Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for disambiguating clustered occurrence identifiers. Digital assets contain a plurality of metadata that can be used to identify the occurrence or event during which the digital asset was captured. Techniques can use various types contextual information based on a category of the digital asset for disambiguation. Events can occur at various locations, over a dispersed area and it can be challenging to automatically identify and label the digital assets with the identification of the event. Automatically labelling the digital assets assists user in organizing and sharing the digital assets with friends and family. Users can search for events by event name, performer name, and category.

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: A memory management system which can effectively determine whether, when, and to what extent, to modify a graph database based on changes in a paired relational database storing various data like media assets, such as photos and videos. The media assets are associated with data objects, such as moments, events, and people. The memory management system also includes one or more knowledge graph metadata networks (or more simply “knowledge graphs”). The knowledge graph(s) store information which is related to the data stored by the relational databases. The memory management system detects changes in the data and data objects stored by the relational database(s). The memory management system can translate detected changes into modification instructions for the knowledge graph. The memory management system can also determine that it is unnecessary to modify the knowledge graph due to a detected change.

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 generating a knowledge graph metadata network (metadata network) for digital asset management (DAM) are described. A DAM logic/module can obtain one or more first metadata assets describing characteristics associated with digital assets (DAs) in the DA collection. The DAM logic/module can also determine second metadata asset(s) and third metadata asset(s) describing characteristics associated with DAs in the DA collection based on the first metadata asset(s). The DAM logic/module can generate at least some of the metadata assets as nodes in a metadata network associated with the DA collection. The DAM logic/module can also determine, for at least two of the metadata assets, a correlation between the at least two metadata assets. The DAM logic/module can generate an edge in the metadata network between the nodes that represent the at least two metadata assets to represent the determined correlation.

Abstract: Systems, methods, and computer-readable media for defining a collection of media content items of a media library for a relevant interest are provided.

Abstract: Systems, methods, and computer-readable media for reducing a number of unverified persons detected in media content are provided.

Abstract: Techniques of generating a knowledge graph metadata network (metadata network) for digital asset management (DAM) are described. A DAM logic/module can obtain one or more first metadata assets describing characteristics associated with digital assets (DAs) in the DA collection. The DAM logic/module can also determine second metadata asset(s) and third metadata asset (s) describing characteristics associated with DAs in the DA collection based on the first metadata asset(s). The DAM logic/module can generate at least some of the metadata assets as nodes in a metadata network associated with the DA collection. The DAM logic/module can also determine, for at least two of the metadata assets, a correlation between the at least two metadata assets. The DAM logic/module can generate an edge in the metadata network between the nodes that represent the at least two metadata assets to represent the determined correlation.

Abstract: Techniques of relating at least two digital assets based on digital asset management (DAM) are described. A DAM logic/module can obtain a knowledge graph metadata network (metadata network) of metadata associated with a collection of digital assets (DA collection). The metadata network comprises correlated metadata assets. Each metadata asset is represented a node in the metadata network. A correlation between metadata assets is represented as an edge in the metadata network. The DAM logic/module can select a first metadata asset using the metadata network. The DAM logic/module can also determine that the first metadata asset is associated with a second metadata asset. The DAM logic/module can identify a third metadata asset based on at least one of the first metadata asset or the second metadata asset. The DAM logic/module can cause one or more DAs associated with the third metadata asset to be presented via an output device.

Abstract: Techniques of digital asset management (DAM) are described. A DAM logic/module can obtain a knowledge graph metadata network (metadata network) of metadata associated with a collection of digital assets (DA collection). The metadata network can comprise correlated metadata assets describing characteristics associated with digital assets (DAs) in the DA collection. Each metadata asset can be represented as a node in the metadata network. Each correlation between two metadata assets can be represented in the metadata network as an edge between the nodes representing the two metadata assets. The DAM logic/module can identify a first metadata asset using the metadata network. The DAM logic/module can also identify a second metadata asset based at least on the first metadata asset. Furthermore, the DAM logic/module can cause one or more DAs associated with at least one of the first or second metadata assets to be presented via an output device.