media library using the semantic mapping to generate semantic scores, which may be used to determine a first tier of assets from the media library that rate highest for semantic score out of all assets. The computing device may present at least one of the first tier assets prominently in a user interface when viewing assets of the media library.

Abstract: In some implementations, a computing device may create a semantic mapping that includes identified features that appear in a particular percentage of assets in a subset of assets of a media library. Also, the computing device may analyze assets of the media library using the semantic mapping to generate semantic scores, which may be used to determine a first tier of assets from the media library that rate highest for semantic score out of all assets. The computing device may present at least one of the first tier assets prominently in a user interface when viewing assets of the media library.

Abstract: In some implementations, a computing device may create a semantic mapping that includes identified features that appear in a particular percentage of assets in a subset of assets of a media library. Also, the computing device may analyze assets of the media library using the semantic mapping to generate semantic scores, which may be used to determine a first tier of assets from the media library that rate highest for semantic score out of all assets. The computing device may present at least one of the first tier assets prominently in a user interface when viewing assets of the media library.

Abstract: In some implementations, a computing device may create a semantic mapping that includes identified features that appear in a particular percentage of assets in a subset of assets of a media library. Also, the computing device may analyze assets of the media library using the semantic mapping to generate semantic scores, which may be used to determine a first tier of assets from the media library that rate highest for semantic score out of all assets. The computing device may present at least one of the first tier assets prominently in a user interface when viewing assets of the media library.

Abstract: Techniques for digital asset classification are described. One or more holiday score are assigned to a set of digital media assets. The one or more holiday scores are calculated based on weighted holiday metrics determined based on characteristics of the set of digital assets. A holiday classification is assigned based on the one or more holiday score, and the set of digital media assets are provided for presentation in accordance with the holiday classification.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for presenting a user interface that allows a user to quickly and easily filter and search a digital asset collection. The disclosed techniques allow for rapid recall of desired digital assets, linking assets into logical collections, and an overall improved user experience. The zero keyword/contextual keyword feature presents multimedia content icons and searchable keywords to allow a user to search the digital asset collection simply by tapping on one of these keywords. The top auto completion feature auto-completes suggestions in the search field based on various heuristics to ensure the method produces diverse and relevant results. The next keyword suggestion feature predicts a next search term based on learned properties about the digital asset collection.

Abstract: In some implementations, a computing device can generate personalized music selections to associate with any collections of visual media (e.g., photos and videos) stored on the computing device. A user may prefer particular genres of music and listen to some genres more frequently than others. The computing device can create measures of the user's genre preferences and use these measures to select music that is preferred by the user and music that may be significant or relevant to the particular collection of visual media. The computing device may also determine music that was being played when and where the visual media were being created. The computing device may store the visual media and music items in association with each other. The computing device may generate composite media items that combine the visual media and music items. When the visual media are viewed, the selected music item is also played.

Abstract: In some implementations, a computing device can generate personalized music selections to associate with any collections of visual media (e.g., photos and videos) stored on the computing device. A user may prefer particular genres of music and listen to some genres more frequently than others. The computing device can create measures of the user's genre preferences and use these measures to select music that is preferred by the user and music that may be significant or relevant to the particular collection of visual media. The computing device may also determine music that was being played when and where the visual media were being created. The computing device may store the visual media and music items in association with each other. The computing device may generate composite media items that combine the visual media and music items. When the visual media are viewed, the selected music item is also played.

Abstract: In some implementations, a computing device may create a semantic mapping that includes identified features that appear in a particular percentage of assets in a subset of assets of a media library. Also, the computing device may analyze assets of the media library using the semantic mapping to generate semantic scores, which may be used to determine a first tier of assets from the media library that rate highest for semantic score out of all assets. The computing device may present at least one of the first tier assets prominently in a user interface when viewing assets of the media library.

Abstract: Systems and processes for operating a digital assistant are provided. In accordance with one or more examples, a method includes, at a user device with one or more processors and memory, receiving unstructured natural language information from at least one user. The method also includes, in response to receiving the unstructured natural language information, determining whether event information is present in the unstructured natural language information. The method further includes, in accordance with a determination that event information is present within the unstructured natural language information, determining whether an agreement on an event is present in the unstructured natural language information. The method further includes, in accordance with a determination that an agreement on an event is present, determining an event type of the event and providing an event description based on the event type.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for enabling a user to quickly and easily filter and search a digital asset collection. The disclosed techniques allow for rapid recall of desired digital assets, linking assets into logical collections, and an overall improved user experience. The zero keyword/contextual keyword feature presents multimedia content icons and searchable keywords to allow a user to search the digital asset collection simply by tapping on one of these keywords. The top auto completion feature auto-completes suggestions in the search field based on various heuristics to ensure the method produces diverse and relevant results. The next keyword suggestion feature predicts a next search term based on learned properties about the digital asset collection. The semantical synonym and syntax synonym features expand an indexed vocabulary to allow broader searching.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for presenting a user interface that allows a user to quickly and easily filter and search a digital asset collection. The disclosed techniques allow for rapid recall of desired digital assets, linking assets into logical collections, and an overall improved user experience. The zero keyword/contextual keyword feature presents multimedia content icons and searchable keywords to allow a user to search the digital asset collection simply by tapping on one of these keywords. The top auto completion feature auto-completes suggestions in the search field based on various heuristics to ensure the method produces diverse and relevant results. The next keyword suggestion feature predicts a next search term based on learned properties about the digital asset collection.

Abstract: Techniques of digital asset management (DAM) are described. A DAM system can obtain a knowledge graph metadata network describing relationships between metadata associated with a user's collection of digital assets (DAs), e.g., images, videos, music, etc. Based on information obtained, e.g., from the user's DA collection and/or the knowledge graph metadata network, the DAM system may provide users with more intelligent (and automated) DA sharing suggestions that are as relevant as possible for a given context. In some embodiments, the sharing suggestions may be based on one or more DAs recently shared with the user from a third party. In other embodiments, a proactive sharing suggestion may be presented to a user based on a detected indication of an intent to share DAs, e.g., based on the extraction of relevant features from an incoming message from a third party (or an outgoing message from the user to a third party).

Abstract: Systems and processes for operating a digital assistant are provided. In accordance with one or more examples, a method includes, at a user device with one or more processors and memory, receiving unstructured natural language information from at least one user. The method also includes, in response to receiving the unstructured natural language information, determining whether event information is present in the unstructured natural language information. The method further includes, in accordance with a determination that event information is present within the unstructured natural language information, determining whether an agreement on an event is present in the unstructured natural language information. The method further includes, in accordance with a determination that an agreement on an event is present, determining an event type of the event and providing an event description based on the event type.

Abstract: Systems and processes for detecting an event within natural language are provided. In one example of a process, unstructured natural language information may be received from at least one user. The presence of event information in the unstructured natural language information may be determined. In accordance with a determination that event information is present within the unstructured natural language information, a pseudo-event entry associated with that event information may be generated.